IBM Cloud API Docs

Introduction

The IBM Watson™ Speech to Text service provides APIs that use IBM's speech-recognition capabilities to produce transcripts of spoken audio. The service can transcribe speech from various languages and audio formats. In addition to basic transcription, the service can produce detailed information about many different aspects of the audio. It returns all JSON response content in the UTF-8 character set.

The service supports two types of models: previous-generation models that include the terms Broadband and Narrowband in their names, and next-generation models that include the terms Multimedia and Telephony in their names. Broadband and multimedia models have minimum sampling rates of 16 kHz. Narrowband and telephony models have minimum sampling rates of 8 kHz. The next-generation models offer high throughput and greater transcription accuracy.

Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

For speech recognition, the service supports synchronous and asynchronous HTTP Representational State Transfer (REST) interfaces. It also supports a WebSocket interface that provides a full-duplex, low-latency communication channel: Clients send requests and audio to the service and receive results over a single connection asynchronously.

The service also offers two customization interfaces. Use language model customization to expand the vocabulary of a base model with domain-specific terminology. Use acoustic model customization to adapt a base model for the acoustic characteristics of your audio. For language model customization, the service also supports grammars. A grammar is a formal language specification that lets you restrict the phrases that the service can recognize.

Language model customization and grammars are available for most previous- and next-generation models. Acoustic model customization is available for all previous-generation models.

This documentation describes Java SDK major version 9. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Node SDK major version 6. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Python SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Ruby SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes .NET Standard SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Go SDK major version 2. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Swift SDK major version 4. For more information about how to update your code from the previous version, see the migration guide.

This documentation describes Unity SDK major version 5. For more information about how to update your code from the previous version, see the migration guide.

The IBM Watson Unity SDK has the following requirements.

  • The SDK requires Unity version 2018.2 or later to support Transport Layer Security (TLS) 1.2.
    • Set the project settings for both the Scripting Runtime Version and the Api Compatibility Level to .NET 4.x Equivalent.
    • For more information, see TLS 1.0 support.
  • The SDK doesn't support the WebGL projects. Change your build settings to any platform except WebGL.

For more information about how to install and configure the SDK and SDK Core, see https://github.com/watson-developer-cloud/unity-sdk.

The code examples on this tab use the client library that is provided for Java.

Maven

<dependency>
  <groupId>com.ibm.watson</groupId>
  <artifactId>ibm-watson</artifactId>
  <version>11.0.0</version>
</dependency>

Gradle

compile 'com.ibm.watson:ibm-watson:11.0.0'

GitHub

The code examples on this tab use the client library that is provided for Node.js.

Installation

npm install ibm-watson@^8.0.0

GitHub

The code examples on this tab use the client library that is provided for Python.

Installation

pip install --upgrade "ibm-watson>=7.0.0"

GitHub

The code examples on this tab use the client library that is provided for Ruby.

Installation

gem install ibm_watson

GitHub

The code examples on this tab use the client library that is provided for Go.

go get -u github.com/watson-developer-cloud/go-sdk/v2@v3.0.0

GitHub

The code examples on this tab use the client library that is provided for Swift.

Cocoapods

pod 'IBMWatsonSpeechToTextV1', '~> 5.0.0'

Carthage

github "watson-developer-cloud/swift-sdk" ~> 5.0.0

Swift Package Manager

.package(url: "https://github.com/watson-developer-cloud/swift-sdk", from: "5.0.0")

GitHub

The code examples on this tab use the client library that is provided for .NET Standard.

Package Manager

Install-Package IBM.Watson.SpeechToText.v1 -Version 7.0.0

.NET CLI

dotnet add package IBM.Watson.SpeechToText.v1 --version 7.0.0

PackageReference

<PackageReference Include="IBM.Watson.SpeechToText.v1" Version="7.0.0" />

GitHub

The code examples on this tab use the client library that is provided for Unity.

GitHub

Endpoint URLs

Identify the base URL for your service instance.

IBM Cloud URLs

The base URLs come from the service instance. To find the URL, view the service credentials by clicking the name of the service in the Resource list. Use the value of the URL. Add the method to form the complete API endpoint for your request.

The following example URL represents a Speech to Text instance that is hosted in Washington, DC:

https://api.us-east.speech-to-text.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2

The following URLs represent the base URLs for Speech to Text. When you call the API, use the URL that corresponds to the location of your service instance.

  • Dallas: https://api.us-south.speech-to-text.watson.cloud.ibm.com
  • Washington, DC: https://api.us-east.speech-to-text.watson.cloud.ibm.com
  • Frankfurt: https://api.eu-de.speech-to-text.watson.cloud.ibm.com
  • Sydney: https://api.au-syd.speech-to-text.watson.cloud.ibm.com
  • Tokyo: https://api.jp-tok.speech-to-text.watson.cloud.ibm.com
  • London: https://api.eu-gb.speech-to-text.watson.cloud.ibm.com
  • Seoul: https://api.kr-seo.speech-to-text.watson.cloud.ibm.com

Set the correct service URL by calling the setServiceUrl() method of the service instance.

Set the correct service URL by specifying the serviceUrl parameter when you create the service instance.

Set the correct service URL by calling the set_service_url() method of the service instance.

Set the correct service URL by specifying the service_url property of the service instance.

Set the correct service URL by calling the SetServiceURL() method of the service instance.

Set the correct service URL by setting the serviceURL property of the service instance.

Set the correct service URL by calling the SetServiceUrl() method of the service instance.

Set the correct service URL by calling the SetServiceUrl() method of the service instance.

Dallas API endpoint example for services managed on IBM Cloud

curl -X {request_method} -u "apikey:{apikey}" "https://api.us-south.speech-to-text.watson.cloud.ibm.com/instances/{instance_id}"

Your service instance might not use this URL

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("https://api.us-east.speech-to-text.watson.cloud.ibm.com");

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { IamAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: 'https://api.us-east.speech-to-text.watson.cloud.ibm.com',
});

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "https://api.us-east.speech-to-text.watson.cloud.ibm.com"

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

if speechToTextErr != nil {
  panic(speechToTextErr)
}

speechToText.SetServiceURL("https://api.us-east.speech-to-text.watson.cloud.ibm.com")

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "https://api.us-east.speech-to-text.watson.cloud.ibm.com"

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://api.us-east.speech-to-text.watson.cloud.ibm.com");

Default URL

https://api.us-south.speech-to-text.watson.cloud.ibm.com

Example for the Washington, DC location

var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://api.us-east.speech-to-text.watson.cloud.ibm.com");

Cloud Pak for Data URLs

For services installed on Cloud Pak for Data, the base URLs come from both the cluster and service instance.

You can find the base URL from the Cloud Pak for Data web client in the details page about the instance. Click the name of the service in your list of instances to see the URL.

Use that URL in your requests to Speech to Text. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the URL by calling the setServiceUrl() method of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by specifying the serviceUrl parameter when you create the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by specifying the url parameter when you create the service instance or by calling the set_url() method of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by specifying the url parameter when you create the service instance or by calling the url= method of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by specifying the URL parameter when you create the service instance or by calling the SetURL= method of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by setting the serviceURL property of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by calling the SetEndpoint() method of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Set the correct service URL by setting the Url property of the service instance. For Cloud Pak for Data System, use a hostname that resolves to an IP address in the cluster.

Endpoint example for Cloud Pak for Data

curl -X {request_method} -H "Authorization: Bearer {token}" "https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api"

Endpoint example for Cloud Pak for Data

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}", "{username}", "{password}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api");

Endpoint example for Cloud Pak for Data

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new CloudPakForDataAuthenticator({
    username: '{username}',
    password: '{password}',
    url: 'https://{cpd_cluster_host}{:port}',
  }),
  serviceUrl: 'https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api',
});

Endpoint example for Cloud Pak for Data

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator

authenticator = CloudPakForDataAuthenticator(
    '{username}',
    '{password}',
    'https://{cpd_cluster_host}{:port}'
)

speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api')

Endpoint example for Cloud Pak for Data

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::CLoudPakForDataAuthenticator.new(
  username: "{username}",
  password: "{password}",
  url: "https://{cpd_cluster_host}{:port}"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api"

Endpoint example for Cloud Pak for Data

speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

if speechToTextErr != nil {
  panic(speechToTextErr)
}

speechToText.SetServiceURL("https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api")

Endpoint example for Cloud Pak for Data

let authenticator = CloudPakForDataAuthenticator(username: "{username}", password: "{password}", url: "https://{cpd_cluster_host}{:port}")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api"

Endpoint example for Cloud Pak for Data

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api");

Endpoint example for Cloud Pak for Data

var authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://{cpd_cluster_host}{:port}/speech-to-text/{deployment_id}/instances/{instance_id}/api");

Disabling SSL verification

All Watson services use Secure Sockets Layer (SSL) (or Transport Layer Security (TLS)) for secure connections between the client and server. The connection is verified against the local certificate store to ensure authentication, integrity, and confidentiality.

If you use a self-signed certificate, you need to disable SSL verification to make a successful connection.

Enabling SSL verification is highly recommended. Disabling SSL jeopardizes the security of the connection and data. Disable SSL only if necessary, and take steps to enable SSL as soon as possible.

To disable SSL verification for a curl request, use the --insecure (-k) option with the request.

To disable SSL verification, create an HttpConfigOptions object and set the disableSslVerification property to true. Then, pass the object to the service instance by using the configureClient method.

To disable SSL verification, set the disableSslVerification parameter to true when you create the service instance.

To disable SSL verification, specify True on the set_disable_ssl_verification method for the service instance.

To disable SSL verification, set the disable_ssl_verification parameter to true in the configure_http_client() method for the service instance.

To disable SSL verification, call the DisableSSLVerification method on the service instance.

To disable SSL verification, call the disableSSLVerification() method on the service instance. You cannot disable SSL verification on Linux.

To disable SSL verification, set the DisableSslVerification method to true on the service instance.

To disable SSL verification, set the DisableSslVerification method to true on the service instance.

Example to disable SSL verification with a service managed on IBM Cloud. Replace {apikey} and {url} with your service credentials.

curl -k -X {request_method} -u "apikey:{apikey}" "{url}/{method}"

Example to disable SSL verification with a service managed on IBM Cloud

IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("{url}");

HttpConfigOptions configOptions = new HttpConfigOptions.Builder()
  .disableSslVerification(true)
  .build();
speechToText.configureClient(configOptions);

Example to disable SSL verification with a service managed on IBM Cloud

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { IamAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
  disableSslVerification: true,
});

Example to disable SSL verification with a service managed on IBM Cloud

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('{url}')

speech_to_text.set_disable_ssl_verification(True)

Example to disable SSL verification with a service managed on IBM Cloud

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "{url}"

speech_to_text.configure_http_client(disable_ssl_verification: true)

Example to disable SSL verification with a service managed on IBM Cloud

speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

if speechToTextErr != nil {
  panic(speechToTextErr)
}

speechToText.SetServiceURL("{url}")

speechToText.DisableSSLVerification()

Example to disable SSL verification with a service managed on IBM Cloud

let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "{url}"

speechToText.disableSSLVerification()

Example to disable SSL verification with a service managed on IBM Cloud

IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.DisableSslVerification(true);

Example to disable SSL verification with a service managed on IBM Cloud

var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.DisableSslVerification = true;

Example to disable SSL verification with an installed service

curl -k -X {request_method} -H "Authorization: Bearer {token}" "{url}/v1/{method}"

Example to disable SSL verification with an installed service

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}", "{username}", "{password}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("{url}";

HttpConfigOptions configOptions = new HttpConfigOptions.Builder()
  .disableSslVerification(true)
  .build();
speechToText.configureClient(configOptions);

Example to disable SSL verification with an installed service

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new CloudPakForDataAuthenticator({
    username: '{username}',
    password: '{password}',
    url: 'https://{cpd_cluster_host}{:port}',
  }),
  serviceUrl: '{url}',
  disableSslVerification: true,
});

Example to disable SSL verification with an installed service

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator

authenticator = CloudPakForDataAuthenticator(
    '{username}',
    '{password}'
)

speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('{url}')

speech_to_text.set_disable_ssl_verification(True)

Example to disable SSL verification with an installed service

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::CLoudPakForDataAuthenticator.new(
  username: "{username}",
  password: "{password}",
  url: "https://{cpd_cluster_host}{:port}"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "{url}"

speech_to_text.configure_http_client(disable_ssl_verification: true)

Example to disable SSL verification with an installed service

speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

if speechToTextErr != nil {
  panic(speechToTextErr)
}

speechToText.SetServiceURL("{url}")

speechToText.DisableSSLVerification()

Example to disable SSL verification with an installed service

let authenticator = WatsonCloudPakForDataAuthenticator(username: "{username}", password: "{password}", url: "https://{cpd_cluster_host}{:port}")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "{url}"

speechToText.disableSSLVerification()

Example to disable SSL verification with an installed service

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.DisableSslVerification(true);

Example to disable SSL verification with an installed service

var authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.DisableSslVerification = true;

Authentication

IBM Cloud services use IBM Cloud Identity and Access Management (IAM) to authenticate. With IBM Cloud Pak for Data, you pass a bearer token.

IBM Cloud

For IBM Cloud instances, you authenticate to the API by using IBM Cloud Identity and Access Management (IAM).

You can pass either a bearer token in an authorization header or an API key. Tokens support authenticated requests without embedding service credentials in every call. API keys use basic authentication. For more information, see Authenticating to Watson services.

  • For testing and development, you can pass an API key directly.
  • For production use, unless you use the Watson SDKs, use an IAM token.

If you pass in an API key, use apikey for the username and the value of the API key as the password. For example, if the API key is f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI in the service credentials, include the credentials in your call like this:

curl -u "apikey:f5sAznhrKQyvBFFaZbtF60m5tzLbqWhyALQawBg5TjRI"

For IBM Cloud instances, the SDK provides initialization methods for each form of authentication.

  • Use the API key to have the SDK manage the lifecycle of the access token. The SDK requests an access token, ensures that the access token is valid, and refreshes it if necessary.
  • Use the access token to manage the lifecycle yourself. You must periodically refresh the token.

For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.For more information, see IAM authentication with the SDK.

IBM Cloud. Replace {apikey} and {url} with your service credentials.

curl -X {request_method} -u "apikey:{apikey}" "{url}/v1/{method}"

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("{url}");

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { IamAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

authenticator = IAMAuthenticator('{apikey}')
speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('{url}')

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::IamAuthenticator.new(
  apikey: "{apikey}"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "{url}"

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

import (
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/speechtotextv1"
)

func main() {
  authenticator := &core.IamAuthenticator{
    ApiKey: "{apikey}",
  }

  options := &speechtotextv1.SpeechToTextV1Options{
    Authenticator: authenticator,
  }

  speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

  if speechToTextErr != nil {
    panic(speechToTextErr)
  }

  speechToText.SetServiceURL("{url}")
}

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

let authenticator = WatsonIAMAuthenticator(apiKey: "{apikey}")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "{url}"

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

IBM Cloud. SDK managing the IAM token. Replace {apikey} and {url}.

var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

Cloud Pak for Data

For Cloud Pak for Data, you pass a bearer token in an Authorization header to authenticate to the API. The token is associated with a username.

  • For testing and development, you can use the bearer token that's displayed in the Cloud Pak for Data web client. To find this token, view the details for the service instance by clicking the name of the service in your list of instances. The details also include the service endpoint URL. Don't use this token in production because it does not expire.
  • For production use, create a user in the Cloud Pak for Data web client to use for authentication. Generate a token from that user's credentials with the POST /v1/authorize method.

For more information, see the Get authorization token method of the Cloud Pak for Data API reference.

For Cloud Pak for Data instances, pass either username and password credentials or a bearer token that you generate to authenticate to the API. Username and password credentials use basic authentication. However, the SDK manages the lifecycle of the token. Tokens are temporary security credentials. If you pass a token, you maintain the token lifecycle.

For production use, create a user in the Cloud Pak for Data web client to use for authentication, and decide which authentication mechanism to use.

  • To have the SDK manage the lifecycle of the token, use the username and password for that new user in your calls.
  • To manage the lifecycle of the token yourself, generate a token from that user's credentials. Call the POST /v1/authorize method to generate the token, and then pass the token in an Authorization header in your calls. You can see an example of the method on the Curl tab.

For more information, see the Get authorization token method of the Cloud Pak for Data API reference.

Don't use the bearer token that's displayed in the web client for the instance except during testing and development because that token does not expire.

To find your value for {url}, view the details for the service instance by clicking the name of the service in your list of instances in the Cloud Pak for Data web client.

Cloud Pak for Data. Generating a bearer token.

Replace {cpd_cluster_host} and {port} with the details for the service instance. Replace {username} and {password} with your Cloud Pak for Data credentials.

curl -k -X POST -H "cache-control: no-cache" -H "Content-Type: application/json" -d "{\"username\":\"{username}\",\"password\":\"{password}\"}" "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize"

The response includes a token property.

Authenticating to the API. Replace {token} with your details.

curl -H "Authorization: Bearer {token}" "{url}/v1/{method}"

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("{url}");

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new CloudPakForDataAuthenticator({
    username: '{username}',
    password: '{password}',
    url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
  }),
  serviceUrl: '{url}',
});

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator

authenticator = CloudPakForDataAuthenticator(
    '{username}',
    '{password}',
    'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
)

speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

speech_to_text.set_service_url('{url}')

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

require "ibm_watson/authenticators"
require "ibm_watson/speech_to_text_v1"
include IBMWatson

authenticator = Authenticators::CloudPakForDataAuthenticator.new(
  username: "{username}",
  password: "{password}",
  url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize"
)
speech_to_text = SpeechToTextV1.new(
  authenticator: authenticator
)
speech_to_text.service_url = "{url}"

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

import (
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/speechtotextv1"
)

func main() {
  authenticator := &core.CloudPakForDataAuthenticator{
    URL: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
    Username: "{username}",
    Password: "{password}",
  }

  options := &speechtotextv1.SpeechToTextV1Options{
    Authenticator: authenticator,
  }

  speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

  if speechToTextErr != nil {
    panic(speechToTextErr)
  }

  speechToText.SetServiceURL("{url}")
}

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {url}, see Endpoint URLs.

let authenticator = WatsonCloudPakForDataAuthenticator(username: "{username}", password: "{password}", url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize")
let speechToText = SpeechToText(authenticator: authenticator)
speechToText.serviceURL = "{url}"

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {cpd_cluster_host}, {port}, {release}, and {instance_id}, see Endpoint URLs.

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
    username: "{username}",
    password: "{password}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

Cloud Pak for Data. SDK managing the token.

Replace {username} and {password} with your Cloud Pak for Data credentials. For {cpd_cluster_host}, {port}, {release}, and {instance_id}, see Endpoint URLs.

var authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
    username: "{username}",
    password: "{password}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

Access between services

Your application might use more than one Watson service. You can grant access between services and you can grant access to more than one service for your applications.

For IBM Cloud services, the method to grant access between Watson services varies depending on the type of API key. For more information, see IAM access.

  • To grant access between IBM Cloud services, create an authorization between the services. For more information, see Granting access between services.
  • To grant access to your services by applications without using user credentials, create a service ID, add an API key, and assign access policies. For more information, see Creating and working with service IDs.

When you give a user ID access to multiple services, use an endpoint URL that includes the service instance ID (for example, https://api.us-south.speech-to-text.watson.cloud.ibm.com/instances/6bbda3b3-d572-45e1-8c54-22d6ed9e52c2). You can find the instance ID in two places:

  • By clicking the service instance row in the Resource list. The instance ID is the GUID in the details pane.

  • By clicking the name of the service instance in the list and looking at the credentials URL.

    If you don't see the instance ID in the URL, the credentials predate service IDs. Add new credentials from the Service credentials page and use those credentials.

Because the Cloud Pak for Data bearer token is associated with a username, you can use the token for all CPD Watson services that are associated with the username.

Error handling

Speech to Text uses standard HTTP response codes to indicate whether a method completed successfully. HTTP response codes in the 2xx range indicate success. A response in the 4xx range is some sort of failure, and a response in the 5xx range usually indicates an internal system error that cannot be resolved by the user. Response codes are listed with the method.

ErrorResponse

Name Description
error
string
Description of the problem.
code
integer
HTTP response code.
code_description
string
Response message.
warnings
string
Warnings associated with the error.

The Java SDK generates an exception for any unsuccessful method invocation. All methods that accept an argument can also throw an IllegalArgumentException.

Exception Description
IllegalArgumentException An invalid argument was passed to the method.

When the Java SDK receives an error response from the Speech to Text service, it generates an exception from the com.ibm.watson.developer_cloud.service.exception package. All service exceptions contain the following fields.

Field Description
statusCode The HTTP response code that is returned.
message A message that describes the error.

When the Node SDK receives an error response from the Speech to Text service, it creates an Error object with information that describes the error that occurred. This error object is passed as the first parameter to the callback function for the method. The contents of the error object are as shown in the following table.

Error

Field Description
code The HTTP response code that is returned.
message A message that describes the error.

The Python SDK generates an exception for any unsuccessful method invocation. When the Python SDK receives an error response from the Speech to Text service, it generates an ApiException with the following fields.

Field Description
code The HTTP response code that is returned.
message A message that describes the error.
info A dictionary of additional information about the error.

When the Ruby SDK receives an error response from the Speech to Text service, it generates an ApiException with the following fields.

Field Description
code The HTTP response code that is returned.
message A message that describes the error.
info A dictionary of additional information about the error.

The Go SDK generates an error for any unsuccessful service instantiation and method invocation. You can check for the error immediately. The contents of the error object are as shown in the following table.

Error

Field Description
code The HTTP response code that is returned.
message A message that describes the error.

The Swift SDK returns a WatsonError in the completionHandler any unsuccessful method invocation. This error type is an enum that conforms to LocalizedError and contains an errorDescription property that returns an error message. Some of the WatsonError cases contain associated values that reveal more information about the error.

Field Description
errorDescription A message that describes the error.

When the .NET Standard SDK receives an error response from the Speech to Text service, it generates a ServiceResponseException with the following fields.

Field Description
Message A message that describes the error.
CodeDescription The HTTP response code that is returned.

When the Unity SDK receives an error response from the Speech to Text service, it generates an IBMError with the following fields.

Field Description
Url The URL that generated the error.
StatusCode The HTTP response code returned.
ErrorMessage A message that describes the error.
Response The contents of the response from the server.
ResponseHeaders A dictionary of headers returned by the request.

Example error handling

try {
  // Invoke a method
} catch (NotFoundException e) {
  // Handle Not Found (404) exception
} catch (RequestTooLargeException e) {
  // Handle Request Too Large (413) exception
} catch (ServiceResponseException e) {
  // Base class for all exceptions caused by error responses from the service
  System.out.println("Service returned status code "
    + e.getStatusCode() + ": " + e.getMessage());
}

Example error handling

speechToText.method(params)
  .catch(err => {
    console.log('error:', err);
  });

Example error handling

from ibm_watson import ApiException
try:
    # Invoke a method
except ApiException as ex:
    print "Method failed with status code " + str(ex.code) + ": " + ex.message

Example error handling

require "ibm_watson"
begin
  # Invoke a method
rescue IBMWatson::ApiException => ex
  print "Method failed with status code #{ex.code}: #{ex.error}"
end

Example error handling

import "github.com/watson-developer-cloud/go-sdk/speechtotextv1"

// Instantiate a service
speechToText, speechToTextErr := speechtotextv1.NewSpeechToTextV1(options)

// Check for errors
if speechToTextErr != nil {
  panic(speechToTextErr)
}

// Call a method
result, _, responseErr := speechToText.MethodName(&methodOptions)

// Check for errors
if responseErr != nil {
  panic(responseErr)
}

Example error handling

speechToText.method() {
  response, error in

  if let error = error {
    switch error {
    case let .http(statusCode, message, metadata):
      switch statusCode {
      case .some(404):
        // Handle Not Found (404) exception
        print("Not found")
      case .some(413):
        // Handle Request Too Large (413) exception
        print("Payload too large")
      default:
        if let statusCode = statusCode {
          print("Error - code: \(statusCode), \(message ?? "")")
        }
      }
    default:
      print(error.localizedDescription)
    }
    return
  }

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }

  print(result)
}

Example error handling

try
{
    // Invoke a method
}
catch(ServiceResponseException e)
{
    Console.WriteLine("Error: " + e.Message);
}
catch (Exception e)
{
    Console.WriteLine("Error: " + e.Message);
}

Example error handling

// Invoke a method
speechToText.MethodName(Callback, Parameters);

// Check for errors
private void Callback(DetailedResponse<ExampleResponse> response, IBMError error)
{
    if (error == null)
    {
        Log.Debug("ExampleCallback", "Response received: {0}", response.Response);
    }
    else
    {
        Log.Debug("ExampleCallback", "Error received: {0}, {1}, {3}", error.StatusCode, error.ErrorMessage, error.Response);
    }
}

Data handling

Additional headers

Some Watson services accept special parameters in headers that are passed with the request.

You can pass request header parameters in all requests or in a single request to the service.

To pass a request header, use the --header (-H) option with a curl request.

To pass header parameters with every request, use the setDefaultHeaders method of the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, use the addHeader method as a modifier on the request before you execute it.

To pass header parameters with every request, specify the headers parameter when you create the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, use the headers method as a modifier on the request before you execute it.

To pass header parameters with every request, specify the set_default_headers method of the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, include headers as a dict in the request.

To pass header parameters with every request, specify the add_default_headers method of the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, specify the headers method as a chainable method in the request.

To pass header parameters with every request, specify the SetDefaultHeaders method of the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, specify the Headers as a map in the request.

To pass header parameters with every request, add them to the defaultHeaders property of the service object. See Data collection for an example use of this method.

To pass header parameters in a single request, pass the headers parameter to the request method.

To pass header parameters in a single request, use the WithHeader() method as a modifier on the request before you execute it. See Data collection for an example use of this method.

To pass header parameters in a single request, use the WithHeader() method as a modifier on the request before you execute it.

Example header parameter in a request

curl -X {request_method} -H "Request-Header: {header_value}" "{url}/v1/{method}"

Example header parameter in a request

ReturnType returnValue = speechToText.methodName(parameters)
  .addHeader("Custom-Header", "{header_value}")
  .execute();

Example header parameter in a request

const parameters = {
  {parameters}
};

speechToText.methodName(
  parameters,
  headers: {
    'Custom-Header': '{header_value}'
  })
   .then(result => {
    console.log(response);
  })
  .catch(err => {
    console.log('error:', err);
  });

Example header parameter in a request

response = speech_to_text.methodName(
    parameters,
    headers = {
        'Custom-Header': '{header_value}'
    })

Example header parameter in a request

response = speech_to_text.headers(
  "Custom-Header" => "{header_value}"
).methodName(parameters)

Example header parameter in a request

result, _, responseErr := speechToText.MethodName(
  &methodOptions{
    Headers: map[string]string{
      "Accept": "application/json",
    },
  },
)

Example header parameter in a request

let customHeader: [String: String] = ["Custom-Header": "{header_value}"]
speechToText.methodName(parameters, headers: customHeader) {
  response, error in
}

Example header parameter in a request for a service managed on IBM Cloud

IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.WithHeader("Custom-Header", "header_value");

Example header parameter in a request for an installed service

CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://{cpd_cluster_host}{:port}/speech-to-text/{release}/instances/{instance_id}/api");

speechToText.WithHeader("Custom-Header", "header_value");

Example header parameter in a request for a service managed on IBM Cloud

var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.WithHeader("Custom-Header", "header_value");

Example header parameter in a request for an installed service

var authenticator = new CloudPakForDataAuthenticator(
    url: "https://{cpd_cluster_host}{:port}",
    username: "{username}",
    password: "{password}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("https://{cpd_cluster_host}{:port}/speech-to-text/{release}/instances/{instance_id}/api");

speechToText.WithHeader("Custom-Header", "header_value");

Response details

The Speech to Text service might return information to the application in response headers.

To access all response headers that the service returns, include the --include (-i) option with a curl request. To see detailed response data for the request, including request headers, response headers, and extra debugging information, include the --verbose (-v) option with the request.

Example request to access response headers

curl -X {request_method} {authentication_method} --include "{url}/v1/{method}"

To access information in the response headers, use one of the request methods that returns details with the response: executeWithDetails(), enqueueWithDetails(), or rxWithDetails(). These methods return a Response<T> object, where T is the expected response model. Use the getResult() method to access the response object for the method, and use the getHeaders() method to access information in response headers.

Example request to access response headers

Response<ReturnType> response = speechToText.methodName(parameters)
  .executeWithDetails();
// Access response from methodName
ReturnType returnValue = response.getResult();
// Access information in response headers
Headers responseHeaders = response.getHeaders();

All response data is available in the Response<T> object that is returned by each method. To access information in the response object, use the following properties.

Property Description
result Returns the response for the service-specific method.
headers Returns the response header information.
status Returns the HTTP status code.

Example request to access response headers

speechToText.methodName(parameters)
  .then(response => {
    console.log(response.headers);
  })
  .catch(err => {
    console.log('error:', err);
  });

The return value from all service methods is a DetailedResponse object. To access information in the result object or response headers, use the following methods.

DetailedResponse

Method Description
get_result() Returns the response for the service-specific method.
get_headers() Returns the response header information.
get_status_code() Returns the HTTP status code.

Example request to access response headers

speech_to_text.set_detailed_response(True)
response = speech_to_text.methodName(parameters)
# Access response from methodName
print(json.dumps(response.get_result(), indent=2))
# Access information in response headers
print(response.get_headers())
# Access HTTP response status
print(response.get_status_code())

The return value from all service methods is a DetailedResponse object. To access information in the response object, use the following properties.

DetailedResponse

Property Description
result Returns the response for the service-specific method.
headers Returns the response header information.
status Returns the HTTP status code.

Example request to access response headers

response = speech_to_text.methodName(parameters)
# Access response from methodName
print response.result
# Access information in response headers
print response.headers
# Access HTTP response status
print response.status

The return value from all service methods is a DetailedResponse object. To access information in the response object or response headers, use the following methods.

DetailedResponse

Method Description
GetResult() Returns the response for the service-specific method.
GetHeaders() Returns the response header information.
GetStatusCode() Returns the HTTP status code.

Example request to access response headers

import (
  "github.com/IBM/go-sdk-core/core"
  "github.com/watson-developer-cloud/go-sdk/speechtotextv1"
)
result, response, responseErr := speechToText.MethodName(
  &methodOptions{})
// Access result
core.PrettyPrint(response.GetResult(), "Result ")

// Access response headers
core.PrettyPrint(response.GetHeaders(), "Headers ")

// Access status code
core.PrettyPrint(response.GetStatusCode(), "Status Code ")

All response data is available in the WatsonResponse<T> object that is returned in each method's completionHandler.

Example request to access response headers

speechToText.methodName(parameters) {
  response, error in

  guard let result = response?.result else {
    print(error?.localizedDescription ?? "unknown error")
    return
  }
  print(result) // The data returned by the service
  print(response?.statusCode)
  print(response?.headers)
}

The response contains fields for response headers, response JSON, and the status code.

DetailedResponse

Property Description
Result Returns the result for the service-specific method.
Response Returns the raw JSON response for the service-specific method.
Headers Returns the response header information.
StatusCode Returns the HTTP status code.

Example request to access response headers

var results = speechToText.MethodName(parameters);

var result = results.Result;            //  The result object
var responseHeaders = results.Headers;  //  The response headers
var responseJson = results.Response;    //  The raw response JSON
var statusCode = results.StatusCode;    //  The response status code

The response contains fields for response headers, response JSON, and the status code.

DetailedResponse

Property Description
Result Returns the result for the service-specific method.
Response Returns the raw JSON response for the service-specific method.
Headers Returns the response header information.
StatusCode Returns the HTTP status code.

Example request to access response headers

private void Example()
{
    speechToText.MethodName(Callback, Parameters);
}

private void Callback(DetailedResponse<ResponseType> response, IBMError error)
{
    var result = response.Result;                 //  The result object
    var responseHeaders = response.Headers;       //  The response headers
    var responseJson = reresponsesults.Response;  //  The raw response JSON
    var statusCode = response.StatusCode;         //  The response status code
}

Data labels (IBM Cloud)

You can remove data associated with a specific customer if you label the data with a customer ID when you send a request to the service.

  • Use the X-Watson-Metadata header to associate a customer ID with the data. By adding a customer ID to a request, you indicate that it contains data that belongs to that customer.

    Specify a random or generic string for the customer ID. Do not include personal data, such as an email address. Pass the string customer_id={id} as the argument of the header.

    Labeling data is used only by methods that accept customer data.

  • Use the Delete labeled data method to remove data that is associated with a customer ID.

Use this process of labeling and deleting data only when you want to remove the data that is associated with a single customer, not when you want to remove data for multiple customers. For more information about Speech to Text and labeling data, see Information security.

For more information about how to pass headers, see Additional headers.

Data collection (IBM Cloud)

By default, Speech to Text service instances managed on IBM Cloud that are not part of Premium plans collect data about API requests and their results. This data is collected only to improve the services for future users. The collected data is not shared or made public. Data is not collected for services that are part of Premium plans.

To prevent IBM usage of your data for an API request, set the X-Watson-Learning-Opt-Out header parameter to true. You can also disable request logging at the account level. For more information, see Controlling request logging for Watson services.

You must set the header on each request that you do not want IBM to access for general service improvements.

You can set the header by using the setDefaultHeaders method of the service object.

You can set the header by using the headers parameter when you create the service object.

You can set the header by using the set_default_headers method of the service object.

You can set the header by using the add_default_headers method of the service object.

You can set the header by using the SetDefaultHeaders method of the service object.

You can set the header by adding it to the defaultHeaders property of the service object.

You can set the header by using the WithHeader() method of the service object.

Example request with a service managed on IBM Cloud

curl -u "apikey:{apikey}" -H "X-Watson-Learning-Opt-Out: true" "{url}/{method}"

Example request with a service managed on IBM Cloud

Map<String, String> headers = new HashMap<String, String>();
headers.put("X-Watson-Learning-Opt-Out", "true");

speechToText.setDefaultHeaders(headers);

Example request with a service managed on IBM Cloud

const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
const { IamAuthenticator } = require('ibm-watson/auth');

const speechToText = new SpeechToTextV1({
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
  headers: {
    'X-Watson-Learning-Opt-Out': 'true'
  }
});

Example request with a service managed on IBM Cloud

speech_to_text.set_default_headers({'x-watson-learning-opt-out': "true"})

Example request with a service managed on IBM Cloud

speech_to_text.add_default_headers(headers: {"x-watson-learning-opt-out" => "true"})

Example request with a service managed on IBM Cloud

import "net/http"

headers := http.Header{}
headers.Add("x-watson-learning-opt-out", "true")
speechToText.SetDefaultHeaders(headers)

Example request with a service managed on IBM Cloud

speechToText.defaultHeaders["X-Watson-Learning-Opt-Out"] = "true"

Example request with a service managed on IBM Cloud

IamAuthenticator authenticator = new IamAuthenticator(
    apikey: "{apikey}"
    );

SpeechToTextService speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.WithHeader("X-Watson-Learning-Opt-Out", "true");

Example request with a service managed on IBM Cloud

var authenticator = new IamAuthenticator(
    apikey: "{apikey}"
);

while (!authenticator.CanAuthenticate())
    yield return null;

var speechToText = new SpeechToTextService(authenticator);
speechToText.SetServiceUrl("{url}");

speechToText.WithHeader("X-Watson-Learning-Opt-Out", "true");

Synchronous and asynchronous requests

The Java SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the ServiceCall interface.

  • To call a method synchronously, use the execute method of the ServiceCall interface. You can call the execute method directly from an instance of the service.
  • To call a method asynchronously, use the enqueue method of the ServiceCall interface to receive a callback when the response arrives. The ServiceCallback interface of the method's argument provides onResponse and onFailure methods that you override to handle the callback.

The Ruby SDK supports both synchronous (blocking) and asynchronous (non-blocking) execution of service methods. All service methods implement the Concurrent::Async module. When you use the synchronous or asynchronous methods, an IVar object is returned. You access the DetailedResponse object by calling ivar_object.value.

For more information about the Ivar object, see the IVar class docs.

  • To call a method synchronously, either call the method directly or use the .await chainable method of the Concurrent::Async module.

    Calling a method directly (without .await) returns a DetailedResponse object.

  • To call a method asynchronously, use the .async chainable method of the Concurrent::Async module.

You can call the .await and .async methods directly from an instance of the service.

Example synchronous request

ReturnType returnValue = speechToText.method(parameters).execute();

Example asynchronous request

speechToText.method(parameters).enqueue(new ServiceCallback<ReturnType>() {
  @Override public void onResponse(ReturnType response) {
    . . .
  }
  @Override public void onFailure(Exception e) {
    . . .
  }
});

Example synchronous request

response = speech_to_text.method_name(parameters)

or

response = speech_to_text.await.method_name(parameters)

Example asynchronous request

response = speech_to_text.async.method_name(parameters)

WebSockets

Recognize audio (WebSockets)

Sends audio and returns transcription results for recognition requests over a WebSocket connection. Requests and responses are enabled over a single TCP connection that abstracts much of the complexity of the request to offer efficient implementation, low latency, high throughput, and an asynchronous response.

The endpoint for the WebSocket API is

wss://api.{location}.speech-to-text.watson.cloud.ibm.com/instances/{instance_id}/v1/recognize
  • {location} indicates where your application is hosted:

    • us-south for Dallas
    • us-east for Washington, DC
    • eu-de for Frankfurt
    • au-syd for Sydney
    • jp-tok for Tokyo
    • eu-gb for London
    • kr-seo for Seoul
  • {instance_id} indicates the unique identifier of the service instance. For more information about how to find the instance ID, see Access between services.

The examples in the documentation abbreviate wss://api.{location}.speech-to-text.watson.cloud.ibm.com/instances/{instance_id} to {ws_url}. So all WebSocket examples call the method as {ws_url}/v1/recognize.

You can pass a maximum of 100 MB and a minimum of 100 bytes of audio per recognition request. You can send multiple requests over a single WebSocket connection. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding.

By default, the service returns only final results for any request. You can request interim results to see intermediate hypotheses as the transcription progress.

See also:

The WebSocket interface cannot be called from curl. Use a client-side scripting language to call the interface. The example request uses JavaScript to invoke the WebSocket recognize method.

The createRecognizeStream method is deprecated. Use the equivalent recognizeUsingWebSocket method instead.

The recognize_with_websocket method is deprecated. Use the equivalent recognize_using_websocket method instead.

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the content-type contentType content_type parameter with the request to specify the format of the audio.
  • For all other formats, you can omit the content-type contentType content_type parameter or specify application/octet-stream with the parameter to have the service automatically detect the format of the audio.

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • application/octet-stream
  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

See also:

The Python recognize_using_websocket method requires the content_type parameter.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

URI /v1/recognize
okhttp3.WebSocket recognizeUsingWebSocket(RecognizeOptions options,
  RecognizeCallback callback)
RecognizeStream recognizeUsingWebSocket(params)
dict recognize_using_websocket(audio, content_type,
  recognize_callback, model=None,
  language_customization_id=None, acoustic_customization_id=None,
  customization_weight=None, base_model_version=None,
  inactivity_timeout=None, interim_results=None,
  keywords=None, keywords_threshold=None,
  max_alternatives=None, word_alternatives_threshold=None,
  word_confidence=None, timestamps=None, profanity_filter=None,
  smart_formatting=None, speaker_labels=None, http_proxy_host=None,
  http_proxy_port=None, customization_id=None, grammar_name=None,
  redaction=None, processing_metrics=None, processing_metrics_interval=None,
  audio_metrics=None, end_of_phrase_silence_time=None,
  split_transcript_at_phrase_end=None, speech_detector_sensitivity=None,
  background_audio_suppression=None, **kwargs)

Request

The client calls the recognize method to obtain a string that contains the URI for the WebSocket interface. The call to the recognize method sets basic parameters for the connection and for all recognition requests that are sent over it. See the Parameters of recognize method table.

The client then establishes a connection with the service by passing the URI to the WebSocket constructor, which returns a WebSocket connection object. The client initiates and manages recognition requests by sending JSON-formatted text messages to the service over the connection. The text messages can include all other parameters of the recognition request. The required action parameter tells the service which action is to be performed. See the Parameters of WebSocket text messages table.

After sending the text message to initiate a request, the client sends the audio data to be transcribed as a binary message (blob) over the connection.

Parameters of recognize method

  • Pass a valid access token to establish an authenticated connection with the service. You must establish the connection before the access token expires. You pass an access token only to establish an authenticated connection. After you establish a connection, you can keep it alive indefinitely. You remain authenticated for as long as you keep the connection open. You do not need to refresh the access token for an active connection that lasts beyond the token's expiration time. After a connection is established, it can remain active even after the token or its credentials are deleted.

    • IBM Cloud only. Pass an Identity and Access Management (IAM) access token to authenticate with the service. You pass an IAM access token instead of passing an API key with the call. For more information, see Authenticating to IBM Cloud.

    • IBM Cloud Pak for Data only. Pass an access token as you would with the Authorization header of an HTTP request. For more information, see Authenticating to IBM Cloud Pak for Data.

  • The model to use for all speech recognition requests that are sent over the connection. See Using a model for speech recognition.

    The default model is en-US_BroadbandModel. For Speech to Text for IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service. For more information, see Using the default model.

    Allowable values: [ar-MS_BroadbandModel, ar-MS_Telephony, cs-CZ_Telephony, de-DE_BroadbandModel, de-DE_Multimedia, de-DE_NarrowbandModel, de-DE_Telephony, en-AU_BroadbandModel, en-AU_Multimedia, en-AU_NarrowbandModel, en-AU_Telephony, en-GB_BroadbandModel, en-GB_Multimedia, en-GB_NarrowbandModel, en-GB_Telephony, en-IN_Telephony, en-US_BroadbandModel, en-US_Multimedia, en-US_NarrowbandModel, en-US_ShortForm_NarrowbandModel, en-US_Telephony, en-WW_Medical_Telephony, es-AR_BroadbandModel, es-AR_NarrowbandModel, es-CL_BroadbandModel, es-CL_NarrowbandModel, es-CO_BroadbandModel, es-CO_NarrowbandModel, es-ES_BroadbandModel, es-ES_NarrowbandModel, es-ES_Multimedia, es-ES_Telephony, es-LA_Telephony, es-MX_BroadbandModel, es-MX_NarrowbandModel, es-PE_BroadbandModel, es-PE_NarrowbandModel, fr-CA_BroadbandModel, fr-CA_Multimedia, fr-CA_NarrowbandModel, fr-CA_Telephony, fr-FR_BroadbandModel, fr-FR_Multimedia, fr-FR_NarrowbandModel, fr-FR_Telephony, hi-IN_Telephony, it-IT_BroadbandModel, it-IT_NarrowbandModel, it-IT_Multimedia, it-IT_Telephony, ja-JP_BroadbandModel, ja-JP_Multimedia, ja-JP_NarrowbandModel, ja-JP_Telephony, ko-KR_BroadbandModel, ko-KR_Multimedia, ko-KR_NarrowbandModel, ko-KR_Telephony, nl-BE_Telephony, nl-NL_BroadbandModel, nl-NL_Multimedia, nl-NL_NarrowbandModel, nl-NL_Telephony, pt-BR_BroadbandModel, pt-BR_Multimedia, pt-BR_NarrowbandModel, pt-BR_Telephony, sv-SE_Telephony, zh-CN_BroadbandModel, zh-CN_NarrowbandModel, zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used for all requests sent over the connection. The base model of the specified custom language model must match the model that is specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. Omit the parameter to use the specified model with no custom language model. See Using a custom language model for speech recognition.

  • The customization ID (GUID) of a custom acoustic model that is to be used for the request. The base model of the specified custom acoustic model must match the model that is specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. Omit the parameter to use the specified model with no custom acoustic model. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used for all requests sent over the connection. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • Indicates whether IBM can use data that is sent over the connection to improve the service for future users. Specify true to prevent IBM from accessing the logged data. See Data collection.

    Default: false

  • Associates a customer ID with all data that is passed over the connection. The parameter accepts the argument customer_id={id}, where {id} is a random or generic string that is to be associated with the data. URL-encode the argument to the parameter, for example customer_id%3dmy_ID. By default, no customer ID is associated with the data. See Data labels.

Call the recognizeUsingWebSocket method to initiate a recognition request. Use the recognizeOptions argument to pass a RecognizeOptions object that provides the parameters for the request, including the audio. Use the callback argument to pass a Java BaseRecognizeCallback object to handle events from the WebSocket connection.

Call the recognizeUsingWebSocket method to initiate a recognition request. The method returns a RecognizeStream object to which you pipe the audio that is to be transcribed. You also use the object's on method to define event handlers for the request. You pass all other parameters of the request as arguments of the method.

Call the recognize_using_websocket method to initiate a recognition request. Pass the audio and all parameters of the request, including the RecognizeCallback and AudioSource objects, as arguments of the method.

Parameters of WebSocket text messages

Parameters

  • The action that is to be performed.

    Allowable values:

    • start initiates a recognition request. The message can also include any other optional parameters that are described in this table. After sending this text message, the client sends the data as a binary message (blob).

      Between recognition requests, the client can send new start messages to modify the parameters that are to be used for subsequent requests. By default, the service continues to use the parameters that were specified with the previous start message.

    • stop indicates that all audio data for the request has been sent to the service. The client can send additional requests with the same or different parameters.

  • Indicates how the data event handler is to return the response from the service:

    • If false, the event handler returns only a string with the final transcription of the recognition results, regardless of the parameters that you pass with the request. You must set the encoding for your instance of the RecognizeStream object to UTF-8 by including a call that is similar to the following line of code in your application:

      recognizeStream.setEncoding('utf8');

      Do not include this call if you set the objectMode parameter to true.

    • If true, the event handler returns the recognition results exactly as it receives them from the service: as one or more instances of a SpeechRecognitionResults object.

    For more information, see the Example request for the method.

  • The audio that is to be transcribed.

    An AudioSource object that provides the audio that is to be transcribed.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream, audio/alaw, audio/basic, audio/flac, audio/g729, audio/l16, audio/mp3, audio/mpeg, audio/mulaw, audio/ogg, audio/ogg;codecs=opus, audio/ogg;codecs=vorbis, audio/wav, audio/webm, audio/webm;codecs=opus, audio/webm;codecs=vorbis]

  • A BaseRecognizeCallback object that implements the RecognizeCallback interface to handle events from the WebSocket connection. Override the definitions of the object's default methods to respond to events as needed by your application.

    A RecognizeCallback object that defines methods to handle events from the WebSocket connection. Override the definitions of the object's default methods to respond to events as needed by your application.

  • The model to use for all speech recognition requests that are sent over the connection. See Using a model for speech recognition.

    The default model is en-US_BroadbandModel. For Speech to Text for IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service. For more information, see Using the default model.

    Allowable values: [ar-MS_BroadbandModel, ar-MS_Telephony, cs-CZ_Telephony, de-DE_BroadbandModel, de-DE_Multimedia, de-DE_NarrowbandModel, de-DE_Telephony, en-AU_BroadbandModel, en-AU_Multimedia, en-AU_NarrowbandModel, en-AU_Telephony, en-GB_BroadbandModel, en-GB_Multimedia, en-GB_NarrowbandModel, en-GB_Telephony, en-IN_Telephony, en-US_BroadbandModel, en-US_Multimedia, en-US_NarrowbandModel, en-US_ShortForm_NarrowbandModel, en-US_Telephony, en-WW_Medical_Telephony, es-AR_BroadbandModel, es-AR_NarrowbandModel, es-CL_BroadbandModel, es-CL_NarrowbandModel, es-CO_BroadbandModel, es-CO_NarrowbandModel, es-ES_BroadbandModel, es-ES_NarrowbandModel, es-ES_Multimedia, es-ES_Telephony, es-LA_Telephony, es-MX_BroadbandModel, es-MX_NarrowbandModel, es-PE_BroadbandModel, es-PE_NarrowbandModel, fr-CA_BroadbandModel, fr-CA_Multimedia, fr-CA_NarrowbandModel, fr-CA_Telephony, fr-FR_BroadbandModel, fr-FR_Multimedia, fr-FR_NarrowbandModel, fr-FR_Telephony, hi-IN_Telephony, it-IT_BroadbandModel, it-IT_NarrowbandModel, it-IT_Multimedia, it-IT_Telephony, ja-JP_BroadbandModel, ja-JP_Multimedia, ja-JP_NarrowbandModel, ja-JP_Telephony, ko-KR_BroadbandModel, ko-KR_Multimedia, ko-KR_NarrowbandModel, ko-KR_Telephony, nl-BE_Telephony, nl-NL_BroadbandModel, nl-NL_Multimedia, nl-NL_NarrowbandModel, nl-NL_Telephony, pt-BR_BroadbandModel, pt-BR_Multimedia, pt-BR_NarrowbandModel, pt-BR_Telephony, sv-SE_Telephony, zh-CN_BroadbandModel, zh-CN_NarrowbandModel, zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used for the request. The base model of the specified custom language model must match the model that is specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. Omit the parameter to use the specified model with no custom language model. See Using a custom language model for speech recognition.

  • The customization ID (GUID) of a custom acoustic model that is to be used for the request. The base model of the specified custom acoustic model must match the model that is specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. Omit the parameter to use the specified model with no custom acoustic model. See Using a custom acoustic model for speech recognition.

  • If you specify a customization ID when you open the connection, If you specify a customization ID, you can use the customization weight to tell the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models

    • 0.2 for most next-generation models

    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when you set the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The version of the specified base model that is to be used for the request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • The time in seconds after which, if only silence (no speech) is detected in the audio, the connection is closed. The default is 30 seconds. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • If true, the service returns intermediate hypotheses as a stream of JSON SpeechRecognitionResults objects before returning final results for an utterance. If false, the service returns only a single SpeechRecognitionResults object with final results for any utterance. (See the objectMode parameter for information about controlling the response from the method.)

    • For previous-generation models, interim results are available for all models. To receive interim results, set the interim_results interimResults parameter to true.

    • For next-generation models, interim results are available only for those models that support low latency. To receive interim results, see both the interim_results interimResults and low_latency lowLatency parameters to true.

    For for information, see:

    Default: false

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, no word confidence measures are returned. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, no smart formatting is performed.

    Beta: The parameter is beta functionality. It can be used with US English, Japanese, and Spanish (all dialects) transcription only. See Smart formatting.

    Default: false

  • Smart formatting version is for next-generation models is supported in US English, Brazilian Portuguese, French and German languages.

    See Smart formatting Version.

    Default: 0

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels speakerLabels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.

    • For next-generation models, can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    Beta: The parameter is beta functionality. See Speaker labels.

    Default: false

  • If you are passing requests through a proxy, specify the hostname of the proxy server. Use the http_proxy_port parameter to specify the port number at which the proxy listens. Omit both parameters if you are not using a proxy.

    Default: None

  • If you are passing requests through a proxy, specify the port number at which the proxy service listens. Use the http_proxy_host parameter to specify the hostname of the proxy. Omit both parameters if you are not using a proxy.

    Default: None

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id languageCustomizationId parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold keywordsThreshold parameters) and returns only a single final transcript (forces the max_alternatives maxAlternatives parameter to be 1).

    Beta: The parameter is beta functionality. It can be used with US English, Japanese, and Korean transcription only. See Numeric redaction.

    Default: false

  • If true, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval that is specified by the processing_metrics_interval processingMetricsInterval parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics. See Processing metrics.

    Default: false

  • Specifies the interval in seconds at which the service is to return processing metrics. The parameter is ignored unless the processing_metrics processingMetrics parameter is set to true.

    The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125.

    The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events.

    See Processing metrics.

    Default: 1.0

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics. See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.

    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds. The default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).

    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.

    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value between 0.0 and 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).

    • 0.5 provides a reasonable level of audio suppression for general usage.

    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    Note: The low_latency lowLatency parameter is not available for previous-generation Broadband and Narrowband models. It is available only for some next-generation models. To obtain interim results with a next-generation model, the model must support low latency and both the interim_results interimResults and low_latency lowLatency parameters must be set to true.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.

    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    Beta: The parameter is beta functionality. It is not available for previous-generation models.

    See Character insertion bias.",

Example request

var access_token = '{access_token}';
var wsURI = '{ws_url}/v1/recognize'
  + '?access_token=' + access_token
  + '&model=en-US_BroadbandModel';

var websocket = new WebSocket(wsURI);
websocket.onopen = function(evt) { onOpen(evt) };
websocket.onclose = function(evt) { onClose(evt) };
websocket.onmessage = function(evt) { onMessage(evt) };
websocket.onerror = function(evt) { onError(evt) };

function onOpen(evt) {
  var message = {
    action: 'start',
    keywords: ['colorado', 'tornado', 'tornadoes'],
    keywords_threshold: 0.5,
    max-alternatives: 3
  };
  websocket.send(JSON.stringify(message));

  // Prepare and send the audio file.
  websocket.send(blob);

  websocket.send(JSON.stringify({action: 'stop'}));
}

function onClose(evt) {
  console.log(evt.data);
}

function onMessage(evt) {
  console.log(evt.data);
}

function onError(evt) {
  console.log(evt.data);
}

Example request

/* * * * *
 * IBM CLOUD: Use the following code only to
 * authenticate to IBM Cloud.
 * * * * */

IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
SpeechToText speechToText = new SpeechToText(authenticator);
speechToText.setServiceUrl("{url}");

/* * * * *
 * IBM CLOUD PAK FOR DATA: Use the following code
 * only to authenticate to IBM Cloud Pak for Data.
 * * * * */

// CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}", "{username}", "{password}");
// SpeechToText speechToText = new SpeechToText(authenticator);
// speechToText.setServiceUrl("{url}");

try {
  RecognizeOptions recognizeOptions = new RecognizeOptions.Builder()
    .audio(new FileInputStream("audio-file.flac"))
    .contentType("audio/flac")
    .model("en-US_BroadbandModel")
    .keywords(Arrays.asList("colorado", "tornado", "tornadoes"))
    .keywordsThreshold((float) 0.5)
    .maxAlternatives(3)
    .build();

  BaseRecognizeCallback baseRecognizeCallback =
    new BaseRecognizeCallback() {

      @Override
      public void onTranscription
        (SpeechRecognitionResults speechRecognitionResults) {
          System.out.println(speechRecognitionResults);
      }

      @Override
      public void onDisconnected() {
        System.exit(0);
      }

    };

  speechToText.recognizeUsingWebSocket(recognizeOptions,
    baseRecognizeCallback);
} catch (FileNotFoundException e) {
  e.printStackTrace();
}

Example request

const fs = require('fs');
const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');

/* * * * *
 * IBM CLOUD: Use the following code only to
 * authenticate to IBM Cloud.
 * * * * */

const { IamAuthenticator } = require('ibm-watson/auth');
const speechToText = new SpeechToTextV1({
  authenticator: new IamAuthenticator({
    apikey: '{apikey}',
  }),
  serviceUrl: '{url}',
});

/* * * * *
 * IBM CLOUD PAK FOR DATA: Use the following code
 * only to authenticate to IBM Cloud Pak for Data.
 * * * * */

// const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
// const speechToText = new SpeechToTextV1({
//   authenticator: new CloudPakForDataAuthenticator({
//     username: '{username}',
//     password: '{password}',
//     url: 'https://{cpd_cluster_host}{:port}',
//  }),
//  serviceUrl: '{url}',
// });

const params = {
  objectMode: true,
  contentType: 'audio/flac',
  model: 'en-US_BroadbandModel',
  keywords: ['colorado', 'tornado', 'tornadoes'],
  keywordsThreshold: 0.5,
  maxAlternatives: 3,
};

// Create the stream.
const recognizeStream = speechToText.recognizeUsingWebSocket(params);

// Pipe in the audio.
fs.createReadStream('audio-file.flac').pipe(recognizeStream);

/*
 * Uncomment the following two lines of code ONLY if `objectMode` is `false`.
 *
 * WHEN USED TOGETHER, the two lines pipe the final transcript to the named
 * file and produce it on the console.
 *
 * WHEN USED ALONE, the following line pipes just the final transcript to
 * the named file but produces numeric values rather than strings on the
 * console.
 */
// recognizeStream.pipe(fs.createWriteStream('transcription.txt'));

/*
 * WHEN USED ALONE, the following line produces just the final transcript
 * on the console.
 */
// recognizeStream.setEncoding('utf8');

// Listen for events.
recognizeStream.on('data', function(event) { onEvent('Data:', event); });
recognizeStream.on('error', function(event) { onEvent('Error:', event); });
recognizeStream.on('close', function(event) { onEvent('Close:', event); });

// Display events on the console.
function onEvent(name, event) {
    console.log(name, JSON.stringify(event, null, 2));
};

Example request

import json
from os.path import join, dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback, AudioSource

##########
# IBM CLOUD: Use the following code only to
# authenticate to IBM Cloud.
##########

from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
authenticator = IAMAuthenticator('{apikey}')
speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)
speech_to_text.set_service_url('{url}')

##########
# IBM CLOUD PAK FOR DATA: Use the following code
# only to authenticate to IBM Cloud Pak for Data.
##########

# from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
# authenticator = CloudPakForDataAuthenticator(
#     '{username}',
#     '{password}',
#     'https://{cpd_cluster_host}{:port}'
# )
# speech_to_text = SpeechToTextV1(
#     authenticator=authenticator
# )
# speech_to_text.set_service_url('{url}')

class MyRecognizeCallback(RecognizeCallback):
    def __init__(self):
        RecognizeCallback.__init__(self)

    def on_data(self, data):
        print(json.dumps(data, indent=2))

    def on_error(self, error):
        print('Error received: {}'.format(error))

    def on_inactivity_timeout(self, error):
        print('Inactivity timeout: {}'.format(error))

myRecognizeCallback = MyRecognizeCallback()

with open(join(dirname(__file__), './.', 'audio-file.flac'),
              'rb') as audio_file:
    audio_source = AudioSource(audio_file)
    speech_to_text.recognize_using_websocket(
        audio=audio_source,
        content_type='audio/flac',
        recognize_callback=myRecognizeCallback,
        model='en-US_BroadbandModel',
        keywords=['colorado', 'tornado', 'tornadoes'],
        keywords_threshold=0.5,
        max_alternatives=3)

Response

Successful recognition returns one or more instances of a SpeechRecognitionResults object. The contents of the response depend on the parameters you send with the recognition request, including the interim_results interimResults parameter. For more information, see the results for the Recognize audio method.

If the objectMode parameter is true, successful recognition returns one or more instances of a SpeechRecognitionResults object. The contents of the response depend on the parameters you send with the recognition request, including the interimResults parameter. For more information, see the results for the Recognize audio method.

If the objectMode parameter is false, successful recognition returns only a single string with the final transcription results.

Response handling

Response handling for the WebSocket interface is different from HTTP response handling. The WebSocket constructor returns an instance of a WebSocket connection object. You assign application-specific calls to the following methods of the object to handle events that are associated with the connection. Each event handler must accept a single argument for an event from the connection. The event that it accepts causes it to execute.

Methods

  • The status of the connection's opening.

  • Response messages from the service, including the results of the request as one or more JSON SpeechRecognitionResults objects.

  • Errors for the connection or request.

  • The status of the connection's closing.

The callback parameter of the recognizeUsingWebSocket method accepts a Java object of type BaseRecognizeCallback, which implements the RecognizeCallback interface to handle events from the WebSocket connection. You override the definitions of the following default empty methods of the object to handle events that are associated with the connection and the request. The methods are called when their associated events occur.

Methods

  • The WebSocket connection is established.

  • The service is listening for audio.

  • Results for the request are received from the service.
  • Final results for the request have been returned by the service.

  • An error occurs in the WebSocket connection.

  • An inactivity timeout occurs for the request.

  • The WebSocket connection is closed.

You handle events that are associated with the WebSocket connection and the request by defining event-handler methods on the RecognizeCallback object that is returned by the recognizeUsingWebSocket method. The methods are called when their associated events occur. You can define handlers for the following events by using the object's on method. For more information about streams and events, see the Node.js documentation.

Events

  • Results for the request are received on the stream.

  • Data is available to be read from the stream.

  • No data remains to be read from the stream.
  • The WebSocket connection is closed.

  • An error occurs in the WebSocket connection.

The recognize_callback parameter of the recognize_using_websocket method accepts an object of type RecognizeCallback. The object defines the methods that handle events from the WebSocket connection. You can override the definitions of the following default empty methods of the object to handle events that are associated with the connection and the request. The methods are called when their associated events occur.

Methods

  • The WebSocket connection is established.

  • The service is listening for audio.

  • Returns all response data for the request from the service.
  • Returns interim results or maximum alternatives from the service when those responses are requested.

  • Returns final transcription results for the request from the service.

  • Reports an error in the WebSocket connection.

  • Reports an inactivity timeout for the request.

The connection can produce the following return codes.

Return code

  • The connection closed normally.

  • The connection closed because the remote peer is leaving.

  • The connection closed due to a protocol error.

  • The connection closed because the service could not process the input from the client.

  • Reserved response code.

  • The connection closed for a reason other than those defined by the remaining return codes.

  • The connection closed abnormally.

  • The connection closed because the service received invalid data.

  • The connection closed due to a policy violation.

  • The connection closed because the frame size exceeded the 4 MB limit.

  • The connection closed because the client requested a required extension that is not available.

  • The connection closed because the service encountered an unexpected internal condition that prevents it from fulfilling the request.

  • The connection was not established due to a TLS handshake error.

Example response

{
  "results": [
    {
      "final": true,
      "alternatives": [
        {
          "transcript": "several tornadoes touch down as a line of severe thunderstorms swept through Colorado on Sunday ",
          "confidence": 0.89
        },
        {
          "transcript": "several tornadoes touch down is a line of severe thunderstorms swept through Colorado on Sunday "
        },
        {
          "transcript": "several tornadoes touched down as a line of severe thunderstorms swept through Colorado on Sunday "
        }
      ],
      "keywords_result": {
        "tornadoes": [
          {
            "normalized_text": "tornadoes",
            "start_time": 1.52,
            "end_time": 2.15,
            "confidence": 1.0
          }
        ],
        "colorado": [
          {
            "normalized_text": "Colorado",
            "start_time": 4.95,
            "end_time": 5.59,
            "confidence": 0.98
          }
        ]
      }
    }
  ],
  "result_index": 0
}

Methods

List models

Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models.

See also: Listing all models.

Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models.

See also: Listing all models.

Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models.

See also: Listing all models.

Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models.

See also: Listing all models.

Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models.

See also: Listing all models.

GET /v1/models
ListModels()
ServiceCall<SpeechModels> listModels()
listModels(params)
list_models(
        self,
        **kwargs,
    ) -> DetailedResponse

Request

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/models"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/models"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListModels();
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListModels();
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    SpeechModels speechModels = speechToText.listModels().execute().getResult();
    System.out.println(speechModels);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    SpeechModels speechModels = speechToText.listModels().execute().getResult();
    System.out.println(speechModels);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.listModels()
      .then(speechModels => {
        console.log(JSON.stringify(speechModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.listModels()
      .then(speechModels => {
        console.log(JSON.stringify(speechModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_models = speech_to_text.list_models().get_result()
    print(json.dumps(speech_models, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_models = speech_to_text.list_models().get_result()
    print(json.dumps(speech_models, indent=2))

Response

Information about the available language models.

Information about the available language models.

Information about the available language models.

Information about the available language models.

Information about the available language models.

Status Code

  • OK. The request succeeded.

  • Not Acceptable. The request specified an Accept header with an incompatible content type.

  • Unsupported Media Type. The request specified an unacceptable media type.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "models": [
        {
          "name": "pt-BR_NarrowbandModel",
          "language": "pt-BR",
          "url": "{url}/v1/models/pt-BR_NarrowbandModel",
          "rate": 8000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "Brazilian Portuguese narrowband model."
        },
        {
          "name": "ko-KR_BroadbandModel",
          "language": "ko-KR",
          "url": "{url}/models/ko-KR_BroadbandModel",
          "rate": 16000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "Korean broadband model."
        },
        {
          "name": "fr-FR_BroadbandModel",
          "language": "fr-FR",
          "url": "{url}/v1/models/fr-FR_BroadbandModel",
          "rate": 16000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "French broadband model."
        }
      ]
    }
  • {
      "models": [
        {
          "name": "pt-BR_NarrowbandModel",
          "language": "pt-BR",
          "url": "{url}/v1/models/pt-BR_NarrowbandModel",
          "rate": 8000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "Brazilian Portuguese narrowband model."
        },
        {
          "name": "ko-KR_BroadbandModel",
          "language": "ko-KR",
          "url": "{url}/models/ko-KR_BroadbandModel",
          "rate": 16000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "Korean broadband model."
        },
        {
          "name": "fr-FR_BroadbandModel",
          "language": "fr-FR",
          "url": "{url}/v1/models/fr-FR_BroadbandModel",
          "rate": 16000,
          "supported_features": {
            "custom_language_model": true,
            "custom_acoustic_model": true,
            "speaker_labels": true
          },
          "description": "French broadband model."
        }
      ]
    }

Get a model

Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things.

See also: Listing a specific model.

Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things.

See also: Listing a specific model.

Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things.

See also: Listing a specific model.

Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things.

See also: Listing a specific model.

Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things.

See also: Listing a specific model.

GET /v1/models/{model_id}
GetModel(string modelId)
ServiceCall<SpeechModel> getModel(GetModelOptions getModelOptions)
getModel(params)
get_model(
        self,
        model_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetModelOptions.Builder to create a GetModelOptions object that contains the parameter values for the getModel method.

Path Parameters

  • The identifier of the model in the form of its name from the output of the List models method.

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

parameters

  • The identifier of the model in the form of its name from the output of the List models method.

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

The getModel options.

parameters

  • The identifier of the model in the form of its name from the output of the List models method.

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

parameters

  • The identifier of the model in the form of its name from the output of the List models method.

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/models/en-US_BroadbandModel"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/models/en-US_BroadbandModel"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetModel(
        modelId: "en-US_BroadbandModel"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetModel(
        modelId: "en-US_BroadbandModel"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetModelOptions getModelOptions = new GetModelOptions.Builder()
      .modelId("en-US_BroadbandModel")
      .build();
    
    SpeechModel speechModel = speechToText.getModel(getModelOptions).execute().getResult();
    System.out.println(speechModel);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetModelOptions getModelOptions = new GetModelOptions.Builder()
      .modelId("en-US_BroadbandModel")
      .build();
    
    SpeechModel speechModel = speechToText.getModel(getModelOptions).execute().getResult();
    System.out.println(speechModel);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getModelParams = {
      modelId: 'en-US_BroadbandModel',
    };
    
    speechToText.getModel(getModelParams)
      .then(speechModel => {
        console.log(JSON.stringify(speechModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getModelParams = {
      modelId: 'en-US_BroadbandModel',
    };
    
    speechToText.getModel(getModelParams)
      .then(speechModel => {
        console.log(JSON.stringify(speechModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_model = speech_to_text.get_model('en-US_BroadbandModel').get_result()
    print(json.dumps(speech_model, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_model = speech_to_text.get_model('en-US_BroadbandModel').get_result()
    print(json.dumps(speech_model, indent=2))

Response

Information about an available language model.

Information about an available language model.

Information about an available language model.

Information about an available language model.

Information about an available language model.

Status Code

  • OK. The request succeeded.

  • Not Found. The specified model_id was not found.

  • Not Acceptable. The request specified an Accept header with an incompatible content type.

  • Unsupported Media Type. The request specified an unacceptable media type.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "rate": 16000,
      "name": "en-US_BroadbandModel",
      "language": "en-US",
      "url": "{url}/v1/models/en-US_BroadbandModel",
      "supported_features": {
        "custom_language_model": true,
        "custom_acoustic_model": true,
        "speaker_labels": true
      },
      "description": "US English broadband model."
    }
  • {
      "rate": 16000,
      "name": "en-US_BroadbandModel",
      "language": "en-US",
      "url": "{url}/v1/models/en-US_BroadbandModel",
      "supported_features": {
        "custom_language_model": true,
        "custom_acoustic_model": true,
        "speaker_labels": true
      },
      "description": "US English broadband model."
    }

Recognize audio

Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Making a basic HTTP request.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Multipart speech recognition

Note: The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition.

The HTTP POST method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request.

Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords.

See also: Making a multipart HTTP request.

Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Making a basic HTTP request.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Multipart speech recognition

Note: The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition.

The HTTP POST method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request.

Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords.

See also: Making a multipart HTTP request.

Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Making a basic HTTP request.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Multipart speech recognition

Note: The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition.

The HTTP POST method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request.

Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords.

See also: Making a multipart HTTP request.

Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Making a basic HTTP request.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Multipart speech recognition

Note: The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition.

The HTTP POST method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request.

Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords.

See also: Making a multipart HTTP request.

Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Making a basic HTTP request.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Multipart speech recognition

Note: The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition.

The HTTP POST method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request.

Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords.

See also: Making a multipart HTTP request.

POST /v1/recognize
Recognize(System.IO.MemoryStream audio, string contentType = null, string model = null, string languageCustomizationId = null, string acousticCustomizationId = null, string baseModelVersion = null, double? customizationWeight = null, long? inactivityTimeout = null, List<string> keywords = null, float? keywordsThreshold = null, long? maxAlternatives = null, float? wordAlternativesThreshold = null, bool? wordConfidence = null, bool? timestamps = null, bool? profanityFilter = null, bool? smartFormatting = null, bool? speakerLabels = null, string grammarName = null, bool? redaction = null, bool? audioMetrics = null, double? endOfPhraseSilenceTime = null, bool? splitTranscriptAtPhraseEnd = null, float? speechDetectorSensitivity = null, float? backgroundAudioSuppression = null, bool? lowLatency = null, float? characterInsertionBias = null)
ServiceCall<SpeechRecognitionResults> recognize(RecognizeOptions recognizeOptions)
recognize(params)
recognize(
        self,
        audio: BinaryIO,
        *,
        content_type: str = None,
        model: str = None,
        language_customization_id: str = None,
        acoustic_customization_id: str = None,
        base_model_version: str = None,
        customization_weight: float = None,
        inactivity_timeout: int = None,
        keywords: List[str] = None,
        keywords_threshold: float = None,
        max_alternatives: int = None,
        word_alternatives_threshold: float = None,
        word_confidence: bool = None,
        timestamps: bool = None,
        profanity_filter: bool = None,
        smart_formatting: bool = None,
        speaker_labels: bool = None,
        grammar_name: str = None,
        redaction: bool = None,
        audio_metrics: bool = None,
        end_of_phrase_silence_time: float = None,
        split_transcript_at_phrase_end: bool = None,
        speech_detector_sensitivity: float = None,
        background_audio_suppression: float = None,
        low_latency: bool = None,
        character_insertion_bias: float = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the RecognizeOptions.Builder to create a RecognizeOptions object that contains the parameter values for the recognize method.

Custom Headers

  • Set to chunked to send the audio in streaming mode. The data does not need to exist fully before being streamed to the service. See Audio transmission.

    Allowable values: [chunked]

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

Query Parameters

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • Smart formatting version is for next-generation models and that is supported in US English, Brazilian Portuguese, French and German languages.

    Default: 0

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0

The audio to transcribe.

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

The recognize options.

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/flac" --data-binary @audio-file2.flac "{url}/v1/recognize?word_alternatives_threshold=0.9&keywords=colorado%2Ctornado%2Ctornadoes&keywords_threshold=0.5"

    Download sample file audio-file2.flac

  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/flac" --data-binary @audio-file2.flac "{url}/v1/recognize?word_alternatives_threshold=0.9&keywords=colorado%2Ctornado%2Ctornadoes&keywords_threshold=0.5"

    Download sample file audio-file2.flac

  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.Recognize(
        audio: new MemoryStream(File.ReadAllBytes("audio-file2.flac")),
        contentType: "audio/flac",
        wordAlternativesThreshold: 0.9f,
        keywords: new List<string>()
        {
            "colorado",
            "tornado",
            "tornadoes"
        },
        keywordsThreshold: 0.5f
        );
    
    Console.WriteLine(result.Response);

    Download sample file audio-file2.flac

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.Recognize(
        audio: new MemoryStream(File.ReadAllBytes("audio-file2.flac")),
        contentType: "audio/flac",
        wordAlternativesThreshold: 0.9f,
        keywords: new List<string>()
        {
            "colorado",
            "tornado",
            "tornadoes"
        },
        keywordsThreshold: 0.5f
        );
    
    Console.WriteLine(result.Response);

    Download sample file audio-file2.flac

  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      RecognizeOptions recognizeOptions = new RecognizeOptions.Builder()
        .audio(new FileInputStream("audio-file2.flac"))
        .contentType("audio/flac")
        .wordAlternativesThreshold((float) 0.9)
        .keywords(Arrays.asList("colorado", "tornado", "tornadoes"))
        .keywordsThreshold((float) 0.5)
        .build();
      
      SpeechRecognitionResults speechRecognitionResults =
        speechToText.recognize(recognizeOptions).execute().getResult();
      System.out.println(speechRecognitionResults);
      } catch (FileNotFoundException e) {
        e.printStackTrace();
      }
    }

    Download sample file audio-file2.flac

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      RecognizeOptions recognizeOptions = new RecognizeOptions.Builder()
        .audio(new FileInputStream("audio-file2.flac"))
        .contentType("audio/flac")
        .wordAlternativesThreshold((float) 0.9)
        .keywords(Arrays.asList("colorado", "tornado", "tornadoes"))
        .keywordsThreshold((float) 0.5)
        .build();
      
      SpeechRecognitionResults speechRecognitionResults =
        speechToText.recognize(recognizeOptions).execute().getResult();
      System.out.println(speechRecognitionResults);
      } catch (FileNotFoundException e) {
        e.printStackTrace();
      }
    }

    Download sample file audio-file2.flac

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const recognizeParams = {
      audio: fs.createReadStream('audio-file2.flac'),
      contentType: 'audio/flac',
      wordAlternativesThreshold: 0.9,
      keywords: ['colorado', 'tornado', 'tornadoes'],
      keywordsThreshold: 0.5,
    };
    
    speechToText.recognize(recognizeParams)
      .then(speechRecognitionResults => {
        console.log(JSON.stringify(speechRecognitionResults, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file audio-file2.flac

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const recognizeParams = {
      audio: fs.createReadStream('audio-file2.flac'),
      contentType: 'audio/flac',
      wordAlternativesThreshold: 0.9,
      keywords: ['colorado', 'tornado', 'tornadoes'],
      keywordsThreshold: 0.5,
    };
    
    speechToText.recognize(recognizeParams)
      .then(speechRecognitionResults => {
        console.log(JSON.stringify(speechRecognitionResults, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file audio-file2.flac

  • from os.path import join, dirname
    import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio-file2.flac'),
                   'rb') as audio_file:
        speech_recognition_results = speech_to_text.recognize(
            audio=audio_file,
            content_type='audio/flac',
            word_alternatives_threshold=0.9,
            keywords=['colorado', 'tornado', 'tornadoes'],
            keywords_threshold=0.5
        ).get_result()
    print(json.dumps(speech_recognition_results, indent=2))

    Download sample file audio-file2.flac

  • from os.path import join, dirname
    import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio-file2.flac'),
                   'rb') as audio_file:
        speech_recognition_results = speech_to_text.recognize(
            audio=audio_file,
            content_type='audio/flac',
            word_alternatives_threshold=0.9,
            keywords=['colorado', 'tornado', 'tornadoes'],
            keywords_threshold=0.5
        ).get_result()
    print(json.dumps(speech_recognition_results, indent=2))

    Download sample file audio-file2.flac

Response

The complete results for a speech recognition request.

The complete results for a speech recognition request.

The complete results for a speech recognition request.

The complete results for a speech recognition request.

The complete results for a speech recognition request.

Status Code

  • OK. The request succeeded.

  • Bad Request. The request failed because of a user input error. For example, the request passed audio that does not match the indicated format or failed to specify a required audio format; specified a custom language or custom acoustic model that is not in the available state; or experienced an inactivity timeout. Specific messages include

    • Model {model} not found
    • Requested model is not available
    • This 8000hz audio input requires a narrow band model. See /v1/models for a listp of available models.
    • speaker_labels is not a supported feature for model {model}
    • keywords_threshold value must be between zero and one (inclusive)
    • word_alternatives_threshold value must be between zero and one (inclusive)
    • You cannot specify both 'customization_id' and 'language_customization_id' parameter!
    • No speech detected for 30s
    • Unable to transcode data stream application/octet-stream -> audio/l16
    • Stream was {number} bytes but needs to be at least 100 bytes.
    • keyword {keyword} length exceeds the maximum length 1024
    • low_latency is not a supported feature for model {model}
    • Character insertion bias must be a value between -1 and 1.
  • Not Found. The specified model does not exist or, for IBM Cloud Pak for Data, the model parameter was not specified but the default model is not installed. The message is Model '{model}' not found.

  • Not Acceptable. The request specified an Accept header with an incompatible content type.

  • Request Timeout. The connection was closed due to inactivity (session timeout) for 30 seconds.

  • Payload Too Large. The request passed an audio file that exceeded the currently supported data limit.

  • Unsupported Media Type. The request specified an unacceptable media type.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "results": [
        {
          "word_alternatives": [
            {
              "start_time": 0.15,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "a"
                }
              ],
              "end_time": 0.3
            },
            {
              "start_time": 0.3,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "line"
                }
              ],
              "end_time": 0.64
            },
            {
              "start_time": 0.64,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "of"
                }
              ],
              "end_time": 0.73
            },
            {
              "start_time": 0.73,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "severe"
                }
              ],
              "end_time": 1.08
            },
            {
              "start_time": 1.08,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "thunderstorms"
                }
              ],
              "end_time": 1.85
            },
            {
              "start_time": 1.85,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "with"
                }
              ],
              "end_time": 2
            },
            {
              "start_time": 2,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "several"
                }
              ],
              "end_time": 2.52
            },
            {
              "start_time": 2.52,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "possible"
                }
              ],
              "end_time": 3.03
            },
            {
              "start_time": 3.03,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "tornadoes"
                }
              ],
              "end_time": 3.85
            },
            {
              "start_time": 3.95,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "is"
                }
              ],
              "end_time": 4.13
            },
            {
              "start_time": 4.13,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "approaching"
                }
              ],
              "end_time": 4.58
            },
            {
              "start_time": 4.58,
              "alternatives": [
                {
                  "confidence": 0.96,
                  "word": "Colorado"
                }
              ],
              "end_time": 5.16
            },
            {
              "start_time": 5.16,
              "alternatives": [
                {
                  "confidence": 0.95,
                  "word": "on"
                }
              ],
              "end_time": 5.32
            },
            {
              "start_time": 5.32,
              "alternatives": [
                {
                  "confidence": 0.98,
                  "word": "Sunday"
                }
              ],
              "end_time": 6.04
            }
          ],
          "keywords_result": {
            "colorado": [
              {
                "normalized_text": "Colorado",
                "start_time": 4.58,
                "confidence": 0.96,
                "end_time": 5.16
              }
            ],
            "tornadoes": [
              {
                "normalized_text": "tornadoes",
                "start_time": 3.03,
                "confidence": 1,
                "end_time": 3.85
              }
            ]
          },
          "alternatives": [
            {
              "confidence": 1,
              "transcript": "a line of severe thunderstorms with several possible tornadoes is approaching Colorado on Sunday "
            }
          ],
          "final": true
        }
      ],
      "result_index": 0
    }
  • {
      "results": [
        {
          "word_alternatives": [
            {
              "start_time": 0.15,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "a"
                }
              ],
              "end_time": 0.3
            },
            {
              "start_time": 0.3,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "line"
                }
              ],
              "end_time": 0.64
            },
            {
              "start_time": 0.64,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "of"
                }
              ],
              "end_time": 0.73
            },
            {
              "start_time": 0.73,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "severe"
                }
              ],
              "end_time": 1.08
            },
            {
              "start_time": 1.08,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "thunderstorms"
                }
              ],
              "end_time": 1.85
            },
            {
              "start_time": 1.85,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "with"
                }
              ],
              "end_time": 2
            },
            {
              "start_time": 2,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "several"
                }
              ],
              "end_time": 2.52
            },
            {
              "start_time": 2.52,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "possible"
                }
              ],
              "end_time": 3.03
            },
            {
              "start_time": 3.03,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "tornadoes"
                }
              ],
              "end_time": 3.85
            },
            {
              "start_time": 3.95,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "is"
                }
              ],
              "end_time": 4.13
            },
            {
              "start_time": 4.13,
              "alternatives": [
                {
                  "confidence": 1,
                  "word": "approaching"
                }
              ],
              "end_time": 4.58
            },
            {
              "start_time": 4.58,
              "alternatives": [
                {
                  "confidence": 0.96,
                  "word": "Colorado"
                }
              ],
              "end_time": 5.16
            },
            {
              "start_time": 5.16,
              "alternatives": [
                {
                  "confidence": 0.95,
                  "word": "on"
                }
              ],
              "end_time": 5.32
            },
            {
              "start_time": 5.32,
              "alternatives": [
                {
                  "confidence": 0.98,
                  "word": "Sunday"
                }
              ],
              "end_time": 6.04
            }
          ],
          "keywords_result": {
            "colorado": [
              {
                "normalized_text": "Colorado",
                "start_time": 4.58,
                "confidence": 0.96,
                "end_time": 5.16
              }
            ],
            "tornadoes": [
              {
                "normalized_text": "tornadoes",
                "start_time": 3.03,
                "confidence": 1,
                "end_time": 3.85
              }
            ]
          },
          "alternatives": [
            {
              "confidence": 1,
              "transcript": "a line of severe thunderstorms with several possible tornadoes is approaching Colorado on Sunday "
            }
          ],
          "final": true
        }
      ],
      "result_index": 0
    }

Register a callback

Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a GET request to the callback URL. The service passes a random alphanumeric challenge string via the challenge_string parameter of the request. The request includes an Accept header that specifies text/plain as the required response type.

To be registered successfully, the callback URL must respond to the GET request from the service. The response must send status code 200 and must include the challenge string in its body. Set the Content-Type response header to text/plain. Upon receiving this response, the service responds to the original registration request with response code 201.

The service sends only a single GET request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200.

If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the POST request. It sends this signature in the X-Callback-Signature header of its GET request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications.

After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time.

See also: Registering a callback URL.

Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a GET request to the callback URL. The service passes a random alphanumeric challenge string via the challenge_string parameter of the request. The request includes an Accept header that specifies text/plain as the required response type.

To be registered successfully, the callback URL must respond to the GET request from the service. The response must send status code 200 and must include the challenge string in its body. Set the Content-Type response header to text/plain. Upon receiving this response, the service responds to the original registration request with response code 201.

The service sends only a single GET request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200.

If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the POST request. It sends this signature in the X-Callback-Signature header of its GET request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications.

After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time.

See also: Registering a callback URL.

Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a GET request to the callback URL. The service passes a random alphanumeric challenge string via the challenge_string parameter of the request. The request includes an Accept header that specifies text/plain as the required response type.

To be registered successfully, the callback URL must respond to the GET request from the service. The response must send status code 200 and must include the challenge string in its body. Set the Content-Type response header to text/plain. Upon receiving this response, the service responds to the original registration request with response code 201.

The service sends only a single GET request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200.

If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the POST request. It sends this signature in the X-Callback-Signature header of its GET request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications.

After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time.

See also: Registering a callback URL.

Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a GET request to the callback URL. The service passes a random alphanumeric challenge string via the challenge_string parameter of the request. The request includes an Accept header that specifies text/plain as the required response type.

To be registered successfully, the callback URL must respond to the GET request from the service. The response must send status code 200 and must include the challenge string in its body. Set the Content-Type response header to text/plain. Upon receiving this response, the service responds to the original registration request with response code 201.

The service sends only a single GET request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200.

If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the POST request. It sends this signature in the X-Callback-Signature header of its GET request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications.

After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time.

See also: Registering a callback URL.

Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a GET request to the callback URL. The service passes a random alphanumeric challenge string via the challenge_string parameter of the request. The request includes an Accept header that specifies text/plain as the required response type.

To be registered successfully, the callback URL must respond to the GET request from the service. The response must send status code 200 and must include the challenge string in its body. Set the Content-Type response header to text/plain. Upon receiving this response, the service responds to the original registration request with response code 201.

The service sends only a single GET request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200.

If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the POST request. It sends this signature in the X-Callback-Signature header of its GET request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications.

After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time.

See also: Registering a callback URL.

POST /v1/register_callback
RegisterCallback(string callbackUrl, string userSecret = null)
ServiceCall<RegisterStatus> registerCallback(RegisterCallbackOptions registerCallbackOptions)
registerCallback(params)
register_callback(
        self,
        callback_url: str,
        *,
        user_secret: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the RegisterCallbackOptions.Builder to create a RegisterCallbackOptions object that contains the parameter values for the registerCallback method.

Query Parameters

  • An HTTP or HTTPS URL to which callback notifications are to be sent. To be allowlisted, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the X-Callback-Signature header to verify the origin of the request.

  • A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the X-Callback-Signature header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header.

parameters

  • An HTTP or HTTPS URL to which callback notifications are to be sent. To be allowlisted, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the X-Callback-Signature header to verify the origin of the request.

  • A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the X-Callback-Signature header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header.

The registerCallback options.

parameters

  • An HTTP or HTTPS URL to which callback notifications are to be sent. To be allowlisted, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the X-Callback-Signature header to verify the origin of the request.

  • A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the X-Callback-Signature header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header.

parameters

  • An HTTP or HTTPS URL to which callback notifications are to be sent. To be allowlisted, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the X-Callback-Signature header to verify the origin of the request.

  • A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the X-Callback-Signature header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header.

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/register_callback?callback_url=http://{user_callback_path}/job_results&user_secret=ThisIsMySecret"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/register_callback?callback_url=http://{user_callback_path}/job_results&user_secret=ThisIsMySecret"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.RegisterCallback(
        callbackUrl: "http://{user_callback_path}/job_results",
        userSecret: "ThisIsMySecret"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.RegisterCallback(
        callbackUrl: "http://{user_callback_path}/job_results",
        userSecret: "ThisIsMySecret"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    RegisterCallbackOptions registerCallbackOptions = new RegisterCallbackOptions.Builder()
      .callbackUrl("http://{user_callback_path}/job_results")
      .userSecret("ThisIsMySecret")
      .build();
    
    RegisterStatus registerStatus =
      speechToText.registerCallback(registerCallbackOptions).execute().getResult();
    System.out.println(registerStatus);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    RegisterCallbackOptions registerCallbackOptions = new RegisterCallbackOptions.Builder()
      .callbackUrl("http://{user_callback_path}/job_results")
      .userSecret("ThisIsMySecret")
      .build();
    
    RegisterStatus registerStatus =
      speechToText.registerCallback(registerCallbackOptions).execute().getResult();
    System.out.println(registerStatus);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const registerCallbackParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
      userSecret: 'ThisIsMySecret',
    };
    
    speechToText.registerCallback(registerCallbackParams)
      .then(registerStatus => {
        console.log(JSON.stringify(registerStatus, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const registerCallbackParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
      userSecret: 'ThisIsMySecret',
    };
    
    speechToText.registerCallback(registerCallbackParams)
      .then(registerStatus => {
        console.log(JSON.stringify(registerStatus, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    register_status = speech_to_text.register_callback(
        'http://{user_callback_path}/job_results',
        user_secret='ThisIsMySecret'
    ).get_result()
    print(json.dumps(register_status, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    register_status = speech_to_text.register_callback(
        'http://{user_callback_path}/job_results',
        'ThisIsMySecret'
    ).get_result()
    print(json.dumps(register_status, indent=2))

Response

Information about a request to register a callback for asynchronous speech recognition.

Information about a request to register a callback for asynchronous speech recognition.

Information about a request to register a callback for asynchronous speech recognition.

Information about a request to register a callback for asynchronous speech recognition.

Information about a request to register a callback for asynchronous speech recognition.

Status Code

  • OK. The callback was already registered (allowlisted). The status included in the response is already created.

  • Created. The callback was successfully registered (allowlisted). The status included in the response is created.

  • Bad Request. The callback registration failed. The request was missing a required parameter or specified an invalid argument; the client sent an invalid response to the service's GET request during the registration process; or the client failed to respond to the server's request before the five-second timeout.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "status": "already created",
      "url": "http://{user_callback_path}/job_results"
    }
  • {
      "status": "already created",
      "url": "http://{user_callback_path}/job_results"
    }
  • {
      "status": "created",
      "url": "http://{user_callback_path}/job_results"
    }
  • {
      "status": "created",
      "url": "http://{user_callback_path}/job_results"
    }

Unregister a callback

Unregisters a callback URL that was previously allowlisted with a Register a callback request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests.

See also: Unregistering a callback URL.

Unregisters a callback URL that was previously allowlisted with a Register a callback request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests.

See also: Unregistering a callback URL.

Unregisters a callback URL that was previously allowlisted with a Register a callback request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests.

See also: Unregistering a callback URL.

Unregisters a callback URL that was previously allowlisted with a Register a callback request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests.

See also: Unregistering a callback URL.

Unregisters a callback URL that was previously allowlisted with a Register a callback request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests.

See also: Unregistering a callback URL.

POST /v1/unregister_callback
UnregisterCallback(string callbackUrl)
ServiceCall<Void> unregisterCallback(UnregisterCallbackOptions unregisterCallbackOptions)
unregisterCallback(params)
unregister_callback(
        self,
        callback_url: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the UnregisterCallbackOptions.Builder to create a UnregisterCallbackOptions object that contains the parameter values for the unregisterCallback method.

Query Parameters

  • The callback URL that is to be unregistered.

parameters

  • The callback URL that is to be unregistered.

The unregisterCallback options.

parameters

  • The callback URL that is to be unregistered.

parameters

  • The callback URL that is to be unregistered.

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/unregister_callback?callback_url=http://{user_callback_path}/job_results"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/unregister_callback?callback_url=http://{user_callback_path}/job_results"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UnregisterCallback(
        callbackUrl: "http://{user_callback_path}/job_results"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UnregisterCallback(
        callbackUrl: "http://{user_callback_path}/job_results"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UnregisterCallbackOptions unregisterCallbackOptions = new UnregisterCallbackOptions.Builder()
      .callbackUrl("http://{user_callback_path}/job_results")
      .build();
    
    speechToText.unregisterCallback(unregisterCallbackOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UnregisterCallbackOptions unregisterCallbackOptions = new UnregisterCallbackOptions.Builder()
      .callbackUrl("http://{user_callback_path}/job_results")
      .build();
    
    speechToText.unregisterCallback(unregisterCallbackOptions).execute().getResult();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const unregisterCallbackParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
    };
    
    speechToText.unregisterCallback(unregisterCallbackParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const unregisterCallbackParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
    };
    
    speechToText.unregisterCallback(unregisterCallbackParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.unregister_callback('http://{user_callback_path}/job_results')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.unregister_callback('http://{user_callback_path}/job_results')

Response

Response type: object

Status Code

  • OK. The callback URL was successfully unregistered.

  • Bad Request. The request failed because of a user input error (for example, because it failed to pass a callback URL).

  • Not Found. The specified callback URL was not found.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

No Sample Response

This method does not specify any sample responses.

Create a job

Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request:

  • By callback notification: Include the callback_url parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the events and user_token parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job.
  • By polling the service: Omit the callback_url, events, and user_token parameters. You must then use the Check jobs or Check a job methods to check the status of the job, using the latter to retrieve the results when the job is complete.

The two approaches are not mutually exclusive. You can poll the service for job status or obtain results from the service manually even if you include a callback URL. In both cases, you can include the results_ttl parameter to specify how long the results are to remain available after the job is complete. Using the HTTPS Check a job method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity.

The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface:

  • callback_url
  • events
  • user_token
  • results_ttl

You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Creating a job.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request:

  • By callback notification: Include the callback_url parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the events and user_token parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job.
  • By polling the service: Omit the callback_url, events, and user_token parameters. You must then use the Check jobs or Check a job methods to check the status of the job, using the latter to retrieve the results when the job is complete.

The two approaches are not mutually exclusive. You can poll the service for job status or obtain results from the service manually even if you include a callback URL. In both cases, you can include the results_ttl parameter to specify how long the results are to remain available after the job is complete. Using the HTTPS Check a job method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity.

The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface:

  • callback_url
  • events
  • user_token
  • results_ttl

You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Creating a job.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request:

  • By callback notification: Include the callback_url parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the events and user_token parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job.
  • By polling the service: Omit the callback_url, events, and user_token parameters. You must then use the Check jobs or Check a job methods to check the status of the job, using the latter to retrieve the results when the job is complete.

The two approaches are not mutually exclusive. You can poll the service for job status or obtain results from the service manually even if you include a callback URL. In both cases, you can include the results_ttl parameter to specify how long the results are to remain available after the job is complete. Using the HTTPS Check a job method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity.

The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface:

  • callback_url
  • events
  • user_token
  • results_ttl

You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Creating a job.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request:

  • By callback notification: Include the callback_url parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the events and user_token parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job.
  • By polling the service: Omit the callback_url, events, and user_token parameters. You must then use the Check jobs or Check a job methods to check the status of the job, using the latter to retrieve the results when the job is complete.

The two approaches are not mutually exclusive. You can poll the service for job status or obtain results from the service manually even if you include a callback URL. In both cases, you can include the results_ttl parameter to specify how long the results are to remain available after the job is complete. Using the HTTPS Check a job method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity.

The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface:

  • callback_url
  • events
  • user_token
  • results_ttl

You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Creating a job.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request:

  • By callback notification: Include the callback_url parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the events and user_token parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job.
  • By polling the service: Omit the callback_url, events, and user_token parameters. You must then use the Check jobs or Check a job methods to check the status of the job, using the latter to retrieve the results when the job is complete.

The two approaches are not mutually exclusive. You can poll the service for job status or obtain results from the service manually even if you include a callback URL. In both cases, you can include the results_ttl parameter to specify how long the results are to remain available after the job is complete. Using the HTTPS Check a job method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity.

The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface:

  • callback_url
  • events
  • user_token
  • results_ttl

You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the curl command, use the --data-binary option to upload the file for the request.)

See also: Creating a job.

Streaming mode

For requests to transcribe live audio as it becomes available, you must set the Transfer-Encoding header to chunked to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for inactivity_timeout seconds of streaming audio; use the inactivity_timeout parameter to change the default of 30 seconds.

See also:

Audio formats (content types)

The service accepts audio in the following formats (MIME types).

  • For formats that are labeled Required, you must use the Content-Type header with the request to specify the format of the audio.
  • For all other formats, you can omit the Content-Type header or specify application/octet-stream with the header to have the service automatically detect the format of the audio. (With the curl command, you can specify either "Content-Type:" or "Content-Type: application/octet-stream".)

Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio.

  • audio/alaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/basic (Required. Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Required. Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Required. Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails.

See also: Supported audio formats.

Next-generation models

The service supports next-generation Multimedia (16 kHz) and Telephony (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of Broadband and Narrowband models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy.

You specify a next-generation model by using the model query parameter, as you do a previous-generation model. Most next-generation models support the low_latency parameter, and all next-generation models support the character_insertion_bias parameter. These parameters are not available with previous-generation models.

Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters:

  • acoustic_customization_id
  • keywords and keywords_threshold
  • processing_metrics and processing_metrics_interval
  • word_alternatives_threshold

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

POST /v1/recognitions
CreateJob(System.IO.MemoryStream audio, string contentType = null, string model = null, string callbackUrl = null, string events = null, string userToken = null, long? resultsTtl = null, string languageCustomizationId = null, string acousticCustomizationId = null, string baseModelVersion = null, double? customizationWeight = null, long? inactivityTimeout = null, List<string> keywords = null, float? keywordsThreshold = null, long? maxAlternatives = null, float? wordAlternativesThreshold = null, bool? wordConfidence = null, bool? timestamps = null, bool? profanityFilter = null, bool? smartFormatting = null, bool? speakerLabels = null, string grammarName = null, bool? redaction = null, bool? processingMetrics = null, float? processingMetricsInterval = null, bool? audioMetrics = null, double? endOfPhraseSilenceTime = null, bool? splitTranscriptAtPhraseEnd = null, float? speechDetectorSensitivity = null, float? backgroundAudioSuppression = null, bool? lowLatency = null, float? characterInsertionBias = null)
ServiceCall<RecognitionJob> createJob(CreateJobOptions createJobOptions)
createJob(params)
create_job(
        self,
        audio: BinaryIO,
        *,
        content_type: str = None,
        model: str = None,
        callback_url: str = None,
        events: str = None,
        user_token: str = None,
        results_ttl: int = None,
        language_customization_id: str = None,
        acoustic_customization_id: str = None,
        base_model_version: str = None,
        customization_weight: float = None,
        inactivity_timeout: int = None,
        keywords: List[str] = None,
        keywords_threshold: float = None,
        max_alternatives: int = None,
        word_alternatives_threshold: float = None,
        word_confidence: bool = None,
        timestamps: bool = None,
        profanity_filter: bool = None,
        smart_formatting: bool = None,
        speaker_labels: bool = None,
        grammar_name: str = None,
        redaction: bool = None,
        processing_metrics: bool = None,
        processing_metrics_interval: float = None,
        audio_metrics: bool = None,
        end_of_phrase_silence_time: float = None,
        split_transcript_at_phrase_end: bool = None,
        speech_detector_sensitivity: float = None,
        background_audio_suppression: float = None,
        low_latency: bool = None,
        character_insertion_bias: float = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the CreateJobOptions.Builder to create a CreateJobOptions object that contains the parameter values for the createJob method.

Custom Headers

  • Set to chunked to send the audio in streaming mode. The data does not need to exist fully before being streamed to the service. See Audio transmission.

    Allowable values: [chunked]

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

Query Parameters

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • A URL to which callback notifications are to be sent. The URL must already be successfully allowlisted by using the Register a callback method. You can include the same callback URL with any number of job creation requests. Omit the parameter to poll the service for job completion and results.

    Use the user_token parameter to specify a unique user-specified string with each job to differentiate the callback notifications for the jobs.

  • If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are

    • recognitions.started generates a callback notification when the service begins to process the job.
    • recognitions.completed generates a callback notification when the job is complete. You must use the Check a job method to retrieve the results before they time out or are deleted.
    • recognitions.completed_with_results generates a callback notification when the job is complete. The notification includes the results of the request.
    • recognitions.failed generates a callback notification if the service experiences an error while processing the job.

    The recognitions.completed and recognitions.completed_with_results events are incompatible. You can specify only of the two events.

    If the job includes a callback URL, omit the parameter to subscribe to the default events: recognitions.started, recognitions.completed, and recognitions.failed. If the job does not include a callback URL, omit the parameter.

    Allowable values: [recognitions.started,recognitions.completed,recognitions.completed_with_results,recognitions.failed]

  • If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter.

  • The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL.

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • Smart formatting version is for next-generation models and that is supported in US English, Brazilian Portuguese, French and German languages.

    Default: 0

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval specified by the processing_metrics_interval parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics.

    See Processing metrics.

    Default: false

  • Specifies the interval in real wall-clock seconds at which the service is to return processing metrics. The parameter is ignored unless the processing_metrics parameter is set to true.

    The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125.

    The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events.

    See Processing metrics.

    Default: 1

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0

The audio to transcribe.

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • A URL to which callback notifications are to be sent. The URL must already be successfully allowlisted by using the Register a callback method. You can include the same callback URL with any number of job creation requests. Omit the parameter to poll the service for job completion and results.

    Use the user_token parameter to specify a unique user-specified string with each job to differentiate the callback notifications for the jobs.

  • If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are

    • recognitions.started generates a callback notification when the service begins to process the job.
    • recognitions.completed generates a callback notification when the job is complete. You must use the Check a job method to retrieve the results before they time out or are deleted.
    • recognitions.completed_with_results generates a callback notification when the job is complete. The notification includes the results of the request.
    • recognitions.failed generates a callback notification if the service experiences an error while processing the job.

    The recognitions.completed and recognitions.completed_with_results events are incompatible. You can specify only of the two events.

    If the job includes a callback URL, omit the parameter to subscribe to the default events: recognitions.started, recognitions.completed, and recognitions.failed. If the job does not include a callback URL, omit the parameter.

    Allowable values: [recognitions.started,recognitions.completed,recognitions.completed_with_results,recognitions.failed]

  • If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter.

  • The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL.

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval specified by the processing_metrics_interval parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics.

    See Processing metrics.

    Default: false

  • Specifies the interval in real wall-clock seconds at which the service is to return processing metrics. The parameter is ignored unless the processing_metrics parameter is set to true.

    The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125.

    The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events.

    See Processing metrics.

    Default: 1.0

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

The createJob options.

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • A URL to which callback notifications are to be sent. The URL must already be successfully allowlisted by using the Register a callback method. You can include the same callback URL with any number of job creation requests. Omit the parameter to poll the service for job completion and results.

    Use the user_token parameter to specify a unique user-specified string with each job to differentiate the callback notifications for the jobs.

  • If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are

    • recognitions.started generates a callback notification when the service begins to process the job.
    • recognitions.completed generates a callback notification when the job is complete. You must use the Check a job method to retrieve the results before they time out or are deleted.
    • recognitions.completed_with_results generates a callback notification when the job is complete. The notification includes the results of the request.
    • recognitions.failed generates a callback notification if the service experiences an error while processing the job.

    The recognitions.completed and recognitions.completed_with_results events are incompatible. You can specify only of the two events.

    If the job includes a callback URL, omit the parameter to subscribe to the default events: recognitions.started, recognitions.completed, and recognitions.failed. If the job does not include a callback URL, omit the parameter.

    Allowable values: [recognitions.started,recognitions.completed,recognitions.completed_with_results,recognitions.failed]

  • If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter.

  • The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL.

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval specified by the processing_metrics_interval parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics.

    See Processing metrics.

    Default: false

  • Specifies the interval in real wall-clock seconds at which the service is to return processing metrics. The parameter is ignored unless the processing_metrics parameter is set to true.

    The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125.

    The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events.

    See Processing metrics.

    Default: 1.0

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

parameters

  • The audio to transcribe.

  • The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.

    Allowable values: [application/octet-stream,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • The model to use for speech recognition. If you omit the model parameter, the service uses the US English en-US_BroadbandModel by default.

    For IBM Cloud Pak for Data, if you do not install the en-US_BroadbandModel, you must either specify a model with the request or specify a new default model for your installation of the service.

    See also:

    Allowable values: [ar-MS_BroadbandModel,ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-IN_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_BroadbandModel,zh-CN_NarrowbandModel,zh-CN_Telephony]

    Default: en-US_BroadbandModel

  • A URL to which callback notifications are to be sent. The URL must already be successfully allowlisted by using the Register a callback method. You can include the same callback URL with any number of job creation requests. Omit the parameter to poll the service for job completion and results.

    Use the user_token parameter to specify a unique user-specified string with each job to differentiate the callback notifications for the jobs.

  • If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are

    • recognitions.started generates a callback notification when the service begins to process the job.
    • recognitions.completed generates a callback notification when the job is complete. You must use the Check a job method to retrieve the results before they time out or are deleted.
    • recognitions.completed_with_results generates a callback notification when the job is complete. The notification includes the results of the request.
    • recognitions.failed generates a callback notification if the service experiences an error while processing the job.

    The recognitions.completed and recognitions.completed_with_results events are incompatible. You can specify only of the two events.

    If the job includes a callback URL, omit the parameter to subscribe to the default events: recognitions.started, recognitions.completed, and recognitions.failed. If the job does not include a callback URL, omit the parameter.

    Allowable values: [recognitions.started,recognitions.completed,recognitions.completed_with_results,recognitions.failed]

  • If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter.

  • The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL.

  • The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See Using a custom language model for speech recognition.

    Note: Use this parameter instead of the deprecated customization_id parameter.

  • The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the model parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See Using a custom acoustic model for speech recognition.

  • The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See Making speech recognition requests with upgraded custom models.

  • If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request.

    Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when the model was trained, the default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    See Using customization weight.

  • The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use -1 for infinity. See Inactivity timeout.

    Default: 30

  • An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords.

    You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive.

    See Keyword spotting.

  • A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See Keyword spotting.

  • The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of 0, the service uses the default value, 1. See Maximum alternatives.

    Default: 1

  • A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See Word alternatives.

  • If true, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See Word confidence.

    Default: false

  • If true, the service returns time alignment for each word. By default, no timestamps are returned. See Word timestamps.

    Default: false

  • If true, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to false to return results with no censoring.

    Note: The parameter can be used with US English and Japanese transcription only. See Profanity filtering.

    Default: true

  • If true, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting.

    Note: The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only.

    See Smart formatting.

    Default: false

  • If true, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting speaker_labels to true forces the timestamps parameter to be true, regardless of whether you specify false for the parameter.

    • For previous-generation models, the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only.
    • For next-generation models, the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only.

    See Speaker labels.

    Default: false

  • The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the language_customization_id parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource.

    See Using a grammar for speech recognition.

  • If true, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an X character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction.

    When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the keywords and keywords_threshold parameters) and returns only a single final transcript (forces the max_alternatives parameter to be 1).

    Note: The parameter can be used with US English, Japanese, and Korean transcription only.

    See Numeric redaction.

    Default: false

  • If true, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval specified by the processing_metrics_interval parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics.

    See Processing metrics.

    Default: false

  • Specifies the interval in real wall-clock seconds at which the service is to return processing metrics. The parameter is ignored unless the processing_metrics parameter is set to true.

    The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125.

    The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events.

    See Processing metrics.

    Default: 1.0

  • If true, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics.

    See Audio metrics.

    Default: false

  • Specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases.

    Specify a value for the pause interval in the range of 0.0 to 120.0.

    • A value greater than 0 specifies the interval that the service is to use for speech recognition.
    • A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter.

    The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds.

    See End of phrase silence time.

    Default: 0.8

  • If true, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.

    By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, end_of_phrase_silence_time has precedence over split_transcript_at_phrase_end.

    See Split transcript at phrase end.

    Default: false

  • The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity.

    Specify a value between 0.0 and 1.0:

    • 0.0 suppresses all audio (no speech is transcribed).
    • 0.5 (the default) provides a reasonable compromise for the level of sensitivity.
    • 1.0 suppresses no audio (speech detection sensitivity is disabled).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Speech detector sensitivity and Language model support.

    Default: 0.5

  • The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise.

    Specify a value in the range of 0.0 to 1.0:

    • 0.0 (the default) provides no suppression (background audio suppression is disabled).
    • 0.5 provides a reasonable level of audio suppression for general usage.
    • 1.0 suppresses all audio (no audio is transcribed).

    The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, 0.55) is typically more than sufficient.

    The parameter is supported with all next-generation models and with most previous-generation models. See Background audio suppression and Language model support.

    Default: 0.0

  • If true for next-generation Multimedia and Telephony models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The low_latency parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used.

    The parameter is not available for previous-generation Broadband and Narrowband models. It is available for most next-generation models.

    Default: false

  • For next-generation models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized to produce the best balance of strings of different lengths.

    The default bias is 0.0. The allowable range of values is -1.0 to 1.0.

    • Negative values bias the service to favor hypotheses with shorter strings of characters.
    • Positive values bias the service to favor hypotheses with longer strings of characters.

    As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments.

    The parameter is not available for previous-generation models.

    See Character insertion bias.

    Default: 0.0

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/flac" --data-binary @audio-file.flac "{url}/v1/recognitions?callback_url=http://{user_callback_path}/job_results&user_token=job25&timestamps=true"

    Download sample file audio-file.flac

  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/flac" --data-binary @audio-file.flac "{url}/v1/recognitions?callback_url=http://{user_callback_path}/job_results&user_token=job25&timestamps=true"

    Download sample file audio-file.flac

  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateJob(
        callbackUrl: "http://{user_callback_path}/job_results",
        userToken: "job25",
        audio: new MemoryStream(File.ReadAllBytes("audio-file.flac")),
        contentType: "audio/flac",
        timestamps: true
        );
    
    Console.WriteLine(result.Response);

    Download sample file audio-file.flac

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateJob(
        callbackUrl: "http://{user_callback_path}/job_results",
        userToken: "job25",
        audio: new MemoryStream(File.ReadAllBytes("audio-file.flac")),
        contentType: "audio/flac",
        timestamps: true
        );
    
    Console.WriteLine(result.Response);

    Download sample file audio-file.flac

  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      CreateJobOptions createJobOptions = new CreateJobOptions.Builder()
        .callbackUrl("http://{user_callback_path}/job_results")
        .userToken("job25")
        .audio(new File("audio-file.flac"))
        .contentType("audio/flac")
        .timestamps(true)
        .build();
    
      RecognitionJob recognitionJob =
        speechToText.createJob(createJobOptions).execute().getResult();
      System.out.println(recognitionJob);
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }

    Download sample file audio-file.flac

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      CreateJobOptions createJobOptions = new CreateJobOptions.Builder()
        .callbackUrl("http://{user_callback_path}/job_results")
        .userToken("job25")
        .audio(new File("audio-file.flac"))
        .contentType("audio/flac")
        .timestamps(true)
        .build();
    
      RecognitionJob recognitionJob =
        speechToText.createJob(createJobOptions).execute().getResult();
      System.out.println(recognitionJob);
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }

    Download sample file audio-file.flac

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const createJobParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
      userToken: 'job25',
      audio: fs.createReadStream('./audio-file.flac'),
      contentType: 'audio/flac',
      timestamps: true,
    };
    
    speechToText.createJob(createJobParams)
      .then(recognitionJob => {
        console.log(JSON.stringify(recognitionJob, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file audio-file.flac

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const createJobParams = {
      callbackUrl: 'http://{user_callback_path}/job_results',
      userToken: 'job25',
      audio: fs.createReadStream('audio-file.flac'),
      contentType: 'audio/flac',
      timestamps: true,
    };
    
    speechToText.createJob(createJobParams)
      .then(recognitionJob => {
        console.log(JSON.stringify(recognitionJob, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file audio-file.flac

  • from os.path import join, dirname
    import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio-file.flac'),
                   'rb') as audio_file:
        recognition_job = speech_to_text.create_job(
            audio_file,
            content_type='audio/flac',
            callback_url='http://{user_callback_path}/job_results',
            user_token='job25',
            timestamps=True
        ).get_result()
    print(json.dumps(recognition_job, indent=2))

    Download sample file audio-file.flac

  • from os.path import join, dirname
    import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio-file.flac'),
                   'rb') as audio_file:
        recognition_job = speech_to_text.create_job(
            audio_file,
            content_type='audio/flac',
            callback_url='http://{user_callback_path}/job_results',
            user_token='job25',
            timestamps=True
        ).get_result()
    print(json.dumps(recognition_job, indent=2))

    Download sample file audio-file.flac

Response

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Status Code

  • Created. The job was successfully created.

  • Bad Request. The request failed because of a user input error. For example, the request passed audio that does not match the indicated format or failed to specify a required audio format; specified a custom language or custom acoustic model that is not in the available state; or specified both the recognitions.completed and recognitions.completed_with_results events. Specific messages include

    • Model {model} not found
    • Requested model is not available
    • This 8000hz audio input requires a narrow band model. See /v1/models for a list of available models.
    • speaker_labels is not a supported feature for model {model}
    • keywords_threshold value must be between zero and one (inclusive)
    • word_alternatives_threshold value must be between zero and one (inclusive)
    • You cannot specify both 'customization_id' and 'language_customization_id' parameter!
    • No speech detected for 30s
    • Unable to transcode data stream application/octet-stream -> audio/l16
    • Stream was {number} bytes but needs to be at least 100 bytes.
    • keyword {keyword} length exceeds the maximum length 1024
    • low_latency is not a supported feature for model {model}
    • Character insertion bias must be a value between -1 and 1.
  • Not Found. The specified model does not exist or, for IBM Cloud Pak for Data, the model parameter was not specified but the default model is not installed. The message is Model '{model}' not found.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
      "status": "waiting",
      "created": "2016-08-17T19:15:17.926Z",
      "url": "{url}/v1/recognitions/4bd734c0-e575-21f3-de03-f932aa0468a0"
    }
  • {
      "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
      "status": "waiting",
      "created": "2016-08-17T19:15:17.926Z",
      "url": "{url}/v1/recognitions/4bd734c0-e575-21f3-de03-f932aa0468a0"
    }

Check jobs

Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is completed or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the Delete a job method or until the job's time to live expires, whichever comes first.

See also: Checking the status of the latest jobs.

Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is completed or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the Delete a job method or until the job's time to live expires, whichever comes first.

See also: Checking the status of the latest jobs.

Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is completed or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the Delete a job method or until the job's time to live expires, whichever comes first.

See also: Checking the status of the latest jobs.

Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is completed or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the Delete a job method or until the job's time to live expires, whichever comes first.

See also: Checking the status of the latest jobs.

Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is completed or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the Delete a job method or until the job's time to live expires, whichever comes first.

See also: Checking the status of the latest jobs.

GET /v1/recognitions
CheckJobs()
ServiceCall<RecognitionJobs> checkJobs()
checkJobs(params)
check_jobs(
        self,
        **kwargs,
    ) -> DetailedResponse

Request

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

No Request Parameters

This method does not accept any request parameters.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/recognitions"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/recognitions"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CheckJobs();
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CheckJobs();
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    RecognitionJobs recognitionJobs = speechToText.checkJobs().execute().getResult();
    System.out.println(recognitionJobs);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    RecognitionJobs recognitionJobs = speechToText.checkJobs().execute().getResult();
    System.out.println(recognitionJobs);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.checkJobs()
      .then(recognitionJobs => {
        console.log(JSON.stringify(recognitionJobs, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.checkJobs()
      .then(recognitionJobs => {
        console.log(JSON.stringify(recognitionJobs, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    recognition_jobs = speech_to_text.check_jobs().get_result()
    print(json.dumps(recognition_jobs, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    recognition_jobs = speech_to_text.check_jobs().get_result()
    print(json.dumps(recognition_jobs, indent=2))

Response

Information about current asynchronous speech recognition jobs.

Information about current asynchronous speech recognition jobs.

Information about current asynchronous speech recognition jobs.

Information about current asynchronous speech recognition jobs.

Information about current asynchronous speech recognition jobs.

Status Code

  • OK. The request succeeded.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "recognitions": [
        {
          "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
          "created": "2016-08-17T19:15:17.926Z",
          "updated": "2016-08-17T19:15:17.926Z",
          "status": "waiting",
          "user_token": "job25"
        },
        {
          "id": "4bb1dca0-f6b1-11e5-80bc-71fb7b058b20",
          "created": "2016-08-17T19:13:23.622Z",
          "updated": "2016-08-17T19:13:24.434Z",
          "status": "processing"
        },
        {
          "id": "398fcd80-330a-22ba-93ce-1a73f454dd98",
          "created": "2016-08-17T19:11:04.298Z",
          "updated": "2016-08-17T19:11:16.003Z",
          "status": "completed"
        }
      ]
    }
  • {
      "recognitions": [
        {
          "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
          "created": "2016-08-17T19:15:17.926Z",
          "updated": "2016-08-17T19:15:17.926Z",
          "status": "waiting",
          "user_token": "job25"
        },
        {
          "id": "4bb1dca0-f6b1-11e5-80bc-71fb7b058b20",
          "created": "2016-08-17T19:13:23.622Z",
          "updated": "2016-08-17T19:13:24.434Z",
          "status": "processing"
        },
        {
          "id": "398fcd80-330a-22ba-93ce-1a73f454dd98",
          "created": "2016-08-17T19:11:04.298Z",
          "updated": "2016-08-17T19:11:16.003Z",
          "status": "completed"
        }
      ]
    }

Check a job

Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is completed, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it.

You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the recognitions.completed_with_results event, and you can retrieve the results multiple times for as long as they remain available. Use the Check jobs method to request information about the most recent jobs associated with the calling credentials.

See also: Checking the status and retrieving the results of a job.

Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is completed, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it.

You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the recognitions.completed_with_results event, and you can retrieve the results multiple times for as long as they remain available. Use the Check jobs method to request information about the most recent jobs associated with the calling credentials.

See also: Checking the status and retrieving the results of a job.

Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is completed, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it.

You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the recognitions.completed_with_results event, and you can retrieve the results multiple times for as long as they remain available. Use the Check jobs method to request information about the most recent jobs associated with the calling credentials.

See also: Checking the status and retrieving the results of a job.

Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is completed, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it.

You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the recognitions.completed_with_results event, and you can retrieve the results multiple times for as long as they remain available. Use the Check jobs method to request information about the most recent jobs associated with the calling credentials.

See also: Checking the status and retrieving the results of a job.

Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is completed, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it.

You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the recognitions.completed_with_results event, and you can retrieve the results multiple times for as long as they remain available. Use the Check jobs method to request information about the most recent jobs associated with the calling credentials.

See also: Checking the status and retrieving the results of a job.

GET /v1/recognitions/{id}
CheckJob(string id)
ServiceCall<RecognitionJob> checkJob(CheckJobOptions checkJobOptions)
checkJob(params)
check_job(
        self,
        id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the CheckJobOptions.Builder to create a CheckJobOptions object that contains the parameter values for the checkJob method.

Path Parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

The checkJob options.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/recognitions/{id}"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/recognitions/{id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CheckJob(
        id: "{id}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CheckJob(
        id: "{id}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CheckJobOptions checkJobOptions = new CheckJobOptions.Builder()
      .id({id})
      .build();
    
    RecognitionJob recognitionJob =
      speechToText.checkJob(checkJobOptions).execute().getResult();
    System.out.println(recognitionJob);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CheckJobOptions checkJobOptions = new CheckJobOptions.Builder()
      .id({id})
      .build();
    
    RecognitionJob recognitionJob =
      speechToText.checkJob(checkJobOptions).execute().getResult();
    System.out.println(recognitionJob);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const checkJobParams = {
      id: '{id}',
    };
    
    speechToText.checkJob(checkJobParams)
      .then(recognitionJob => {
        console.log(JSON.stringify(recognitionJob, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const checkJobParams = {
      id: {id},
    };
    
    speechToText.checkJob(checkJobParams)
      .then(recognitionJob => {
        console.log(JSON.stringify(recognitionJob, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    recognition_job = speech_to_text.check_job({id}).get_result()
    print(json.dumps(recognition_job, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    recognition_job = speech_to_text.check_job({id}).get_result()
    print(json.dumps(recognition_job, indent=2))

Response

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Information about a current asynchronous speech recognition job.

Status Code

  • OK. The request succeeded.

  • Not Found. The specified job ID was not found.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "created": "2016-08-17T19:11:04.298Z",
      "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
      "updated": "2016-08-17T19:11:16.003Z",
      "results": [
        {
          "result_index": 0,
          "results": [
            {
              "final": true,
              "alternatives": [
                {
                  "transcript": "several tornadoes touch down as a line of severe thunderstorms swept through Colorado on Sunday ",
                  "timestamps": [
                    [
                      "several",
                      1,
                      1.52
                    ],
                    [
                      "tornadoes",
                      1.52,
                      2.15
                    ],
                    [
                      "touch",
                      2.15,
                      2.49
                    ],
                    [
                      "down",
                      2.49,
                      2.82
                    ],
                    [
                      "as",
                      2.82,
                      2.92
                    ],
                    [
                      "a",
                      2.92,
                      3.01
                    ],
                    [
                      "line",
                      3.01,
                      3.3
                    ],
                    [
                      "of",
                      3.3,
                      3.39
                    ],
                    [
                      "severe",
                      3.39,
                      3.77
                    ],
                    [
                      "thunderstorms",
                      3.77,
                      4.51
                    ],
                    [
                      "swept",
                      4.51,
                      4.79
                    ],
                    [
                      "through",
                      4.79,
                      4.95
                    ],
                    [
                      "Colorado",
                      4.95,
                      5.59
                    ],
                    [
                      "on",
                      5.59,
                      5.73
                    ],
                    [
                      "Sunday",
                      5.73,
                      6.35
                    ]
                  ],
                  "confidence": 0.96
                }
              ]
            }
          ]
        }
      ],
      "status": "completed"
    }
  • {
      "created": "2016-08-17T19:11:04.298Z",
      "id": "4bd734c0-e575-21f3-de03-f932aa0468a0",
      "updated": "2016-08-17T19:11:16.003Z",
      "results": [
        {
          "result_index": 0,
          "results": [
            {
              "final": true,
              "alternatives": [
                {
                  "transcript": "several tornadoes touch down as a line of severe thunderstorms swept through Colorado on Sunday ",
                  "timestamps": [
                    [
                      "several",
                      1,
                      1.52
                    ],
                    [
                      "tornadoes",
                      1.52,
                      2.15
                    ],
                    [
                      "touch",
                      2.15,
                      2.49
                    ],
                    [
                      "down",
                      2.49,
                      2.82
                    ],
                    [
                      "as",
                      2.82,
                      2.92
                    ],
                    [
                      "a",
                      2.92,
                      3.01
                    ],
                    [
                      "line",
                      3.01,
                      3.3
                    ],
                    [
                      "of",
                      3.3,
                      3.39
                    ],
                    [
                      "severe",
                      3.39,
                      3.77
                    ],
                    [
                      "thunderstorms",
                      3.77,
                      4.51
                    ],
                    [
                      "swept",
                      4.51,
                      4.79
                    ],
                    [
                      "through",
                      4.79,
                      4.95
                    ],
                    [
                      "Colorado",
                      4.95,
                      5.59
                    ],
                    [
                      "on",
                      5.59,
                      5.73
                    ],
                    [
                      "Sunday",
                      5.73,
                      6.35
                    ]
                  ],
                  "confidence": 0.96
                }
              ]
            }
          ]
        }
      ],
      "status": "completed"
    }

Delete a job

Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it.

See also: Deleting a job.

Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it.

See also: Deleting a job.

Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it.

See also: Deleting a job.

Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it.

See also: Deleting a job.

Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it.

See also: Deleting a job.

DELETE /v1/recognitions/{id}
DeleteJob(string id)
ServiceCall<Void> deleteJob(DeleteJobOptions deleteJobOptions)
deleteJob(params)
delete_job(
        self,
        id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteJobOptions.Builder to create a DeleteJobOptions object that contains the parameter values for the deleteJob method.

Path Parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

The deleteJob options.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

parameters

  • The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/recognitions/{id}"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/recognitions/{id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteJob(
        id: "{id}"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteJob(
        id: "{id}"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteJobOptions deleteJobOptions = new DeleteJobOptions.Builder()
      .id({id})
      .build();
    
    speechToText.deleteJob(deleteJobOptions).execute();
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteJobOptions deleteJobOptions = new DeleteJobOptions.Builder()
      .id({id})
      .build();
    
    speechToText.deleteJob(deleteJobOptions).execute();
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteJobParams = {
      id: '{id}',
    };
    
    speechToText.deleteJob(deleteJobParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteJobParams = {
      id: {id},
    };
    
    speechToText.deleteJob(deleteJobParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_job({id})
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_job({id})

Response

Response type: object

Status Code

  • No Content. The job was successfully deleted.

  • Bad Request. The service cannot delete a job that it is actively processing:

    • Unable to delete the processing job
  • Not Found. The specified job ID was not found.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

No Sample Response

This method does not specify any sample responses.

Create a custom language model

Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also:

POST /v1/customizations
CreateLanguageModel(string name, string baseModelName, string dialect = null, string description = null)
ServiceCall<LanguageModel> createLanguageModel(CreateLanguageModelOptions createLanguageModelOptions)
createLanguageModel(params)
create_language_model(
        self,
        name: str,
        base_model_name: str,
        *,
        dialect: str = None,
        description: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the CreateLanguageModelOptions.Builder to create a CreateLanguageModelOptions object that contains the parameter values for the createLanguageModel method.

Custom Headers

  • The type of the input.

    Allowable values: [application/json]

A CreateLanguageModel object that provides basic information about the new custom language model.

parameters

  • A user-defined name for the new custom language model. Use a localized name that matches the language of the custom model. Use a name that describes the domain of the custom model, such as Medical custom model or Legal custom model. Use a name that is unique among all custom language models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom language model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports language model customization, use the Get a model method and check that the attribute custom_language_model is set to true. You can also refer to Language support for customization.

    Allowable values: [ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_Telephony]

  • The dialect of the specified language that is to be used with the custom language model. For all languages, it is always safe to omit this field. The service automatically uses the language identifier from the name of the base model. For example, the service automatically uses en-US for all US English models.

    If you specify the dialect for a new custom model, follow these guidelines. For non-Spanish previous-generation models and for next-generation models, you must specify a value that matches the five-character language identifier from the name of the base model. For Spanish previous-generation models, you must specify one of the following values:

    • es-ES for Castilian Spanish (es-ES models)
    • es-LA for Latin American Spanish (es-AR, es-CL, es-CO, and es-PE models)
    • es-US for Mexican (North American) Spanish (es-MX models)

    All values that you pass for the dialect field are case-insensitive.

  • A recommended description of the new custom language model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

The createLanguageModel options.

parameters

  • A user-defined name for the new custom language model. Use a localized name that matches the language of the custom model. Use a name that describes the domain of the custom model, such as Medical custom model or Legal custom model. Use a name that is unique among all custom language models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom language model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports language model customization, use the Get a model method and check that the attribute custom_language_model is set to true. You can also refer to Language support for customization.

    Allowable values: [ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_Telephony]

  • The dialect of the specified language that is to be used with the custom language model. For all languages, it is always safe to omit this field. The service automatically uses the language identifier from the name of the base model. For example, the service automatically uses en-US for all US English models.

    If you specify the dialect for a new custom model, follow these guidelines. For non-Spanish previous-generation models and for next-generation models, you must specify a value that matches the five-character language identifier from the name of the base model. For Spanish previous-generation models, you must specify one of the following values:

    • es-ES for Castilian Spanish (es-ES models)
    • es-LA for Latin American Spanish (es-AR, es-CL, es-CO, and es-PE models)
    • es-US for Mexican (North American) Spanish (es-MX models)

    All values that you pass for the dialect field are case-insensitive.

  • A recommended description of the new custom language model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

parameters

  • A user-defined name for the new custom language model. Use a localized name that matches the language of the custom model. Use a name that describes the domain of the custom model, such as Medical custom model or Legal custom model. Use a name that is unique among all custom language models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom language model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports language model customization, use the Get a model method and check that the attribute custom_language_model is set to true. You can also refer to Language support for customization.

    Allowable values: [ar-MS_Telephony,cs-CZ_Telephony,de-DE_BroadbandModel,de-DE_Multimedia,de-DE_NarrowbandModel,de-DE_Telephony,en-AU_BroadbandModel,en-AU_Multimedia,en-AU_NarrowbandModel,en-AU_Telephony,en-GB_BroadbandModel,en-GB_Multimedia,en-GB_NarrowbandModel,en-GB_Telephony,en-IN_Telephony,en-US_BroadbandModel,en-US_Multimedia,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,en-US_Telephony,en-WW_Medical_Telephony,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-ES_Multimedia,es-ES_Telephony,es-LA_Telephony,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_Multimedia,fr-CA_NarrowbandModel,fr-CA_Telephony,fr-FR_BroadbandModel,fr-FR_Multimedia,fr-FR_NarrowbandModel,fr-FR_Telephony,hi-IN_Telephony,it-IT_BroadbandModel,it-IT_NarrowbandModel,it-IT_Multimedia,it-IT_Telephony,ja-JP_BroadbandModel,ja-JP_Multimedia,ja-JP_NarrowbandModel,ja-JP_Telephony,ko-KR_BroadbandModel,ko-KR_Multimedia,ko-KR_NarrowbandModel,ko-KR_Telephony,nl-BE_Telephony,nl-NL_BroadbandModel,nl-NL_Multimedia,nl-NL_NarrowbandModel,nl-NL_Telephony,pt-BR_BroadbandModel,pt-BR_Multimedia,pt-BR_NarrowbandModel,pt-BR_Telephony,sv-SE_Telephony,zh-CN_Telephony]

  • The dialect of the specified language that is to be used with the custom language model. For all languages, it is always safe to omit this field. The service automatically uses the language identifier from the name of the base model. For example, the service automatically uses en-US for all US English models.

    If you specify the dialect for a new custom model, follow these guidelines. For non-Spanish previous-generation models and for next-generation models, you must specify a value that matches the five-character language identifier from the name of the base model. For Spanish previous-generation models, you must specify one of the following values:

    • es-ES for Castilian Spanish (es-ES models)
    • es-LA for Latin American Spanish (es-AR, es-CL, es-CO, and es-PE models)
    • es-US for Mexican (North American) Spanish (es-MX models)

    All values that you pass for the dialect field are case-insensitive.

  • A recommended description of the new custom language model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{\"name\": \"First example language model\", \"base_model_name\": \"en-US_BroadbandModel\", \"description\": \"First example custom language model\"}" "{url}/v1/customizations"
  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{\"name\": \"First example language model\", \"base_model_name\": \"en-US_BroadbandModel\", \"description\": \"First example custom language model\"}" "{url}/v1/customizations"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateLanguageModel(
        name: "First example language model",
        baseModelName: "en-US_BroadbandModel",
        description: "First custom language model example"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateLanguageModel(
        name: "First example language model",
        baseModelName: "en-US_BroadbandModel",
        description: "First custom language model example"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CreateLanguageModelOptions createLanguageModelOptions =
      new CreateLanguageModelOptions.Builder()
        .name("First example language model")
        .baseModelName("en-US_BroadbandModel")
        .description("First custom language model example")
        .build();
    
    LanguageModel languageModel =
      speechToText.createLanguageModel(createLanguageModelOptions).execute().getResult();
    System.out.println(languageModel);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CreateLanguageModelOptions createLanguageModelOptions =
      new CreateLanguageModelOptions.Builder()
        .name("First example language model")
        .baseModelName("en-US_BroadbandModel")
        .description("First custom language model example")
        .build();
    
    LanguageModel languageModel =
      speechToText.createLanguageModel(createLanguageModelOptions).execute().getResult();
    System.out.println(languageModel);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const createLanguageModelParams = {
      name: 'First example language model',
      baseModelName: 'en-US_BroadbandModel',
      description: 'First custom language model example',
    };
    
    speechToText.createLanguageModel(createLanguageModelParams)
      .then(languageModel => {
        console.log(JSON.stringify(languageModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const createLanguageModelParams = {
      name: 'First example language model',
      baseModelName: 'en-US_BroadbandModel',
      description: 'First custom language model example',
    };
    
    speechToText.createLanguageModel(createLanguageModelParams)
      .then(languageModel => {
        console.log(JSON.stringify(languageModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_model = speech_to_text.create_language_model(
        'First example language model',
        'en-US_BroadbandModel',
        description='First custom language model example'
    ).get_result()
    print(json.dumps(language_model, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_model = speech_to_text.create_language_model(
        'First example language model',
        'en-US_BroadbandModel',
        description='First custom language model example'
    ).get_result()
    print(json.dumps(language_model, indent=2))

Response

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Status Code

  • Created. The custom language model was successfully created.

  • Bad Request. A required parameter is null or invalid. Specific failure messages include:

    • Required parameter '{name}' is missing
    • Required parameter '{name}' cannot be empty string
    • Required parameter '{name}' cannot be null
    • The base model '{model_name}' is not recognized
    • Language customization is not supported for base model '{model_name}'
    • Invalid dialect value '{dialect}' specified for language '{language}'
    • You exceeded the maximum '{model_number}' of allowed custom language models. You have '{model_number}' custom language models. Please remove the models you do not need or contact the IBM speech support team to apply for an exception.
  • Unauthorized. The specified credentials are invalid.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96"
    }
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96"
    }

List custom language models

Lists information about all custom language models that are owned by an instance of the service. Use the language parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Lists information about all custom language models that are owned by an instance of the service. Use the language parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Lists information about all custom language models that are owned by an instance of the service. Use the language parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Lists information about all custom language models that are owned by an instance of the service. Use the language parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Lists information about all custom language models that are owned by an instance of the service. Use the language parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

GET /v1/customizations
ListLanguageModels(string language = null)
ServiceCall<LanguageModels> listLanguageModels(ListLanguageModelsOptions listLanguageModelsOptions)
listLanguageModels(params)
list_language_models(
        self,
        *,
        language: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListLanguageModelsOptions.Builder to create a ListLanguageModelsOptions object that contains the parameter values for the listLanguageModels method.

Query Parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

The listLanguageModels options.

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListLanguageModels();
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListLanguageModels();
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    LanguageModels languageModels =
      speechToText.listLanguageModels().execute().getResult();
    System.out.println(languageModels);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    LanguageModels languageModels =
      speechToText.listLanguageModels().execute().getResult()
    System.out.println(languageModels);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.listLanguageModels()
      .then(languageModels => {
        console.log(JSON.stringify(languageModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    speechToText.listLanguageModels()
      .then(languageModels => {
        console.log(JSON.stringify(languageModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_models = speech_to_text.list_language_models().get_result()
    print(json.dumps(language_models, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_models = speech_to_text.list_language_models().get_result()
    print(json.dumps(language_models, indent=2))

Response

Information about existing custom language models.

Information about existing custom language models.

Information about existing custom language models.

Information about existing custom language models.

Information about existing custom language models.

Status Code

  • OK. The request succeeded.

  • Bad Request. A required parameter is null or invalid. Specific failure messages include:

    • Language '{language}' is not supported for customization.
  • Unauthorized. The specified credentials are invalid.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customizations": [
        {
          "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
          "created": "2016-06-01T14:21:26.894Z",
          "updated": "2020-01-18T18:42:25.324Z",
          "language": "en-US",
          "dialect": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2018-07-31",
            "en-US_BroadbandModel.v2020-01-16"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model",
          "description": "Example custom language model",
          "base_model_name": "en-US_BroadbandModel",
          "status": "pending",
          "progress": 0
        },
        {
          "customization_id": "8391f918-3b76-e109-763c-b7732fae4829",
          "created": "2017-12-02T18:51:37.291Z",
          "updated": "2017-12-02T20:02:10.624Z",
          "language": "en-US",
          "dialect": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2017-11-15"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model two",
          "description": "Example custom language model two",
          "base_model_name": "en-US_BroadbandModel",
          "status": "available",
          "progress": 100
        }
      ]
    }
  • {
      "customizations": [
        {
          "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
          "created": "2016-06-01T14:21:26.894Z",
          "updated": "2020-01-18T18:42:25.324Z",
          "language": "en-US",
          "dialect": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2018-07-31",
            "en-US_BroadbandModel.v2020-01-16"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model",
          "description": "Example custom language model",
          "base_model_name": "en-US_BroadbandModel",
          "status": "pending",
          "progress": 0
        },
        {
          "customization_id": "8391f918-3b76-e109-763c-b7732fae4829",
          "created": "2017-12-02T18:51:37.291Z",
          "updated": "2017-12-02T20:02:10.624Z",
          "language": "en-US",
          "dialect": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2017-11-15"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model two",
          "description": "Example custom language model two",
          "base_model_name": "en-US_BroadbandModel",
          "status": "available",
          "progress": 100
        }
      ]
    }

Get a custom language model

Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it.

See also:

GET /v1/customizations/{customization_id}
GetLanguageModel(string customizationId)
ServiceCall<LanguageModel> getLanguageModel(GetLanguageModelOptions getLanguageModelOptions)
getLanguageModel(params)
get_language_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetLanguageModelOptions.Builder to create a GetLanguageModelOptions object that contains the parameter values for the getLanguageModel method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The getLanguageModel options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetLanguageModelOptions getLanguageModelOptions =
      new GetLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    LanguageModel languageModel =
      speechToText.getLanguageModel(getLanguageModelOptions).execute().getResult();
    System.out.println(languageModel);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetLanguageModelOptions getLanguageModelOptions =
      new GetLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    LanguageModel languageModel =
      speechToText.getLanguageModel(getLanguageModelOptions).execute().getResult();
    System.out.println(languageModel);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.getLanguageModel(getLanguageModelParams)
      .then(languageModel => {
        console.log(JSON.stringify(languageModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.getLanguageModel(getLanguageModelParams)
      .then(languageModel => {
        console.log(JSON.stringify(languageModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_model = speech_to_text.get_language_model('{customization_id}').get_result()
    print(json.dumps(language_model, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    language_model = speech_to_text.get_language_model('{customization_id}').get_result()
    print(json.dumps(language_model, indent=2))

Response

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Information about an existing custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
      "created": "2016-06-01T14:21:26.894Z",
      "updated": "2020-01-18T18:42:25.324Z",
      "language": "en-US",
      "dialect": "en-US",
      "versions": [
        "en-US_BroadbandModel.v2018-07-31",
        "en-US_BroadbandModel.v2020-01-16"
      ],
      "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
      "name": "Example model",
      "description": "Example custom language model",
      "base_model_name": "en-US_BroadbandModel",
      "status": "pending",
      "progress": 0
    }
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96",
      "created": "2016-06-01T14:21:26.894Z",
      "updated": "2020-01-18T18:42:25.324Z",
      "language": "en-US",
      "dialect": "en-US",
      "versions": [
        "en-US_BroadbandModel.v2018-07-31",
        "en-US_BroadbandModel.v2020-01-16"
      ],
      "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
      "name": "Example model",
      "description": "Example custom language model",
      "base_model_name": "en-US_BroadbandModel",
      "status": "pending",
      "progress": 0
    }

Delete a custom language model

Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

See also:

Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

See also:

Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

See also:

Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

See also:

Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

See also:

DELETE /v1/customizations/{customization_id}
DeleteLanguageModel(string customizationId)
ServiceCall<Void> deleteLanguageModel(DeleteLanguageModelOptions deleteLanguageModelOptions)
deleteLanguageModel(params)
delete_language_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteLanguageModelOptions.Builder to create a DeleteLanguageModelOptions object that contains the parameter values for the deleteLanguageModel method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The deleteLanguageModel options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteLanguageModelOptions deleteLanguageModelOptions =
      new DeleteLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.deleteLanguageModel(deleteLanguageModelOptions).execute();
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteLanguageModelOptions deleteLanguageModelOptions =
      new DeleteLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.deleteLanguageModel(deleteLanguageModelOptions).execute().getResult();
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.deleteLanguageModel(deleteLanguageModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.deleteLanguageModel(deleteLanguageModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_language_model('{customization_id}')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_language_model('{customization_id}')

Response

Response type: object

Status Code

  • OK. The custom language model was successfully deleted.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials, including the case where the custom model does not exist:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Train a custom language model

Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. If you added custom words directly to a custom model that is based on a next-generation model, allow for some minutes of extra training time for the model.

The method returns a LanguageModel object that includes status and progress fields. A status of available means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes.

For custom models that are based on improved base language models, training also performs an automatic upgrade to a newer version of the base model. You do not need to use the Upgrade a custom language model method to perform the upgrade.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model.
  • No training data have been added to the custom model.
  • The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. If you added custom words directly to a custom model that is based on a next-generation model, allow for some minutes of extra training time for the model.

The method returns a LanguageModel object that includes status and progress fields. A status of available means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes.

For custom models that are based on improved base language models, training also performs an automatic upgrade to a newer version of the base model. You do not need to use the Upgrade a custom language model method to perform the upgrade.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model.
  • No training data have been added to the custom model.
  • The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. If you added custom words directly to a custom model that is based on a next-generation model, allow for some minutes of extra training time for the model.

The method returns a LanguageModel object that includes status and progress fields. A status of available means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes.

For custom models that are based on improved base language models, training also performs an automatic upgrade to a newer version of the base model. You do not need to use the Upgrade a custom language model method to perform the upgrade.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model.
  • No training data have been added to the custom model.
  • The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. If you added custom words directly to a custom model that is based on a next-generation model, allow for some minutes of extra training time for the model.

The method returns a LanguageModel object that includes status and progress fields. A status of available means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes.

For custom models that are based on improved base language models, training also performs an automatic upgrade to a newer version of the base model. You do not need to use the Upgrade a custom language model method to perform the upgrade.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model.
  • No training data have been added to the custom model.
  • The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. If you added custom words directly to a custom model that is based on a next-generation model, allow for some minutes of extra training time for the model.

The method returns a LanguageModel object that includes status and progress fields. A status of available means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes.

For custom models that are based on improved base language models, training also performs an automatic upgrade to a newer version of the base model. You do not need to use the Upgrade a custom language model method to perform the upgrade.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model.
  • No training data have been added to the custom model.
  • The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.
POST /v1/customizations/{customization_id}/train
TrainLanguageModel(string customizationId, string wordTypeToAdd = null, double? customizationWeight = null, bool? strict = null, bool? force = null)
ServiceCall<TrainingResponse> trainLanguageModel(TrainLanguageModelOptions trainLanguageModelOptions)
trainLanguageModel(params)
train_language_model(
        self,
        customization_id: str,
        *,
        word_type_to_add: str = None,
        customization_weight: float = None,
        strict: bool = None,
        force: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the TrainLanguageModelOptions.Builder to create a TrainLanguageModelOptions object that contains the parameter values for the trainLanguageModel method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

Query Parameters

  • For custom models that are based on previous-generation models, the type of words from the custom language model's words resource on which to train the model:

    • all (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user.
    • user trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.

    For custom models that are based on next-generation models, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary.

    Allowable values: [all,user]

    Default: all

  • Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0. The default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request.

    See Using customization weight.

  • If false, allows training of the custom language model to proceed as long as the model contains at least one valid resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom language model fails (status code 400) if the model contains one or more invalid resources (corpus files, grammar files, or custom words).

    Default: true

  • If true, forces the training of the custom language model regardless of whether it contains any changes (is in the ready or available state). By default (false), the model must be in the ready state to be trained. You can use the parameter to train and thus upgrade a custom model that is based on an improved next-generation model. The parameter is available only for IBM Cloud, not for IBM Cloud Pak for Data.

    See Upgrading a custom language model based on an improved next-generation model.

    Default: false

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • For custom models that are based on previous-generation models, the type of words from the custom language model's words resource on which to train the model:

    • all (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user.
    • user trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.

    For custom models that are based on next-generation models, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary.

    Allowable values: [all,user]

    Default: all

  • Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0. The default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request.

    See Using customization weight.

  • If false, allows training of the custom language model to proceed as long as the model contains at least one valid resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom language model fails (status code 400) if the model contains one or more invalid resources (corpus files, grammar files, or custom words).

    Default: true

  • If true, forces the training of the custom language model regardless of whether it contains any changes (is in the ready or available state). By default (false), the model must be in the ready state to be trained. You can use the parameter to train and thus upgrade a custom model that is based on an improved next-generation model. The parameter is available only for IBM Cloud, not for IBM Cloud Pak for Data.

    See Upgrading a custom language model based on an improved next-generation model.

    Default: false

The trainLanguageModel options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • For custom models that are based on previous-generation models, the type of words from the custom language model's words resource on which to train the model:

    • all (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user.
    • user trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.

    For custom models that are based on next-generation models, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary.

    Allowable values: [all,user]

    Default: all

  • Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0. The default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request.

    See Using customization weight.

  • If false, allows training of the custom language model to proceed as long as the model contains at least one valid resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom language model fails (status code 400) if the model contains one or more invalid resources (corpus files, grammar files, or custom words).

    Default: true

  • If true, forces the training of the custom language model regardless of whether it contains any changes (is in the ready or available state). By default (false), the model must be in the ready state to be trained. You can use the parameter to train and thus upgrade a custom model that is based on an improved next-generation model. The parameter is available only for IBM Cloud, not for IBM Cloud Pak for Data.

    See Upgrading a custom language model based on an improved next-generation model.

    Default: false

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • For custom models that are based on previous-generation models, the type of words from the custom language model's words resource on which to train the model:

    • all (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user.
    • user trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.

    For custom models that are based on next-generation models, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary.

    Allowable values: [all,user]

    Default: all

  • Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0. The default value is:

    • 0.3 for previous-generation models
    • 0.2 for most next-generation models
    • 0.1 for next-generation English and Japanese models

    The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases.

    The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request.

    See Using customization weight.

  • If false, allows training of the custom language model to proceed as long as the model contains at least one valid resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom language model fails (status code 400) if the model contains one or more invalid resources (corpus files, grammar files, or custom words).

    Default: true

  • If true, forces the training of the custom language model regardless of whether it contains any changes (is in the ready or available state). By default (false), the model must be in the ready state to be trained. You can use the parameter to train and thus upgrade a custom model that is based on an improved next-generation model. The parameter is available only for IBM Cloud, not for IBM Cloud Pak for Data.

    See Upgrading a custom language model based on an improved next-generation model.

    Default: false

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/train"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/train"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.TrainLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.TrainLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    TrainLanguageModelOptions trainLanguageModelOptions =
      new TrainLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.trainLanguageModel(trainLanguageModelOptions).execute().getResult();
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    TrainLanguageModelOptions trainLanguageModelOptions =
      new TrainLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.trainLanguageModel(trainLanguageModelOptions).execute().getResult();
    // Poll for language model status.
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const trainLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.trainLanguageModel(trainLanguageModelParams)
      .then(result => {
        // Poll for language model status.
       })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const trainLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.trainLanguageModel(trainLanguageModelParams)
      .then(result => {
        // Poll for language model status.
       })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.train_language_model('{customization_id}')
    # Poll for language model status.
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.train_language_model('{customization_id}')
    # Poll for language model status.

Response

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

Status Code

  • OK. Training of the custom language model started successfully.

  • Bad Request. A required parameter is null or invalid, the custom model is not ready to be trained, or the total number of words or OOV words exceeds the maximum threshold. Specific failure messages include:

    • No input data available for running training
    • Fix errors in the following words: [{words}] before training
    • Total number of words {number} exceeds maximum allowed
    • Total number of OOV words {number} exceeds {maximum}
    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Reset a custom language model

Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it.

See also:

Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it.

See also:

Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it.

See also:

Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it.

See also:

Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it.

See also:

POST /v1/customizations/{customization_id}/reset
ResetLanguageModel(string customizationId)
ServiceCall<Void> resetLanguageModel(ResetLanguageModelOptions resetLanguageModelOptions)
resetLanguageModel(params)
reset_language_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ResetLanguageModelOptions.Builder to create a ResetLanguageModelOptions object that contains the parameter values for the resetLanguageModel method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The resetLanguageModel options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/reset"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/reset"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ResetLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ResetLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ResetLanguageModelOptions resetLanguageModelOptions =
      new ResetLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.resetLanguageModel(resetLanguageModelOptions).execute();
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ResetLanguageModelOptions resetLanguageModelOptions =
      new ResetLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.resetLanguageModel(resetLanguageModelOptions).execute();
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const resetLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.resetLanguageModel(resetLanguageModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const resetLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.resetLanguageModel(resetLanguageModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.reset_language_model('{customization_id}')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.reset_language_model('{customization_id}')

Response

Response type: object

Status Code

  • OK. The custom language model was successfully reset.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Upgrade a custom language model

Initiates the upgrade of a custom language model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes to complete depending on the amount of data in the custom model and the current load on the service. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom language model method to poll the model's status. The method returns a LanguageModel object that includes status and progress fields. Use a loop to check the status every 10 seconds.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes.

For custom models that are based on improved base language models, the Train a custom language model method also performs an automatic upgrade to a newer version of the base model. You do not need to use the upgrade method.

See also:

Initiates the upgrade of a custom language model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes to complete depending on the amount of data in the custom model and the current load on the service. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom language model method to poll the model's status. The method returns a LanguageModel object that includes status and progress fields. Use a loop to check the status every 10 seconds.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes.

For custom models that are based on improved base language models, the Train a custom language model method also performs an automatic upgrade to a newer version of the base model. You do not need to use the upgrade method.

See also:

Initiates the upgrade of a custom language model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes to complete depending on the amount of data in the custom model and the current load on the service. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom language model method to poll the model's status. The method returns a LanguageModel object that includes status and progress fields. Use a loop to check the status every 10 seconds.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes.

For custom models that are based on improved base language models, the Train a custom language model method also performs an automatic upgrade to a newer version of the base model. You do not need to use the upgrade method.

See also:

Initiates the upgrade of a custom language model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes to complete depending on the amount of data in the custom model and the current load on the service. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom language model method to poll the model's status. The method returns a LanguageModel object that includes status and progress fields. Use a loop to check the status every 10 seconds.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes.

For custom models that are based on improved base language models, the Train a custom language model method also performs an automatic upgrade to a newer version of the base model. You do not need to use the upgrade method.

See also:

Initiates the upgrade of a custom language model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes to complete depending on the amount of data in the custom model and the current load on the service. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom language model method to poll the model's status. The method returns a LanguageModel object that includes status and progress fields. Use a loop to check the status every 10 seconds.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes.

For custom models that are based on improved base language models, the Train a custom language model method also performs an automatic upgrade to a newer version of the base model. You do not need to use the upgrade method.

See also:

POST /v1/customizations/{customization_id}/upgrade_model
UpgradeLanguageModel(string customizationId)
ServiceCall<Void> upgradeLanguageModel(UpgradeLanguageModelOptions upgradeLanguageModelOptions)
upgradeLanguageModel(params)
upgrade_language_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the UpgradeLanguageModelOptions.Builder to create a UpgradeLanguageModelOptions object that contains the parameter values for the upgradeLanguageModel method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The upgradeLanguageModel options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/upgrade_model"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/upgrade_model"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UpgradeLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UpgradeLanguageModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UpgradeLanguageModelOptions upgradeLanguageModelOptions =
      new UpgradeLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.upgradeLanguageModel(upgradeLanguageModelOptions).execute();
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UpgradeLanguageModelOptions upgradeLanguageModelOptions =
      new UpgradeLanguageModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.upgradeLanguageModel(upgradeLanguageModelOptions).execute();
    // Poll for language model status.
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const upgradeLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.upgradeLanguageModel(upgradeLanguageModelParams)
      .then(result => {
        // Poll for language model status.
       })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const upgradeLanguageModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.upgradeLanguageModel(upgradeLanguageModelParams)
      .then(result => {
        // Poll for language model status.
       })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.upgrade_language_model('{customization_id}')
    # Poll for language model status.
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.upgrade_language_model('{customization_id}')
    # Poll for language model status.

Response

Response type: object

Status Code

  • OK. Upgrade of the custom language model started successfully.

  • Bad Request. The specified customization ID is invalid or the specified custom model cannot be upgraded:

    • Malformed GUID: '{customization_id}'
    • Custom model is up-to-date
    • No input data available to upgrade the model
    • Cannot upgrade failed custom model
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

List corpora

Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

GET /v1/customizations/{customization_id}/corpora
ListCorpora(string customizationId)
ServiceCall<Corpora> listCorpora(ListCorporaOptions listCorporaOptions)
listCorpora(params)
list_corpora(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListCorporaOptions.Builder to create a ListCorporaOptions object that contains the parameter values for the listCorpora method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The listCorpora options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/corpora"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/corpora"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListCorpora(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListCorpora(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListCorporaOptions listCorporaOptions = new ListCorporaOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    Corpora corpora =
      speechToText.listCorpora(listCorporaOptions).execute().getResult();
    System.out.println(corpora);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListCorporaOptions listCorporaOptions = new ListCorporaOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    Corpora corpora =
      speechToText.listCorpora(listCorporaOptions).execute().getResult();
    System.out.println(corpora);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const listCorporaParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listCorpora(listCorporaParams)
      .then(corpora => {
        console.log(JSON.stringify(corpora, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const listCorporaParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listCorpora(listCorporaParams)
      .then(corpora => {
        console.log(JSON.stringify(corpora, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    corpora = speech_to_text.list_corpora('{customization_id}').get_result()
    print(json.dumps(corpora, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    corpora = speech_to_text.list_corpora('{customization_id}').get_result()
    print(json.dumps(corpora, indent=2))

Response

Information about the corpora from a custom language model.

Information about the corpora from a custom language model.

Information about the corpora from a custom language model.

Information about the corpora from a custom language model.

Information about the corpora from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "corpora": [
        {
          "name": "corpus1",
          "out_of_vocabulary_words": 191,
          "total_words": 5037,
          "status": "analyzed"
        },
        {
          "name": "corpus2",
          "out_of_vocabulary_words": 0,
          "total_words": 0,
          "status": "being_processed"
        },
        {
          "name": "corpus3",
          "out_of_vocabulary_words": 0,
          "total_words": 0,
          "status": "undetermined",
          "error": "Analysis of corpus 'corpus3.txt' failed. Please try adding the corpus again by setting the 'allow_overwrite' flag to 'true'."
        }
      ]
    }
  • {
      "corpora": [
        {
          "name": "corpus1",
          "out_of_vocabulary_words": 191,
          "total_words": 5037,
          "status": "analyzed"
        },
        {
          "name": "corpus2",
          "out_of_vocabulary_words": 0,
          "total_words": 0,
          "status": "being_processed"
        },
        {
          "name": "corpus3",
          "out_of_vocabulary_words": 0,
          "total_words": 0,
          "status": "undetermined",
          "error": "Analysis of corpus 'corpus3.txt' failed. Please try adding the corpus again by setting the 'allow_overwrite' flag to 'true'."
        }
      ]
    }

Add a corpus

Adds a single corpus text file of new training data to a custom language model. Use multiple requests to submit multiple corpus text files. You must use credentials for the instance of the service that owns a model to add a corpus to it. Adding a corpus does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, for custom models that are based on previous-generation models, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the Get a corpus method to check the status of the analysis.

For custom models that are based on previous-generation models, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the List custom words method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed.

To add a corpus file that has the same name as an existing corpus, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. For a custom model that is based on a previous-generation model, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the Add custom words or Add a custom word method.

The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. For a custom model that is based on a previous-generation model, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly.

See also:

Adds a single corpus text file of new training data to a custom language model. Use multiple requests to submit multiple corpus text files. You must use credentials for the instance of the service that owns a model to add a corpus to it. Adding a corpus does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, for custom models that are based on previous-generation models, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the Get a corpus method to check the status of the analysis.

For custom models that are based on previous-generation models, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the List custom words method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed.

To add a corpus file that has the same name as an existing corpus, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. For a custom model that is based on a previous-generation model, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the Add custom words or Add a custom word method.

The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. For a custom model that is based on a previous-generation model, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly.

See also:

Adds a single corpus text file of new training data to a custom language model. Use multiple requests to submit multiple corpus text files. You must use credentials for the instance of the service that owns a model to add a corpus to it. Adding a corpus does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, for custom models that are based on previous-generation models, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the Get a corpus method to check the status of the analysis.

For custom models that are based on previous-generation models, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the List custom words method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed.

To add a corpus file that has the same name as an existing corpus, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. For a custom model that is based on a previous-generation model, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the Add custom words or Add a custom word method.

The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. For a custom model that is based on a previous-generation model, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly.

See also:

Adds a single corpus text file of new training data to a custom language model. Use multiple requests to submit multiple corpus text files. You must use credentials for the instance of the service that owns a model to add a corpus to it. Adding a corpus does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, for custom models that are based on previous-generation models, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the Get a corpus method to check the status of the analysis.

For custom models that are based on previous-generation models, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the List custom words method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed.

To add a corpus file that has the same name as an existing corpus, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. For a custom model that is based on a previous-generation model, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the Add custom words or Add a custom word method.

The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. For a custom model that is based on a previous-generation model, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly.

See also:

Adds a single corpus text file of new training data to a custom language model. Use multiple requests to submit multiple corpus text files. You must use credentials for the instance of the service that owns a model to add a corpus to it. Adding a corpus does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy.

The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, for custom models that are based on previous-generation models, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the Get a corpus method to check the status of the analysis.

For custom models that are based on previous-generation models, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the List custom words method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed.

To add a corpus file that has the same name as an existing corpus, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. For a custom model that is based on a previous-generation model, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the Add custom words or Add a custom word method.

The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. For a custom model that is based on a previous-generation model, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly.

See also:

POST /v1/customizations/{customization_id}/corpora/{corpus_name}
AddCorpus(string customizationId, string corpusName, System.IO.MemoryStream corpusFile, bool? allowOverwrite = null)
ServiceCall<Void> addCorpus(AddCorpusOptions addCorpusOptions)
addCorpus(params)
add_corpus(
        self,
        customization_id: str,
        corpus_name: str,
        corpus_file: BinaryIO,
        *,
        allow_overwrite: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the AddCorpusOptions.Builder to create a AddCorpusOptions object that contains the parameter values for the addCorpus method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new corpus for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the corpus.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing corpus or grammar that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.

Query Parameters

  • If true, the specified corpus overwrites an existing corpus with the same name. If false, the request fails if a corpus with the same name already exists. The parameter has no effect if a corpus with the same name does not already exist.

    Default: false

Form Parameters

  • A plain text file that contains the training data for the corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service assumes UTF-8 encoding if it encounters non-ASCII characters.

    Make sure that you know the character encoding of the file. You must use that same encoding when working with the words in the custom language model. For more information, see Character encoding for custom words.

    With the curl command, use the --data-binary option to upload the file for the request.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new corpus for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the corpus.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing corpus or grammar that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the training data for the corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service assumes UTF-8 encoding if it encounters non-ASCII characters.

    Make sure that you know the character encoding of the file. You must use that same encoding when working with the words in the custom language model. For more information, see Character encoding for custom words.

    With the curl command, use the --data-binary option to upload the file for the request.

  • If true, the specified corpus overwrites an existing corpus with the same name. If false, the request fails if a corpus with the same name already exists. The parameter has no effect if a corpus with the same name does not already exist.

    Default: false

The addCorpus options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new corpus for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the corpus.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing corpus or grammar that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the training data for the corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service assumes UTF-8 encoding if it encounters non-ASCII characters.

    Make sure that you know the character encoding of the file. You must use that same encoding when working with the words in the custom language model. For more information, see Character encoding for custom words.

    With the curl command, use the --data-binary option to upload the file for the request.

  • If true, the specified corpus overwrites an existing corpus with the same name. If false, the request fails if a corpus with the same name already exists. The parameter has no effect if a corpus with the same name does not already exist.

    Default: false

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new corpus for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the corpus.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing corpus or grammar that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the training data for the corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service assumes UTF-8 encoding if it encounters non-ASCII characters.

    Make sure that you know the character encoding of the file. You must use that same encoding when working with the words in the custom language model. For more information, see Character encoding for custom words.

    With the curl command, use the --data-binary option to upload the file for the request.

  • If true, the specified corpus overwrites an existing corpus with the same name. If false, the request fails if a corpus with the same name already exists. The parameter has no effect if a corpus with the same name does not already exist.

    Default: false

  • curl -X POST -u "apikey:{apikey}" --data-binary @corpus1.txt "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • curl -X POST --header "Authorization: Bearer {token}" --data-binary @corpus1.txt "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    DetailedResponse<object> result = null;
    using (FileStream fs = File.OpenRead("corpus1.txt"))
    {
        using (MemoryStream ms = new MemoryStream())
        {
            fs.CopyTo(ms);
            result = speechToText.AddCorpus(
                customizationId: "{customizationId}",
                corpusFile: ms,
                corpusName: "corpus1"
                );
        }
    }
    
    Console.WriteLine(result.Response);
    // Poll for corpus status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    DetailedResponse<object> result = null;
    using (FileStream fs = File.OpenRead("corpus1.txt"))
    {
        using (MemoryStream ms = new MemoryStream())
        {
            fs.CopyTo(ms);
            result = speechToText.AddCorpus(
                customizationId: "{customizationId}",
                corpusFile: ms,
                corpusName: "corpus1"
                );
        }
    }
    
    Console.WriteLine(result.Response);
    // Poll for corpus status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddCorpusOptions addCorpusOptions = new AddCorpusOptions.Builder()
        .customizationId("{customizationId}")
        .corpusFile(new File("corpus1.txt"))
        .corpusName("corpus1")
        .build();
    
      speechToText.addCorpus(addCorpusOptions).execute();
      // Poll for corpus status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddCorpusOptions addCorpusOptions = new AddCorpusOptions.Builder()
        .customizationId("{customizationId}")
        .corpusFile(new File("corpus1.txt"))
        .corpusName("corpus1")
        .build();
    
      speechToText.addCorpus(addCorpusOptions).execute();
      // Poll for corpus status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }
  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const addCorpusParams = {
      customizationId: '{customization_id}',
      corpusFile: fs.createReadStream('./corpus1.txt'),
      corpusName: 'corpus1',
    };
    
    speechToText.addCorpus(addCorpusParams)
      .then(result => {
        // Poll for corpus status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const addCorpusParams = {
      customizationId: '{customization_id}',
      corpusFile: fs.createReadStream('corpus1.txt'),
      corpusName: 'corpus1',
    };
    
    speechToText.addCorpus(addCorpusParams)
      .then(result => {
        // Poll for corpus status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'corpus1.txt'),
                   'rb') as corpus_file:
        speech_to_text.add_corpus(
            '{customization_id}',
            'corpus1',
            corpus_file
        )
    # Poll for corpus status.
  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'corpus1.txt'),
                   'rb') as corpus_file:
        speech_to_text.add_corpus(
            '{customization_id}',
            'corpus1',
            corpus_file
        )
    # Poll for corpus status.

Response

Response type: object

Status Code

  • Created. Addition of the corpus data was successfully started. The service is analyzing the data.

  • Bad Request. A required parameter is null or invalid, the specified corpus name already exists, or the custom model needs to be upgraded, among other possibilities. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Corpus file not specified or empty
    • Corpus '{corpus_name}' already exists - change its name, remove existing file before adding new one, or overwrite existing file by setting 'allow_overwrite' to 'true'
    • Grammar exists with corpus name '{corpus_name}'. Please use different name.
    • TOTAL_NUMBER_OF_OOV_WORDS_EXCEEDS_MAXIMUM_ALLOWED_FORMAT: "Total number of OOV words {total_number} exceeds {maximum_allowed}"
    • Analysis of corpus '{corpus_name}' failed due to {error_message}. Please fix the error then add the corpus again by setting the 'allow_overwrite' flag to 'true'., where {error_message} is a message of the form {"code": 404, "error": "Model en-US_BroadbandModel (version: en-US_BroadbandModel.{version}) not found", "code_description": "Not Found"}. Upgrade the custom language model to the latest version of its base language model, and then add the corpus to the custom model.
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. The corpus name includes characters that need to be URL-encoded.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Unsupported Media Type. The request specified an unacceptable media type.

  • Internal Server Error. An internal error prevented the service from satisfying the request. You can also receive status code 500 Forwarding Error if the service is currently busy handling a previous request for the custom model.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Get a corpus

Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. For custom models that are based on previous-generation models, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora.

See also: Listing corpora for a custom language model.

GET /v1/customizations/{customization_id}/corpora/{corpus_name}
GetCorpus(string customizationId, string corpusName)
ServiceCall<Corpus> getCorpus(GetCorpusOptions getCorpusOptions)
getCorpus(params)
get_corpus(
        self,
        customization_id: str,
        corpus_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetCorpusOptions.Builder to create a GetCorpusOptions object that contains the parameter values for the getCorpus method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

The getCorpus options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetCorpus(
        customizationId: "{customizationId}",
        corpusName: "corpus1"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetCorpus(
        customizationId: "{customizationId}",
        corpusName: "corpus1"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetCorpusOptions getCorpusOptions = new GetCorpusOptions.Builder()
      .customizationId("{customizationId}")
      .corpusName("corpus1")
      .build();
    
    Corpus corpus =
      speechToText.getCorpus(getCorpusOptions).execute().getResult();
    System.out.println(corpus);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetCorpusOptions getCorpusOptions = new GetCorpusOptions.Builder()
      .customizationId("{customizationId}")
      .corpusName("corpus1")
      .build();
    
    Corpus corpus =
      speechToText.getCorpus(getCorpusOptions).execute().getResult();
    System.out.println(corpus);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getCorpusParams = {
      customizationId: '{customization_id}',
      corpusName: 'corpus1',
    };
    
    speechToText.getCorpus(getCorpusParams)
      .then(corpus => {
        console.log(JSON.stringify(corpus, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getCorpusParams = {
      customizationId: '{customization_id}',
      corpusName: 'corpus1',
    };
    
    speechToText.getCorpus(getCorpusParams)
      .then(corpus => {
        console.log(JSON.stringify(corpus, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    corpus = speech_to_text.get_corpus(
        '{customization_id}',
        'corpus1'
    ).get_result()
    print(json.dumps(corpus, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    corpus = speech_to_text.get_corpus(
        '{customization_id}',
        'corpus1'
    ).get_result()
    print(json.dumps(corpus, indent=2))

Response

Information about a corpus from a custom language model.

Information about a corpus from a custom language model.

Information about a corpus from a custom language model.

Information about a corpus from a custom language model.

Information about a corpus from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID or corpus name is invalid, including the case where the corpus does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for corpus name '{corpus_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "name": "corpus1",
      "out_of_vocabulary_words": 191,
      "total_words": 5037,
      "status": "analyzed"
    }
  • {
      "name": "corpus1",
      "out_of_vocabulary_words": 191,
      "total_words": 5037,
      "status": "analyzed"
    }

Delete a corpus

Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its corpora.

For custom models that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the Add custom words or Add a custom word method.

See also: Deleting a corpus from a custom language model.

Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its corpora.

For custom models that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the Add custom words or Add a custom word method.

See also: Deleting a corpus from a custom language model.

Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its corpora.

For custom models that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the Add custom words or Add a custom word method.

See also: Deleting a corpus from a custom language model.

Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its corpora.

For custom models that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the Add custom words or Add a custom word method.

See also: Deleting a corpus from a custom language model.

Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its corpora.

For custom models that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the Add custom words or Add a custom word method.

See also: Deleting a corpus from a custom language model.

DELETE /v1/customizations/{customization_id}/corpora/{corpus_name}
DeleteCorpus(string customizationId, string corpusName)
ServiceCall<Void> deleteCorpus(DeleteCorpusOptions deleteCorpusOptions)
deleteCorpus(params)
delete_corpus(
        self,
        customization_id: str,
        corpus_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteCorpusOptions.Builder to create a DeleteCorpusOptions object that contains the parameter values for the deleteCorpus method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

The deleteCorpus options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the corpus for the custom language model.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteCorpus(
        customizationId: "{customizationId}",
        corpusName: "corpus1"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteCorpus(
        customizationId: "{customizationId}",
        corpusName: "corpus1"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteCorpusOptions deleteCorpusOptions = new DeleteCorpusOptions.Builder()
      .customizationId("{customizationId}")
      .corpusName("corpus1")
      .build();
    
    speechToText.deleteCorpus(deleteCorpusOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteCorpusOptions deleteCorpusOptions = new DeleteCorpusOptions.Builder()
      .customizationId("{customizationId}")
      .corpusName("corpus1")
      .build();
    
    speechToText.deleteCorpus(deleteCorpusOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteCorpusParams = {
      customizationId: '{customization_id}',
      corpusName: 'corpus1',
    };
    
    speechToText.deleteCorpus(deleteCorpusParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteCorpusParams = {
      customizationId: '{customization_id}',
      corpusName: 'corpus1',
    };
    
    speechToText.deleteCorpus(deleteCorpusParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_corpus(
        '{customization_id}',
        'corpus1'
    )
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_corpus(
        '{customization_id}',
        'corpus1'
    )

Response

Response type: object

Status Code

  • OK. The corpus was successfully deleted from the custom language model.

  • Bad Request. The specified customization ID or corpus name is invalid, including the case where the corpus does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for corpus name '{corpus_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. No corpus name was specified with the request.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

List custom words

Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, for a custom model that is based on a previous-generation model, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. For a custom model that is based on a next-generation model, you can list all words or only those words that were added directly by a user, which return the same results.

You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, for a custom model that is based on a previous-generation model, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. For a custom model that is based on a next-generation model, you can list all words or only those words that were added directly by a user, which return the same results.

You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, for a custom model that is based on a previous-generation model, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. For a custom model that is based on a next-generation model, you can list all words or only those words that were added directly by a user, which return the same results.

You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, for a custom model that is based on a previous-generation model, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. For a custom model that is based on a next-generation model, you can list all words or only those words that were added directly by a user, which return the same results.

You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, for a custom model that is based on a previous-generation model, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. For a custom model that is based on a next-generation model, you can list all words or only those words that were added directly by a user, which return the same results.

You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

GET /v1/customizations/{customization_id}/words
ListWords(string customizationId, string wordType = null, string sort = null)
ServiceCall<Words> listWords(ListWordsOptions listWordsOptions)
listWords(params)
list_words(
        self,
        customization_id: str,
        *,
        word_type: str = None,
        sort: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListWordsOptions.Builder to create a ListWordsOptions object that contains the parameter values for the listWords method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

Query Parameters

  • The type of words to be listed from the custom language model's words resource:

    • all (the default) shows all words.
    • user shows only custom words that were added or modified by the user directly.
    • corpora shows only OOV that were extracted from corpora.
    • grammars shows only OOV words that are recognized by grammars.

    For a custom model that is based on a next-generation model, only all and user apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models.

    Allowable values: [all,user,corpora,grammars]

    Default: all

  • Indicates the order in which the words are to be listed, alphabetical or by count. You can prepend an optional + or - to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With the curl command, URL-encode the + symbol as %2B.

    Allowable values: [alphabetical,count]

    Default: alphabetical

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The type of words to be listed from the custom language model's words resource:

    • all (the default) shows all words.
    • user shows only custom words that were added or modified by the user directly.
    • corpora shows only OOV that were extracted from corpora.
    • grammars shows only OOV words that are recognized by grammars.

    For a custom model that is based on a next-generation model, only all and user apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models.

    Allowable values: [all,user,corpora,grammars]

    Default: all

  • Indicates the order in which the words are to be listed, alphabetical or by count. You can prepend an optional + or - to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With the curl command, URL-encode the + symbol as %2B.

    Allowable values: [alphabetical,count]

    Default: alphabetical

The listWords options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The type of words to be listed from the custom language model's words resource:

    • all (the default) shows all words.
    • user shows only custom words that were added or modified by the user directly.
    • corpora shows only OOV that were extracted from corpora.
    • grammars shows only OOV words that are recognized by grammars.

    For a custom model that is based on a next-generation model, only all and user apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models.

    Allowable values: [all,user,corpora,grammars]

    Default: all

  • Indicates the order in which the words are to be listed, alphabetical or by count. You can prepend an optional + or - to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With the curl command, URL-encode the + symbol as %2B.

    Allowable values: [alphabetical,count]

    Default: alphabetical

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The type of words to be listed from the custom language model's words resource:

    • all (the default) shows all words.
    • user shows only custom words that were added or modified by the user directly.
    • corpora shows only OOV that were extracted from corpora.
    • grammars shows only OOV words that are recognized by grammars.

    For a custom model that is based on a next-generation model, only all and user apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models.

    Allowable values: [all,user,corpora,grammars]

    Default: all

  • Indicates the order in which the words are to be listed, alphabetical or by count. You can prepend an optional + or - to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With the curl command, URL-encode the + symbol as %2B.

    Allowable values: [alphabetical,count]

    Default: alphabetical

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/words?sort=%2Balphabetical"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/words?sort=%2Balphabetical"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListWords(
        customizationId: "{customizationId}",
        sort: "+alphabetical"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListWords(
        customizationId: "{customizationId}",
        sort: "+alphabetical"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListWordsOptions listWordsOptions = new ListWordsOptions.Builder()
      .customizationId("{customizationId}")
      .sort("+alphabetical")
      .build();
    
    Words words = speechToText.listWords(listWordsOptions).execute().getResult();
    System.out.println(words);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListWordsOptions listWordsOptions = new ListWordsOptions.Builder()
      .customizationId("{customizationId}")
      .sort("+alphabetical")
      .build();
    
    Words words = speechToText.listWords(listWordsOptions).execute().getResult();
    System.out.println(words);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const listWordsParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listWords(listWordsParams)
      .then(words => {
        console.log(JSON.stringify(words, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const listWordsParams = {
      customizationId: '{customization_id}',
      sort: '+alphabetical',
    };
    
    speechToText.listWords(listWordsParams)
      .then(words => {
        console.log(JSON.stringify(words, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    words = speech_to_text.list_words('{customization_id}').get_result()
    print(json.dumps(words, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    words = speech_to_text.list_words('{customization_id}').get_result()
    print(json.dumps(words, indent=2))

Response

Information about the words from a custom language model.

Information about the words from a custom language model.

Information about the words from a custom language model.

Information about the words from a custom language model.

Information about the words from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "words": [
        {
          "word": "75.00",
          "sounds_like": [
            "75 dollars"
          ],
          "display_as": "75.00",
          "count": 1,
          "source": [
            "user"
          ],
          "error": [
            {
              "75 dollars": "Numbers are not allowed in sounds_like. You can try for example 'seventy five dollars'."
            }
          ]
        },
        {
          "word": "HHonors",
          "sounds_like": [
            "hilton honors",
            "H. honors"
          ],
          "display_as": "HHonors",
          "count": 1,
          "source": [
            "corpus1",
            "user"
          ]
        },
        {
          "words": [
            {
              "display_as": "HHonors",
              "sounds_like": [
                "H. honors",
                "hilton honors"
              ],
              "mapping_only": true,
              "count": 1,
              "source": [
                "user"
              ],
              "word": "HHonors"
            },
            {
              "display_as": "IEEE",
              "sounds_like": [
                "I. triple E."
              ],
              "count": 1,
              "source": [
                "user"
              ],
              "word": "IEEE"
            }
          ]
        },
        {
          "word": "IEEE",
          "sounds_like": [
            "I. triple E."
          ],
          "display_as": "IEEE",
          "count": 3,
          "source": [
            "corpus1",
            "corpus2",
            "user"
          ]
        },
        {
          "word": "NCAA",
          "sounds_like": [
            "N. C. A. A.",
            "N. C. double A."
          ],
          "display_as": "NCAA",
          "count": 3,
          "source": [
            "corpus3",
            "user"
          ]
        },
        {
          "word": "tomato",
          "sounds_like": [
            "tomatoh",
            "tomayto"
          ],
          "display_as": "tomato",
          "count": 1,
          "source": [
            "user"
          ]
        }
      ]
    }
  • {
      "words": [
        {
          "word": "75.00",
          "sounds_like": [
            "75 dollars"
          ],
          "display_as": "75.00",
          "count": 1,
          "source": [
            "user"
          ],
          "error": [
            {
              "75 dollars": "Numbers are not allowed in sounds_like. You can try for example 'seventy five dollars'."
            }
          ]
        },
        {
          "word": "HHonors",
          "sounds_like": [
            "hilton honors",
            "H. honors"
          ],
          "display_as": "HHonors",
          "count": 1,
          "source": [
            "corpus1",
            "user"
          ]
        },
        {
          "words": [
            {
              "display_as": "HHonors",
              "sounds_like": [
                "H. honors",
                "hilton honors"
              ],
              "mapping_only": true,
              "count": 1,
              "source": [
                "user"
              ],
              "word": "HHonors"
            },
            {
              "display_as": "IEEE",
              "sounds_like": [
                "I. triple E."
              ],
              "count": 1,
              "source": [
                "user"
              ],
              "word": "IEEE"
            }
          ]
        },
        {
          "word": "IEEE",
          "sounds_like": [
            "I. triple E."
          ],
          "display_as": "IEEE",
          "count": 3,
          "source": [
            "corpus1",
            "corpus2",
            "user"
          ]
        },
        {
          "word": "NCAA",
          "sounds_like": [
            "N. C. A. A.",
            "N. C. double A."
          ],
          "display_as": "NCAA",
          "count": 3,
          "source": [
            "corpus3",
            "user"
          ]
        },
        {
          "word": "tomato",
          "sounds_like": [
            "tomatoh",
            "tomayto"
          ],
          "display_as": "tomato",
          "count": 1,
          "source": [
            "user"
          ]
        }
      ]
    }

Add custom words

Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation model, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

You add custom words by providing a CustomWords object, which is an array of CustomWord objects, one per word. Use the object's word parameter to identify the word that is to be added. You can also provide one or both of the optional display_as or sounds_like fields for each word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like I triple E. You can specify a maximum of five sounds-like pronunciations for a word. For a custom model that is based on a previous-generation model, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.
  • The mapping_only field provides parameter for custom words. You can use the 'mapping_only' key in custom words as a form of post processing. This key parameter has a boolean value to determine whether 'sounds_like' (for non-Japanese models) or word (for Japanese) is not used for the model fine-tuning, but for the replacement for 'display_as'. This feature helps you when you use custom words exclusively to map 'sounds_like' (or word) to 'display_as' value. When you use custom words solely for post-processing purposes that does not need fine-tuning.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource.

The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar.

You can monitor the status of the request by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a Customization object that includes a status field. A status of ready means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes.

You can use the List custom words or Get a custom word method to review the words that you add. Words with an invalid sounds_like field include an error field that describes the problem. You can use other words-related methods to correct errors, eliminate typos, and modify how words are pronounced as needed.

See also:

Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation model, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

You add custom words by providing a CustomWords object, which is an array of CustomWord objects, one per word. Use the object's word parameter to identify the word that is to be added. You can also provide one or both of the optional display_as or sounds_like fields for each word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like I triple E. You can specify a maximum of five sounds-like pronunciations for a word. For a custom model that is based on a previous-generation model, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource.

The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar.

You can monitor the status of the request by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a Customization object that includes a status field. A status of ready means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes.

You can use the List custom words or Get a custom word method to review the words that you add. Words with an invalid sounds_like field include an error field that describes the problem. You can use other words-related methods to correct errors, eliminate typos, and modify how words are pronounced as needed.

See also:

Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation model, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

You add custom words by providing a CustomWords object, which is an array of CustomWord objects, one per word. Use the object's word parameter to identify the word that is to be added. You can also provide one or both of the optional display_as or sounds_like fields for each word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like I triple E. You can specify a maximum of five sounds-like pronunciations for a word. For a custom model that is based on a previous-generation model, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource.

The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar.

You can monitor the status of the request by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a Customization object that includes a status field. A status of ready means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes.

You can use the List custom words or Get a custom word method to review the words that you add. Words with an invalid sounds_like field include an error field that describes the problem. You can use other words-related methods to correct errors, eliminate typos, and modify how words are pronounced as needed.

See also:

Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation model, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

You add custom words by providing a CustomWords object, which is an array of CustomWord objects, one per word. Use the object's word parameter to identify the word that is to be added. You can also provide one or both of the optional display_as or sounds_like fields for each word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like I triple E. You can specify a maximum of five sounds-like pronunciations for a word. For a custom model that is based on a previous-generation model, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource.

The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar.

You can monitor the status of the request by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a Customization object that includes a status field. A status of ready means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes.

You can use the List custom words or Get a custom word method to review the words that you add. Words with an invalid sounds_like field include an error field that describes the problem. You can use other words-related methods to correct errors, eliminate typos, and modify how words are pronounced as needed.

See also:

Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation model, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

You add custom words by providing a CustomWords object, which is an array of CustomWord objects, one per word. Use the object's word parameter to identify the word that is to be added. You can also provide one or both of the optional display_as or sounds_like fields for each word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like I triple E. You can specify a maximum of five sounds-like pronunciations for a word. For a custom model that is based on a previous-generation model, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource.

The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar.

You can monitor the status of the request by using the Get a custom language model method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a Customization object that includes a status field. A status of ready means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes.

You can use the List custom words or Get a custom word method to review the words that you add. Words with an invalid sounds_like field include an error field that describes the problem. You can use other words-related methods to correct errors, eliminate typos, and modify how words are pronounced as needed.

See also:

POST /v1/customizations/{customization_id}/words
AddWords(string customizationId, List<CustomWord> words)
ServiceCall<Void> addWords(AddWordsOptions addWordsOptions)
addWords(params)
add_words(
        self,
        customization_id: str,
        words: List['CustomWord'],
        **kwargs,
    ) -> DetailedResponse

Request

Use the AddWordsOptions.Builder to create a AddWordsOptions object that contains the parameter values for the addWords method.

Custom Headers

  • The type of the input.

    Allowable values: [application/json]

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

A CustomWords object that provides information about one or more custom words that are to be added to or updated in the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • An array of CustomWord objects that provides information about each custom word that is to be added to or updated in the custom language model.

The addWords options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • An array of CustomWord objects that provides information about each custom word that is to be added to or updated in the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • An array of CustomWord objects that provides information about each custom word that is to be added to or updated in the custom language model.

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{\"words\": [ {\"word\": \"HHonors\", \"sounds_like\": [ \"hilton honors\", \"H. honors\"], \"display_as\": \"HHonors\"}, {\"word\": \"IEEE\", \"sounds_like\": [\"I. triple E.\"]} ]}" "{url}/v1/customizations/{customization_id}/words"
    curl -X POST -u "apikey:$apikey" --header "Content-Type: application/json" --data "{\"words\": [ {\"word\": \"HHonors\", \"sounds_like\": [ \"hilton honors\", \"H. honors\"], \"mapping_only:'true'\": \"HHonors\"}, {\"word\": \"IEEE\", \"sounds_like\": [\"I. triple E.\"]} ]}" "{url}/v1/customizations/{customization_id}/words"
  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{\"words\": [ {\"word\": \"HHonors\", \"sounds_like\": [\"hilton honors\", \"H. honors\"], \"display_as\": \"HHonors\"}, {\"word\": \"IEEE\", \"sounds_like\": [\"I. triple E.\"]} ]}" "{url}/v1/customizations/{customization_id}/words"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var customWords = new List<CustomWord>()
    {
        new CustomWord()
        {
            DisplayAs = "HHonors",
            SoundsLike = new List<string>()
            {
                "hilton honors",
                "H. honors"
            },
            Word = "HHonors"
        },
        new CustomWord()
        {
            SoundsLike = new List<string>()
            {
                "I. tripe E."
            },
            Word = "IEEE"
        }
    };
    
    var result = speechToText.AddWords(
        customizationId: "{customizationId}",
        words: customWords
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var customWords = new List<CustomWord>()
    {
        new CustomWord()
        {
            DisplayAs = "HHonors",
            SoundsLike = new List<string>()
            {
                "hilton honors",
                "H. honors"
            },
            Word = "HHonors"
        },
        new CustomWord()
        {
            SoundsLike = new List<string>()
            {
                "I. tripe E."
            },
            Word = "IEEE"
        }
    };
    
    var result = speechToText.AddWords(
        customizationId: "{customizationId}",
        words: customWords
        );
    
    Console.WriteLine(result.Response);
    // Poll for language model status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    List<CustomWord> customWords = new ArrayList<>();
    
    CustomWord HHonors = new CustomWord();
    HHonors.setWord("HHonors");
    HHonors.setSoundsLike(Arrays.asList("hilton honors", "H. honors"));
    HHonors.setDisplayAs("HHonors");
    customWords.add(HHonors);
    
    CustomWord IEEE = new CustomWord();
    IEEE.setWord("IEEE");
    IEEE.setSoundsLike(Arrays.asList("I. tripe E."));
    customWords.add(IEEE);
    
    AddWordsOptions addWordsOptions = new AddWordsOptions.Builder()
      .customizationId("{customizationId}")
      .words(customWords)
      .build();
    
    speechToText.addWords(addWordsOptions).execute();
    // Poll for language model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    List<CustomWord> customWords = new ArrayList<>();
    
    CustomWord HHonors = new CustomWord();
    HHonors.setWord("HHonors");
    HHonors.setSoundsLike(Arrays.asList("hilton honors", "H. honors"));
    HHonors.setDisplayAs("HHonors");
    customWords.add(HHonors);
    
    CustomWord IEEE = new CustomWord();
    IEEE.setWord("IEEE");
    IEEE.setSoundsLike(Arrays.asList("I. tripe E."));
    customWords.add(IEEE);
    
    AddWordsOptions addWordsOptions = new AddWordsOptions.Builder()
      .customizationId("{customizationId}")
      .words(customWords)
      .build();
    
    speechToText.addWords(addWordsOptions).execute();
    // Poll for language model status.
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const addWordsParams = {
      customizationId: '{customization_id}',
      contentType: 'application/json',
      words: [
        {word: 'HHonors', sounds_like: ['hilton honors', 'H. honors'], display_as: 'HHonors'},
        {word: 'IEEE', sounds_like: ['I. triple E.']},
      ],
    };
    
    speechToText.addWords(addWordsParams)
      .then(result => {
        // Poll for language model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const addWordsParams = {
      customizationId: '{customization_id}',
      contentType: 'application/json',
      words: [
        {word: 'HHonors', sounds_like: ['hilton honors', 'H. honors'], display_as: 'HHonors'},
        {word: 'IEEE', sounds_like: ['I. triple E.']},
      ],
    };
    
    speechToText.addWords(addWordsParams)
      .then(result => {
        // Poll for language model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_watson.speech_to_text_v1 import CustomWord
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    HHonors = CustomWord(
        'HHonors',
        ['hilton honors', 'H. honors'],
        'HHonors'
    )
    IEEE = CustomWord(
        'IEEE',
        ['I. triple E.']
    )
    
    speech_to_text.add_words(
        '{customization_id}',
        [HHonors, IEEE]
    )
    # Poll for language model status.
  • from ibm_watson import SpeechToTextV1
    from ibm_watson.speech_to_text_v1 import CustomWord
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    HHonors = CustomWord(
        'HHonors',
        ['hilton honors', 'H. honors'],
        'HHonors'
    )
    IEEE = CustomWord(
        'IEEE',
        ['I. triple E.']
    )
    
    speech_to_text.add_words(
        '{customization_id}',
        [HHonors, IEEE]
    )
    # Poll for language model status.

Response

Response type: object

Status Code

  • Created. Addition of the custom words was successfully started. The service is analyzing the data.

  • Bad Request. A required parameter is null or invalid, the JSON input is invalid, or the maximum number of sounds-like pronunciations for a word is exceeded. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Required property '{property}' is missing in JSON '{JSON}'
    • Word '{word}' contains invalid character character
    • Maximum number of sounds-like for a word exceeded
    • Maximum number of allowed phones of one item of sounds_like for word '{word}' exceeded
    • Malformed JSON: '{JSON}'
    • Wrong type of parameter '{parameter}' detected in the passed JSON
    • TOTAL_NUMBER_OF_OOV_WORDS_EXCEEDS_MAXIMUM_ALLOWED_FORMAT: "Total number of OOV words {total_number} exceeds {maximum_allowed}"
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Add a custom word

Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation models, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

Use the word_name parameter to specify the custom word that is to be added or modified. Use the CustomWord object to provide one or both of the optional display_as or sounds_like fields for the word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like i triple e. You can specify a maximum of five sounds-like pronunciations for a word. For custom models that are based on previous-generation models, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the Get a custom word method to review the word that you add.

See also:

Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation models, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

Use the word_name parameter to specify the custom word that is to be added or modified. Use the CustomWord object to provide one or both of the optional display_as or sounds_like fields for the word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like i triple e. You can specify a maximum of five sounds-like pronunciations for a word. For custom models that are based on previous-generation models, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the Get a custom word method to review the word that you add.

See also:

Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation models, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

Use the word_name parameter to specify the custom word that is to be added or modified. Use the CustomWord object to provide one or both of the optional display_as or sounds_like fields for the word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like i triple e. You can specify a maximum of five sounds-like pronunciations for a word. For custom models that are based on previous-generation models, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the Get a custom word method to review the word that you add.

See also:

Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation models, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

Use the word_name parameter to specify the custom word that is to be added or modified. Use the CustomWord object to provide one or both of the optional display_as or sounds_like fields for the word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like i triple e. You can specify a maximum of five sounds-like pronunciations for a word. For custom models that are based on previous-generation models, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the Get a custom word method to review the word that you add.

See also:

Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. For custom models that are based on previous-generation models, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource.

For a custom model that is based on a previous-generation models, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly.

You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the Train a custom language model method.

Use the word_name parameter to specify the custom word that is to be added or modified. Use the CustomWord object to provide one or both of the optional display_as or sounds_like fields for the word.

  • The display_as field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word IBM is to be displayed as IBM&trade;.
  • The sounds_like field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word IEEE can sound like i triple e. You can specify a maximum of five sounds-like pronunciations for a word. For custom models that are based on previous-generation models, if you omit the sounds_like field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid.

If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the Get a custom word method to review the word that you add.

See also:

PUT /v1/customizations/{customization_id}/words/{word_name}
AddWord(string customizationId, string wordName, string word = null, List<string> soundsLike = null, string displayAs = null)
ServiceCall<Void> addWord(AddWordOptions addWordOptions)
addWord(params)
add_word(
        self,
        customization_id: str,
        word_name: str,
        *,
        word: str = None,
        sounds_like: List[str] = None,
        display_as: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the AddWordOptions.Builder to create a AddWordOptions object that contains the parameter values for the addWord method.

Custom Headers

  • The type of the input.

    Allowable values: [application/json]

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be added to or updated in the custom language model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

A CustomWord object that provides information about the specified custom word. Specify an empty object to add a word with no sounds-like or display-as information.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be added to or updated in the custom language model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

  • For the Add custom words method, you must specify the custom word that is to be added to or updated in the custom model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. A Japanese custom word can include at most 25 characters, not including leading or trailing spaces.

    Omit this parameter for the Add a custom word method.

  • As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users.

    • For custom models that are based on previous-generation models, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word.
    • For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary.

    A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters, not including leading or trailing spaces. A Japanese pronunciation can include at most 25 characters, not including leading or trailing spaces.

  • An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.

    For custom models that are based on next-generation models, the service uses the spelling of the word as the display-as value if you omit the field.

The addWord options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be added to or updated in the custom language model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

  • For the Add custom words method, you must specify the custom word that is to be added to or updated in the custom model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. A Japanese custom word can include at most 25 characters, not including leading or trailing spaces.

    Omit this parameter for the Add a custom word method.

  • As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users.

    • For custom models that are based on previous-generation models, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word.
    • For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary.

    A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters, not including leading or trailing spaces. A Japanese pronunciation can include at most 25 characters, not including leading or trailing spaces.

  • An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.

    For custom models that are based on next-generation models, the service uses the spelling of the word as the display-as value if you omit the field.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be added to or updated in the custom language model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

  • For the Add custom words method, you must specify the custom word that is to be added to or updated in the custom model. Do not use characters that need to be URL-encoded, for example, spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, or question marks. Use a - (dash) or _ (underscore) to connect the tokens of compound words. A Japanese custom word can include at most 25 characters, not including leading or trailing spaces.

    Omit this parameter for the Add a custom word method.

  • As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users.

    • For custom models that are based on previous-generation models, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word.
    • For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary.

    A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters, not including leading or trailing spaces. A Japanese pronunciation can include at most 25 characters, not including leading or trailing spaces.

  • An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.

    For custom models that are based on next-generation models, the service uses the spelling of the word as the display-as value if you omit the field.

  • curl -X PUT -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{\"sounds_like\": [\"N. C. A. A.\", \"N. C. double A.\"], \"display_as\": \"NCAA\"}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • curl -X PUT --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{\"sounds_like\": [\"N. C. A. A.\", \"N. C. double A.\"], \"display_as\": \"NCAA\"}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddWord(
        customizationId: "{customizationId}",
        wordName: "NCAA",
        soundsLike: new List<string>()
        {
            "N. C. A. A.",
            "N. C. double A."
        },
        displayAs: "NCAA"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddWord(
        customizationId: "{customizationId}",
        wordName: "NCAA",
        soundsLike: new List<string>()
        {
            "N. C. A. A.",
            "N. C. double A."
        },
        displayAs: "NCAA"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    AddWordOptions addWordOptions = new AddWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .soundsLike(Arrays.asList("N. C. A. A.", "N. C. double A."))
      .displayAs("NCAA")
      .build();
    
    speechToText.addWord(addWordOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    AddWordOptions addWordOptions = new AddWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .soundsLike(Arrays.asList("N. C. A. A.", "N. C. double A."))
      .displayAs("NCAA")
      .build();
    
    speechToText.addWord(addWordOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const addWordParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.addWord(addWordParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const addWordParams = {
      customizationId: '{customization_id}',
      contentType: 'application/json',
      word: 'NCAA',
      soundsLike: ['N. C. A. A.', 'N. C. double A.'],
      displayAs: 'NCAA',
    };
    
    speechToText.addWord(addWordParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.add_word(
        '{customization_id}',
        'NCAA',
        sounds_like=['N. C. A. A.', 'N. C. double A.'],
        display_as='NCAA'
    )
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.add_word(
        '{customization_id}',
        'NCAA',
        sounds_like=['N. C. A. A.', 'N. C. double A.'],
        display_as='NCAA'
    )

Response

Response type: object

Status Code

  • Created. The custom word was successfully added to the custom language model.

  • Bad Request. The specified customization ID is invalid, or the maximum number of sounds-like pronunciations for a word is exceeded. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Maximum number of sounds-like for a word exceeded
    • Maximum number of allowed phones of one item of sounds_like for word '{word}' exceeded
    • TOTAL_NUMBER_OF_OOV_WORDS_EXCEEDS_MAXIMUM_ALLOWED_FORMAT: "Total number of OOV words {total_number} exceeds {maximum_allowed}"
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. The word name includes characters that need to be URL-encoded.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Get a custom word

Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words.

See also: Listing words from a custom language model.

GET /v1/customizations/{customization_id}/words/{word_name}
GetWord(string customizationId, string wordName)
ServiceCall<Word> getWord(GetWordOptions getWordOptions)
getWord(params)
get_word(
        self,
        customization_id: str,
        word_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetWordOptions.Builder to create a GetWordOptions object that contains the parameter values for the getWord method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be read from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be read from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

The getWord options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be read from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be read from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetWord(
        customizationId: "{customizationId}",
        wordName: "NCAA"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetWord(
        customizationId: "{customizationId}",
        wordName: "NCAA"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetWordOptions getWordOptions = new GetWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .build();
    
    Word word = speechToText.getWord(getWordOptions).execute().getResult();
    System.out.println(word);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetWordOptions getWordOptions = new GetWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .build();
    
    Word word = speechToText.getWord(getWordOptions).execute().getResult();
    System.out.println(word);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getWordParams = {
      customizationId: '{customization_id}',
      wordName: 'NCAA',
    };
    
    speechToText.getWord(getWordParams)
      .then(word => {
        console.log(JSON.stringify(word, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getWordParams = {
      customizationId: '{customization_id}',
      wordName: 'NCAA',
    };
    
    speechToText.getWord(getWordParams)
      .then(word => {
        console.log(JSON.stringify(word, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    word = speech_to_text.get_word(
        '{customization_id}',
        'NCAA'
    ).get_result()
    print(json.dumps(word, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    word = speech_to_text.get_word(
        '{customization_id}',
        'NCAA'
    ).get_result()
    print(json.dumps(word, indent=2))

Response

Information about a word from a custom language model.

Information about a word from a custom language model.

Information about a word from a custom language model.

Information about a word from a custom language model.

Information about a word from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID or word is invalid, including the case where the word does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for word '{word}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "word": "NCAA",
      "sounds_like": [
        "N. C. A. A.",
        "N. C. double A."
      ],
      "display_as": "NCAA",
      "count": 3,
      "source": [
        "corpus3",
        "user"
      ]
    }
  • {
      "word": "NCAA",
      "sounds_like": [
        "N. C. A. A.",
        "N. C. double A."
      ],
      "display_as": "NCAA",
      "count": 3,
      "source": [
        "corpus3",
        "user"
      ]
    }

Delete a custom word

Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its words.

See also: Deleting a word from a custom language model.

Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its words.

See also: Deleting a word from a custom language model.

Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its words.

See also: Deleting a word from a custom language model.

Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its words.

See also: Deleting a word from a custom language model.

Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its words.

See also: Deleting a word from a custom language model.

DELETE /v1/customizations/{customization_id}/words/{word_name}
DeleteWord(string customizationId, string wordName)
ServiceCall<Void> deleteWord(DeleteWordOptions deleteWordOptions)
deleteWord(params)
delete_word(
        self,
        customization_id: str,
        word_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteWordOptions.Builder to create a DeleteWordOptions object that contains the parameter values for the deleteWord method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be deleted from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be deleted from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

The deleteWord options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be deleted from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The custom word that is to be deleted from the custom language model. URL-encode the word if it includes non-ASCII characters. For more information, see Character encoding.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/words/NCAA"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteWord(
        customizationId: "{customizationId}",
        wordName: "NCAA"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteWord(
        customizationId: "{customizationId}",
        wordName: "NCAA"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteWordOptions deleteWordOptions = new DeleteWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .build();
    
    speechToText.deleteWord(deleteWordOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteWordOptions deleteWordOptions = new DeleteWordOptions.Builder()
      .customizationId("{customizationId}")
      .wordName("NCAA")
      .build();
    
    speechToText.deleteWord(deleteWordOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteWordParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.deleteWord(deleteWordParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteWordParams = {
      customizationId: '{customization_id}',
      wordName: 'NCAA',
    };
    
    speechToText.deleteWord(deleteWordParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_word(
        '{customization_id}',
        'NCAA'
    )
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_word(
        '{customization_id}',
        'NCAA'
    )

Response

Response type: object

Status Code

  • OK. The custom word was successfully deleted from the custom language model.

  • Bad Request. The specified customization ID or word is invalid, including the case where the word does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for word '{word}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. No word name was specified with the request.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

List grammars

Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

GET /v1/customizations/{customization_id}/grammars
ListGrammars(string customizationId)
ServiceCall<Grammars> listGrammars(ListGrammarsOptions listGrammarsOptions)
listGrammars(params)
list_grammars(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListGrammarsOptions.Builder to create a ListGrammarsOptions object that contains the parameter values for the listGrammars method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The listGrammars options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/grammars"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/grammars"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListGrammars(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListGrammars(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListGrammarsOptions listGrammarsOptions = new ListGrammarsOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    Grammars grammars =
      speechToText.listGrammars(listGrammarsOptions).execute().getResult();
    System.out.println(grammars);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListGrammarsOptions listGrammarsOptions = new ListGrammarsOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    Grammars grammars =
      speechToText.listGrammars(listGrammarsOptions).execute().getResult();
    System.out.println(grammars);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const listGrammarsParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listGrammars(listGrammarsParams)
      .then(grammars => {
        console.log(JSON.stringify(grammars, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const listGrammarsParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listGrammars(listGrammarsParams)
      .then(grammars => {
        console.log(JSON.stringify(grammars, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    grammars = speech_to_text.list_grammars('{customization_id}').get_result()
    print(json.dumps(grammars, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    grammars = speech_to_text.list_grammars('{customization_id}').get_result()
    print(json.dumps(grammars, indent=2))

Response

Information about the grammars from a custom language model.

Information about the grammars from a custom language model.

Information about the grammars from a custom language model.

Information about the grammars from a custom language model.

Information about the grammars from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "grammars": [
        {
          "out_of_vocabulary_words": 0,
          "name": "confirm-xml",
          "status": "analyzed"
        },
        {
          "out_of_vocabulary_words": 0,
          "name": "confirm-abnf",
          "status": "analyzed"
        },
        {
          "out_of_vocabulary_words": 8,
          "name": "list-abnf",
          "status": "analyzed"
        }
      ]
    }
  • {
      "grammars": [
        {
          "out_of_vocabulary_words": 0,
          "name": "confirm-xml",
          "status": "analyzed"
        },
        {
          "out_of_vocabulary_words": 0,
          "name": "confirm-abnf",
          "status": "analyzed"
        },
        {
          "out_of_vocabulary_words": 8,
          "name": "list-abnf",
          "status": "analyzed"
        }
      ]
    }

Add a grammar

Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the Get a grammar method to check the status of the analysis.

For grammars that are based on previous-generation models, the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the List custom words method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. For grammars that are based on next-generation models, the service extracts no OOV words from the grammars.

To add a grammar that has the same name as an existing grammar, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the Add custom words or Add a custom word method.

For grammars that are based on previous-generation models, the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly.

See also:

Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the Get a grammar method to check the status of the analysis.

For grammars that are based on previous-generation models, the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the List custom words method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. For grammars that are based on next-generation models, the service extracts no OOV words from the grammars.

To add a grammar that has the same name as an existing grammar, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the Add custom words or Add a custom word method.

For grammars that are based on previous-generation models, the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly.

See also:

Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the Get a grammar method to check the status of the analysis.

For grammars that are based on previous-generation models, the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the List custom words method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. For grammars that are based on next-generation models, the service extracts no OOV words from the grammars.

To add a grammar that has the same name as an existing grammar, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the Add custom words or Add a custom word method.

For grammars that are based on previous-generation models, the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly.

See also:

Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the Get a grammar method to check the status of the analysis.

For grammars that are based on previous-generation models, the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the List custom words method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. For grammars that are based on next-generation models, the service extracts no OOV words from the grammars.

To add a grammar that has the same name as an existing grammar, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the Add custom words or Add a custom word method.

For grammars that are based on previous-generation models, the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly.

See also:

Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the Train a custom language model method.

The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the Get a grammar method to check the status of the analysis.

For grammars that are based on previous-generation models, the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the List custom words method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. For grammars that are based on next-generation models, the service extracts no OOV words from the grammars.

To add a grammar that has the same name as an existing grammar, set the allow_overwrite parameter to true; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the Add custom words or Add a custom word method.

For grammars that are based on previous-generation models, the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly.

See also:

POST /v1/customizations/{customization_id}/grammars/{grammar_name}
AddGrammar(string customizationId, string grammarName, System.IO.MemoryStream grammarFile, string contentType, bool? allowOverwrite = null)
ServiceCall<Void> addGrammar(AddGrammarOptions addGrammarOptions)
addGrammar(params)
add_grammar(
        self,
        customization_id: str,
        grammar_name: str,
        grammar_file: BinaryIO,
        content_type: str,
        *,
        allow_overwrite: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the AddGrammarOptions.Builder to create a AddGrammarOptions object that contains the parameter values for the addGrammar method.

Custom Headers

  • The format (MIME type) of the grammar file:

    • application/srgs for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars.
    • application/srgs+xml for XML Form, which uses XML elements to represent the grammar.

    Allowable values: [application/srgs,application/srgs+xml]

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new grammar for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the grammar.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing grammar or corpus that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.

Query Parameters

  • If true, the specified grammar overwrites an existing grammar with the same name. If false, the request fails if a grammar with the same name already exists. The parameter has no effect if a grammar with the same name does not already exist.

    Default: false

A plain text file that contains the grammar in the format specified by the Content-Type header. Encode the file in UTF-8 (ASCII is a subset of UTF-8). Using any other encoding can lead to issues when compiling the grammar or to unexpected results in decoding. The service ignores an encoding that is specified in the header of the grammar.

With the curl command, use the --data-binary option to upload the file for the request.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new grammar for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the grammar.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing grammar or corpus that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the grammar in the format specified by the Content-Type header. Encode the file in UTF-8 (ASCII is a subset of UTF-8). Using any other encoding can lead to issues when compiling the grammar or to unexpected results in decoding. The service ignores an encoding that is specified in the header of the grammar.

    With the curl command, use the --data-binary option to upload the file for the request.

  • The format (MIME type) of the grammar file:

    • application/srgs for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars.
    • application/srgs+xml for XML Form, which uses XML elements to represent the grammar.

    Allowable values: [application/srgs,application/srgs+xml]

  • If true, the specified grammar overwrites an existing grammar with the same name. If false, the request fails if a grammar with the same name already exists. The parameter has no effect if a grammar with the same name does not already exist.

    Default: false

The addGrammar options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new grammar for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the grammar.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing grammar or corpus that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the grammar in the format specified by the Content-Type header. Encode the file in UTF-8 (ASCII is a subset of UTF-8). Using any other encoding can lead to issues when compiling the grammar or to unexpected results in decoding. The service ignores an encoding that is specified in the header of the grammar.

    With the curl command, use the --data-binary option to upload the file for the request.

  • The format (MIME type) of the grammar file:

    • application/srgs for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars.
    • application/srgs+xml for XML Form, which uses XML elements to represent the grammar.

    Allowable values: [application/srgs,application/srgs+xml]

  • If true, the specified grammar overwrites an existing grammar with the same name. If false, the request fails if a grammar with the same name already exists. The parameter has no effect if a grammar with the same name does not already exist.

    Default: false

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new grammar for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the grammar.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an existing grammar or corpus that is already defined for the custom model.
    • Do not use the name user, which is reserved by the service to denote custom words that are added or modified by the user.
    • Do not use the name base_lm or default_lm. Both names are reserved for future use by the service.
  • A plain text file that contains the grammar in the format specified by the Content-Type header. Encode the file in UTF-8 (ASCII is a subset of UTF-8). Using any other encoding can lead to issues when compiling the grammar or to unexpected results in decoding. The service ignores an encoding that is specified in the header of the grammar.

    With the curl command, use the --data-binary option to upload the file for the request.

  • The format (MIME type) of the grammar file:

    • application/srgs for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars.
    • application/srgs+xml for XML Form, which uses XML elements to represent the grammar.

    Allowable values: [application/srgs,application/srgs+xml]

  • If true, the specified grammar overwrites an existing grammar with the same name. If false, the request fails if a grammar with the same name already exists. The parameter has no effect if a grammar with the same name does not already exist.

    Default: false

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/srgs" --data-binary "@list.abnf" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"

    Download sample file list.abnf

  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/srgs" --data-binary "@list.abnf" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"

    Download sample file list.abnf

  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddGrammar(
        customizationId: "{customizationId}",
        grammarFile: new MemoryStream(File.ReadAllText("list.abnf")),
        grammarName: "list-abnf",
        contentType: "application/srgs"
        );
    
    Console.WriteLine(result.Response);
    // Poll for grammar status.

    Download sample file list.abnf

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddGrammar(
        customizationId: "{customizationId}",
        grammarFile: new MemoryStream(File.ReadAllText("list.abnf")),
        grammarName: "list-abnf",
        contentType: "application/srgs"
        );
    
    Console.WriteLine(result.Response);
    // Poll for grammar status.

    Download sample file list.abnf

  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddGrammarOptions addGrammarOptions = new AddGrammarOptions.Builder()
        .customizationId("{customizationId}")
        .grammarFile(new File("list.abnf"))
        .grammarName("list-abnf")
        .contentType("application/srgs")
        .build();
    
      speechToText.addGrammar(addGrammarOptions).execute();
      // Poll for grammar status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }

    Download sample file list.abnf

  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddGrammarOptions addGrammarOptions = new AddGrammarOptions.Builder()
        .customizationId("{customizationId}")
        .grammarFile(new File("list.abnf"))
        .grammarName("list-abnf")
        .contentType("application/srgs")
        .build();
    
      speechToText.addGrammar(addGrammarOptions).execute();
      // Poll for grammar status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }

    Download sample file list.abnf

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const addGrammarParams = {
      customizationId: '{customization_id}',
      grammarFile: fs.createReadStream('./list.abnf'),
      grammarName: 'list-abnf',
      contentType: 'application/srgs',
    };
    
    speechToText.addGrammar(addGrammarParams)
      .then(result => {
        // Poll for grammar status.
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file list.abnf

  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const addGrammarParams = {
      customizationId: '{customization_id}',
      grammarFile: fs.createReadStream('list.abnf'),
      grammarName: 'list-abnf',
      contentType: 'application/srgs',
    };
    
    speechToText.addGrammar(addGrammarParams)
      .then(result => {
        // Poll for grammar status.
      })
      .catch(err => {
        console.log('error:', err);
      });

    Download sample file list.abnf

  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'list.abnf'),
                   'rb') as grammar_file:
        speech_to_text.add_grammar(
            '{customization_id}',
            'list-abnf',
            grammar_file,
            'application/srgs'
        )
    # Poll for grammar status.

    Download sample file list.abnf

  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'list.abnf'),
                   'rb') as grammar_file:
        speech_to_text.add_grammar(
            '{customization_id}',
            'list-abnf',
            grammar_file,
            'application/srgs'
        )
    # Poll for grammar status.

    Download sample file list.abnf

Response

Response type: object

Status Code

  • Created. Addition of the grammar was successfully started. The service is analyzing the data.

  • Bad Request. A required parameter is null or invalid, the specified grammar name already exists, or the custom model needs to be upgraded, among other possibilities. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Grammar file not specified or empty
    • Grammar '{grammar_name}' already exists - change its name, remove existing grammar before adding new one, or overwrite existing grammar by setting 'allow_overwrite' to 'true'
    • Corpus exists with grammar name '{grammar_name}'. Please use different name.
    • TOTAL_NUMBER_OF_OOV_WORDS_EXCEEDS_MAXIMUM_ALLOWED_FORMAT: "Total number of OOV words {total_number} exceeds {maximum_allowed}"
    • Analysis of grammar '{grammar_name}' failed due to {error_message}. Please fix the error then add the grammar again by setting the 'allow_overwrite' flag to 'true'., where {error_message} is a message of the form {"code": 404, "error": "Model en-US_BroadbandModel (version: en-US_BroadbandModel.{version}) not found", "code_description": "Not Found"}. Upgrade the custom language model to the latest version of its base language model, and then add the grammar to the custom model.
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. The grammar name includes characters that need to be URL-encoded.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Unsupported Media Type. The request specified an unacceptable media type.

  • Internal Server Error. An internal error prevented the service from satisfying the request. You can also receive status code 500 Forwarding Error if the service is currently busy handling a previous request for the custom model.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Get a grammar

Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars.

See also:

GET /v1/customizations/{customization_id}/grammars/{grammar_name}
GetGrammar(string customizationId, string grammarName)
ServiceCall<Grammar> getGrammar(GetGrammarOptions getGrammarOptions)
getGrammar(params)
get_grammar(
        self,
        customization_id: str,
        grammar_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetGrammarOptions.Builder to create a GetGrammarOptions object that contains the parameter values for the getGrammar method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

The getGrammar options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetGrammar(
        customizationId: "{customizationId}",
        grammarName: "list-abnf"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetGrammar(
        customizationId: "{customizationId}",
        grammarName: "list-abnf"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetGrammarOptions getGrammarOptions = new GetGrammarOptions.Builder()
      .customizationId("{customizationId}")
      .grammarName("list-abnf")
      .build();
    
    Grammar grammar =
      speechToText.getGrammar(getGrammarOptions).execute().getResult();
    System.out.println(grammar);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetGrammarOptions getGrammarOptions = new GetGrammarOptions.Builder()
      .customizationId("{customizationId}")
      .grammarName("list-abnf")
      .build();
    
    Grammar grammar =
      speechToText.getGrammar(getGrammarOptions).execute().getResult();
    System.out.println(grammar);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getGrammarParams = {
      customizationId: '{customization_id}',
      grammarName: 'list-abnf',
    };
    
    speechToText.getGrammar(getGrammarParams)
      .then(grammar => {
        console.log(JSON.stringify(grammar, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getGrammarParams = {
      customizationId: '{customization_id}',
      grammarName: 'list-abnf',
    };
    
    speechToText.getGrammar(getGrammarParams)
      .then(grammar => {
        console.log(JSON.stringify(grammar, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    grammar = speech_to_text.get_grammar(
        '{customization_id}',
        'list-abnf'
    ).get_result()
    print(json.dumps(grammar, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    grammar = speech_to_text.get_grammar(
        '{customization_id}',
        'list-abnf'
    ).get_result()
    print(json.dumps(grammar, indent=2))

Response

Information about a grammar from a custom language model.

Information about a grammar from a custom language model.

Information about a grammar from a custom language model.

Information about a grammar from a custom language model.

Information about a grammar from a custom language model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID or grammar name is invalid, including the case where the grammar does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for grammar name '{grammar_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "out_of_vocabulary_words": 8,
      "name": "list-abnf",
      "status": "analyzed"
    }
  • {
      "out_of_vocabulary_words": 8,
      "name": "list-abnf",
      "status": "analyzed"
    }

Delete a grammar

Deletes an existing grammar from a custom language model. For grammars that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the Add custom words or Add a custom word method. Removing a grammar does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its grammar.

See also:

Deletes an existing grammar from a custom language model. For grammars that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the Add custom words or Add a custom word method. Removing a grammar does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its grammar.

See also:

Deletes an existing grammar from a custom language model. For grammars that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the Add custom words or Add a custom word method. Removing a grammar does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its grammar.

See also:

Deletes an existing grammar from a custom language model. For grammars that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the Add custom words or Add a custom word method. Removing a grammar does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its grammar.

See also:

Deletes an existing grammar from a custom language model. For grammars that are based on previous-generation models, the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the Add custom words or Add a custom word method. Removing a grammar does not affect the custom model until you train the model with the Train a custom language model method. You must use credentials for the instance of the service that owns a model to delete its grammar.

See also:

DELETE /v1/customizations/{customization_id}/grammars/{grammar_name}
DeleteGrammar(string customizationId, string grammarName)
ServiceCall<Void> deleteGrammar(DeleteGrammarOptions deleteGrammarOptions)
deleteGrammar(params)
delete_grammar(
        self,
        customization_id: str,
        grammar_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteGrammarOptions.Builder to create a DeleteGrammarOptions object that contains the parameter values for the deleteGrammar method.

Path Parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

The deleteGrammar options.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

parameters

  • The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the grammar for the custom language model.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteGrammar(
        customizationId: "{customizationId}",
        grammarName: "list-abnf"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteGrammar(
        customizationId: "{customizationId}",
        grammarName: "list-abnf"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteGrammarOptions deleteGrammarOptions = new DeleteGrammarOptions.Builder()
      .customizationId("{customizationId}")
      .grammarName("list-abnf")
      .build();
    
    speechToText.deleteGrammar(deleteGrammarOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteGrammarOptions deleteGrammarOptions = new DeleteGrammarOptions.Builder()
      .customizationId("{customizationId}")
      .grammarName("list-abnf")
      .build();
    
    speechToText.deleteGrammar(deleteGrammarOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteGrammarParams = {
      customizationId: '{customization_id}',
      grammarName: 'list-abnf',
    };
    
    speechToText.deleteGrammar(deleteGrammarParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteGrammarParams = {
      customizationId: '{customization_id}',
      grammarName: 'list-abnf',
    };
    
    speechToText.deleteGrammar(deleteGrammarParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_grammar(
        '{customization_id}',
        'list-abnf'
    )
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_grammar(
        '{customization_id}',
        'list-abnf'
    )

Response

Response type: object

Status Code

  • OK. The grammar was successfully deleted from the custom language model.

  • Bad Request. The specified customization ID or grammar name is invalid, including the case where the grammar does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for grammar name '{grammar_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. No grammar name was specified with the request.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Create a custom acoustic model

Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also: Create a custom acoustic model.

Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also: Create a custom acoustic model.

Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also: Create a custom acoustic model.

Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also: Create a custom acoustic model.

Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it.

You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

Important: Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see Migrating to next-generation models.

See also: Create a custom acoustic model.

POST /v1/acoustic_customizations
CreateAcousticModel(string name, string baseModelName, string description = null)
ServiceCall<AcousticModel> createAcousticModel(CreateAcousticModelOptions createAcousticModelOptions)
createAcousticModel(params)
create_acoustic_model(
        self,
        name: str,
        base_model_name: str,
        *,
        description: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the CreateAcousticModelOptions.Builder to create a CreateAcousticModelOptions object that contains the parameter values for the createAcousticModel method.

Custom Headers

  • The type of the input.

    Allowable values: [application/json]

A CreateAcousticModel object that provides basic information about the new custom acoustic model.

parameters

  • A user-defined name for the new custom acoustic model. Use a localized name that matches the language of the custom model. Use a name that describes the acoustic environment of the custom model, such as Mobile custom model or Noisy car custom model. Use a name that is unique among all custom acoustic models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom acoustic model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports acoustic model customization, refer to Language support for customization.

    Allowable values: [ar-MS_BroadbandModel,de-DE_BroadbandModel,de-DE_NarrowbandModel,en-AU_BroadbandModel,en-AU_NarrowbandModel,en-GB_BroadbandModel,en-GB_NarrowbandModel,en-US_BroadbandModel,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_NarrowbandModel,fr-FR_BroadbandModel,fr-FR_NarrowbandModel,it-IT_BroadbandModel,it-IT_NarrowbandModel,ja-JP_BroadbandModel,ja-JP_NarrowbandModel,ko-KR_BroadbandModel,ko-KR_NarrowbandModel,nl-NL_BroadbandModel,nl-NL_NarrowbandModel,pt-BR_BroadbandModel,pt-BR_NarrowbandModel,zh-CN_BroadbandModel,zh-CN_NarrowbandModel]

  • A recommended description of the new custom acoustic model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

The createAcousticModel options.

parameters

  • A user-defined name for the new custom acoustic model. Use a localized name that matches the language of the custom model. Use a name that describes the acoustic environment of the custom model, such as Mobile custom model or Noisy car custom model. Use a name that is unique among all custom acoustic models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom acoustic model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports acoustic model customization, refer to Language support for customization.

    Allowable values: [ar-MS_BroadbandModel,de-DE_BroadbandModel,de-DE_NarrowbandModel,en-AU_BroadbandModel,en-AU_NarrowbandModel,en-GB_BroadbandModel,en-GB_NarrowbandModel,en-US_BroadbandModel,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_NarrowbandModel,fr-FR_BroadbandModel,fr-FR_NarrowbandModel,it-IT_BroadbandModel,it-IT_NarrowbandModel,ja-JP_BroadbandModel,ja-JP_NarrowbandModel,ko-KR_BroadbandModel,ko-KR_NarrowbandModel,nl-NL_BroadbandModel,nl-NL_NarrowbandModel,pt-BR_BroadbandModel,pt-BR_NarrowbandModel,zh-CN_BroadbandModel,zh-CN_NarrowbandModel]

  • A recommended description of the new custom acoustic model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

parameters

  • A user-defined name for the new custom acoustic model. Use a localized name that matches the language of the custom model. Use a name that describes the acoustic environment of the custom model, such as Mobile custom model or Noisy car custom model. Use a name that is unique among all custom acoustic models that you own.

    Include a maximum of 256 characters in the name. Do not use backslashes, slashes, colons, equal signs, ampersands, or question marks in the name.

  • The name of the base language model that is to be customized by the new custom acoustic model. The new custom model can be used only with the base model that it customizes.

    To determine whether a base model supports acoustic model customization, refer to Language support for customization.

    Allowable values: [ar-MS_BroadbandModel,de-DE_BroadbandModel,de-DE_NarrowbandModel,en-AU_BroadbandModel,en-AU_NarrowbandModel,en-GB_BroadbandModel,en-GB_NarrowbandModel,en-US_BroadbandModel,en-US_NarrowbandModel,en-US_ShortForm_NarrowbandModel,es-AR_BroadbandModel,es-AR_NarrowbandModel,es-CL_BroadbandModel,es-CL_NarrowbandModel,es-CO_BroadbandModel,es-CO_NarrowbandModel,es-ES_BroadbandModel,es-ES_NarrowbandModel,es-MX_BroadbandModel,es-MX_NarrowbandModel,es-PE_BroadbandModel,es-PE_NarrowbandModel,fr-CA_BroadbandModel,fr-CA_NarrowbandModel,fr-FR_BroadbandModel,fr-FR_NarrowbandModel,it-IT_BroadbandModel,it-IT_NarrowbandModel,ja-JP_BroadbandModel,ja-JP_NarrowbandModel,ko-KR_BroadbandModel,ko-KR_NarrowbandModel,nl-NL_BroadbandModel,nl-NL_NarrowbandModel,pt-BR_BroadbandModel,pt-BR_NarrowbandModel,zh-CN_BroadbandModel,zh-CN_NarrowbandModel]

  • A recommended description of the new custom acoustic model. Use a localized description that matches the language of the custom model. Include a maximum of 128 characters in the description.

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{\"name\": \"First example acoustic model\", \"base_model_name\": \"en-US_BroadbandModel\", \"description\": \"First example custom acoustic model\"}" "{url}/v1/acoustic_customizations"
  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{\"name\": \"First example acoustic model\", \"base_model_name\": \"en-US_BroadbandModel\", \"description\": \"First example custom acoustic model\"}" "{url}/v1/acoustic_customizations"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateAcousticModel(
        name: "First example acoustic model",
        baseModelName: "en-US_BroadbandModel",
        description: "First custom acoustic model example"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.CreateAcousticModel(
        name: "First example acoustic model",
        baseModelName: "en-US_BroadbandModel",
        description: "First custom acoustic model example"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CreateAcousticModelOptions createAcousticModelOptions =
      new CreateAcousticModelOptions.Builder()
        .name("First example acoustic model")
        .baseModelName("en-US_BroadbandModel")
        .description("First custom acoustic model example")
        .build();
    
    AcousticModel acousticModel =
      speechToText.createAcousticModel(createAcousticModelOptions).execute().getResult();
    System.out.println(acousticModel);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    CreateAcousticModelOptions createAcousticModelOptions =
      new CreateAcousticModelOptions.Builder()
        .name("First example acoustic model")
        .baseModelName("en-US_BroadbandModel")
        .description("First custom acoustic model example")
        .build();
    
    AcousticModel acousticModel =
      speechToText.createAcousticModel(createAcousticModelOptions).execute().getResult();
    System.out.println(acousticModel);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const createAcousticModelParams = {
      name: 'First example acoustic model',
      baseModelName: 'en-US_BroadbandModel',
      description: 'First custom acoustic model example',
    };
    
    speechToText.createAcousticModel(createAcousticModelParams)
      .then(acousticModel => {
        console.log(JSON.stringify(acousticModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const createAcousticModelParams = {
      name: 'First example acoustic model',
      baseModelName: 'en-US_BroadbandModel',
      description: 'First custom acoustic model example',
    };
    
    speechToText.createAcousticModel(createAcousticModelParams)
      .then(acousticModel => {
        console.log(JSON.stringify(acousticModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_model = speech_to_text.create_acoustic_model(
        'First example acoustic model',
        'en-US_BroadbandModel',
        description='First custom acoustic model example'
    ).get_result()
    print(json.dumps(acoustic_model, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_model = speech_to_text.create_acoustic_model(
        'First example acoustic model',
        'en-US_BroadbandModel',
        description='First custom acoustic model example'
    ).get_result()
    print(json.dumps(acoustic_model, indent=2))

Response

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Status Code

  • Created. The custom acoustic model was successfully created.

  • Bad Request. A required parameter is null or invalid. Specific failure messages include:

    • Required parameter '{name}' is missing
    • Required parameter '{name}' cannot be empty string
    • Required parameter '{name}' cannot be null
    • The base model '{model_name}' is not recognized
    • Acoustic customization is not supported for base model '{model_name}'
    • You exceeded the maximum '{model_number}' of allowed custom acoustic models. You have '{model_number}' custom acoustic models. Please remove the models you do not need or contact the IBM speech support team to apply for an exception.
  • Unauthorized. The specified credentials are invalid.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96"
    }
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96"
    }

List custom acoustic models

Lists information about all custom acoustic models that are owned by an instance of the service. Use the language parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Lists information about all custom acoustic models that are owned by an instance of the service. Use the language parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Lists information about all custom acoustic models that are owned by an instance of the service. Use the language parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Lists information about all custom acoustic models that are owned by an instance of the service. Use the language parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Lists information about all custom acoustic models that are owned by an instance of the service. Use the language parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

GET /v1/acoustic_customizations
ListAcousticModels(string language = null)
ServiceCall<AcousticModels> listAcousticModels(ListAcousticModelsOptions listAcousticModelsOptions)
listAcousticModels(params)
list_acoustic_models(
        self,
        *,
        language: str = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListAcousticModelsOptions.Builder to create a ListAcousticModelsOptions object that contains the parameter values for the listAcousticModels method.

Query Parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

The listAcousticModels options.

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

parameters

  • The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify en-US to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.

    To determine the languages for which customization is available, see Language support for customization.

    Allowable values: [ar-MS,cs-CZ,de-DE,en-AU,en-GB,en-IN,en-US,en-WW,es-AR,es-CL,es-CO,es-ES,es-LA,es-MX,es-PE,fr-CA,fr-FR,hi-IN,it-IT,ja-JP,ko-KR,nl-BE,nl-NL,pt-BR,sv-SE,zh-CN]

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/acoustic_customizations?language=en-US"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations?language=en-US"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListAcousticModels(
        language: "en-US"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListAcousticModels(
        language: "en-US"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListAcousticModelsOptions listAcousticModelsOptions =
      new ListAcousticModelsOptions.Builder()
        .language("en-US")
        .build();
    
    AcousticModels acousticModels =
      speechToText.listAcousticModels(listAcousticModelsOptions).execute().getResult();
    System.out.println(acousticModels);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListAcousticModelsOptions listAcousticModelsOptions =
      new ListAcousticModelsOptions.Builder()
        .language("en-US")
        .build();
    
    AcousticModels acousticModels =
      speechToText.listAcousticModels(listAcousticModelsOptions).execute().getResult();
    System.out.println(acousticModels);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const listAcousticModelsParams = {
      language: 'en-US',
    };
    
    speechToText.listAcousticModels(listAcousticModelsParams)
      .then(acousticModels => {
        console.log(JSON.stringify(acousticModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const acousticModelParams = {
      language: 'en-US',
    };
    
    speechToText.listAcousticModels(listAcousticModelsParams)
      .then(acousticModels => {
        console.log(JSON.stringify(acousticModels, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_models = speech_to_text.list_acoustic_models('en-US').get_result()
    print(json.dumps(acoustic_models, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_models = speech_to_text.list_acoustic_models('en-US').get_result()
    print(json.dumps(acoustic_models, indent=2))

Response

Information about existing custom acoustic models.

Information about existing custom acoustic models.

Information about existing custom acoustic models.

Information about existing custom acoustic models.

Information about existing custom acoustic models.

Status Code

  • OK. The request succeeded.

  • Bad Request. A required parameter is null or invalid. Specific failure messages include:

    • Language '{language}' is not supported for customization
  • Unauthorized. The specified credentials are invalid.

  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customizations": [
        {
          "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fa97",
          "created": "2016-06-01T18:42:25.324Z",
          "updated": "2020-01-19T11:12:02.296Z",
          "language": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2018-07-31",
            "en-US_BroadbandModel.v2020-01-16"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model one",
          "description": "Example custom acoustic model",
          "base_model_name": "en-US_BroadbandModel",
          "status": "pending",
          "progress": 0
        },
        {
          "customization_id": "8391f918-3b76-e109-763c-b7732faa3312",
          "created": "2017-12-01T18:51:37.291Z",
          "updated": "2017-12-02T19:21:06.825Z",
          "language": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2017-11-15"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model two",
          "description": "Example custom acoustic model two",
          "base_model_name": "en-US_BroadbandModel",
          "status": "available",
          "progress": 100
        }
      ]
    }
  • {
      "customizations": [
        {
          "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fa97",
          "created": "2016-06-01T18:42:25.324Z",
          "updated": "2020-01-19T11:12:02.296Z",
          "language": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2018-07-31",
            "en-US_BroadbandModel.v2020-01-16"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model one",
          "description": "Example custom acoustic model",
          "base_model_name": "en-US_BroadbandModel",
          "status": "pending",
          "progress": 0
        },
        {
          "customization_id": "8391f918-3b76-e109-763c-b7732faa3312",
          "created": "2017-12-01T18:51:37.291Z",
          "updated": "2017-12-02T19:21:06.825Z",
          "language": "en-US",
          "versions": [
            "en-US_BroadbandModel.v2017-11-15"
          ],
          "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
          "name": "Example model two",
          "description": "Example custom acoustic model two",
          "base_model_name": "en-US_BroadbandModel",
          "status": "available",
          "progress": 100
        }
      ]
    }

Get a custom acoustic model

Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing custom acoustic models.

GET /v1/acoustic_customizations/{customization_id}
GetAcousticModel(string customizationId)
ServiceCall<AcousticModel> getAcousticModel(GetAcousticModelOptions getAcousticModelOptions)
getAcousticModel(params)
get_acoustic_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetAcousticModelOptions.Builder to create a GetAcousticModelOptions object that contains the parameter values for the getAcousticModel method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The getAcousticModel options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetAcousticModelOptions getAcousticModelOptions =
      new GetAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    AcousticModel acousticModel =
      speechToText.getAcousticModel(getAcousticModelOptions).execute().getResult();
    System.out.println(acousticModel);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetAcousticModelOptions getAcousticModelOptions =
      new GetAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    AcousticModel acousticModel =
      speechToText.getAcousticModel(getAcousticModelOptions).execute().getResult();
    System.out.println(acousticModel);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.getAcousticModel(getAcousticModelParams)
      .then(acousticModel => {
        console.log(JSON.stringify(acousticModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.getAcousticModel(getAcousticModelParams)
      .then(acousticModel => {
        console.log(JSON.stringify(acousticModel, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_model = speech_to_text.get_acoustic_model('{customization_id}').get_result()
    print(json.dumps(acoustic_model, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    acoustic_model = speech_to_text.get_acoustic_model('{customization_id}').get_result()
    print(json.dumps(acoustic_model, indent=2))

Response

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Information about an existing custom acoustic model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fa97",
      "created": "2016-06-01T18:42:25.324Z",
      "updated": "2020-01-19T11:12:02.296Z",
      "language": "en-US",
      "versions": [
        "en-US_BroadbandModel.v2018-07-31",
        "en-US_BroadbandModel.v2020-01-16"
      ],
      "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
      "name": "Example model one",
      "description": "Example custom acoustic model",
      "base_model_name": "en-US_BroadbandModel",
      "status": "pending",
      "progress": 0
    }
  • {
      "customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fa97",
      "created": "2016-06-01T18:42:25.324Z",
      "updated": "2020-01-19T11:12:02.296Z",
      "language": "en-US",
      "versions": [
        "en-US_BroadbandModel.v2018-07-31",
        "en-US_BroadbandModel.v2020-01-16"
      ],
      "owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
      "name": "Example model one",
      "description": "Example custom acoustic model",
      "base_model_name": "en-US_BroadbandModel",
      "status": "pending",
      "progress": 0
    }

Delete a custom acoustic model

Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting a custom acoustic model.

Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting a custom acoustic model.

Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting a custom acoustic model.

Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting a custom acoustic model.

Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting a custom acoustic model.

DELETE /v1/acoustic_customizations/{customization_id}
DeleteAcousticModel(string customizationId)
ServiceCall<Void> deleteAcousticModel(DeleteAcousticModelOptions deleteAcousticModelOptions)
deleteAcousticModel(params)
delete_acoustic_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteAcousticModelOptions.Builder to create a DeleteAcousticModelOptions object that contains the parameter values for the deleteAcousticModel method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The deleteAcousticModel options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteAcousticModelOptions deleteAcousticModelOptions =
      new DeleteAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.deleteAcousticModel(deleteAcousticModelOptions).execute();
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteAcousticModelOptions deleteAcousticModelOptions =
      new DeleteAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.deleteAcousticModel(deleteAcousticModelOptions).execute();
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.deleteAcousticModel(deleteAcousticModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.deleteAcousticModel(deleteAcousticModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_acoustic_model('{customization_id}')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_acoustic_model('{customization_id}')

Response

Response type: object

Status Code

  • OK. The custom acoustic model was successfully deleted.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials, including the case where the custom model does not exist:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Train a custom acoustic model

Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom acoustic model method to poll the model's status. Use a loop to check the status once a minute. The method returns an AcousticModel object that includes status and progress fields. A status of available indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes.

You can use the optional custom_language_model_id parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model.
  • The custom model contains less than 10 minutes of audio that includes speech, not silence.
  • The custom model contains more than 50 hours of audio (for IBM Cloud) or more that 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.
  • You passed a custom language model with the custom_language_model_id query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model.
  • You passed an incompatible custom language model with the custom_language_model_id query parameter. Both custom models must be based on the same version of the same base model.
  • The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom acoustic model method to poll the model's status. Use a loop to check the status once a minute. The method returns an AcousticModel object that includes status and progress fields. A status of available indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes.

You can use the optional custom_language_model_id parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model.
  • The custom model contains less than 10 minutes of audio that includes speech, not silence.
  • The custom model contains more than 50 hours of audio (for IBM Cloud) or more that 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.
  • You passed a custom language model with the custom_language_model_id query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model.
  • You passed an incompatible custom language model with the custom_language_model_id query parameter. Both custom models must be based on the same version of the same base model.
  • The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom acoustic model method to poll the model's status. Use a loop to check the status once a minute. The method returns an AcousticModel object that includes status and progress fields. A status of available indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes.

You can use the optional custom_language_model_id parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model.
  • The custom model contains less than 10 minutes of audio that includes speech, not silence.
  • The custom model contains more than 50 hours of audio (for IBM Cloud) or more that 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.
  • You passed a custom language model with the custom_language_model_id query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model.
  • You passed an incompatible custom language model with the custom_language_model_id query parameter. Both custom models must be based on the same version of the same base model.
  • The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom acoustic model method to poll the model's status. Use a loop to check the status once a minute. The method returns an AcousticModel object that includes status and progress fields. A status of available indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes.

You can use the optional custom_language_model_id parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model.
  • The custom model contains less than 10 minutes of audio that includes speech, not silence.
  • The custom model contains more than 50 hours of audio (for IBM Cloud) or more that 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.
  • You passed a custom language model with the custom_language_model_id query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model.
  • You passed an incompatible custom language model with the custom_language_model_id query parameter. Both custom models must be based on the same version of the same base model.
  • The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.

Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it.

The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun.

You can monitor the status of the training by using the Get a custom acoustic model method to poll the model's status. Use a loop to check the status once a minute. The method returns an AcousticModel object that includes status and progress fields. A status of available indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes.

You can use the optional custom_language_model_id parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also:

Training failures

Training can fail to start for the following reasons:

  • The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model.
  • The custom model contains less than 10 minutes of audio that includes speech, not silence.
  • The custom model contains more than 50 hours of audio (for IBM Cloud) or more that 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.
  • You passed a custom language model with the custom_language_model_id query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model.
  • You passed an incompatible custom language model with the custom_language_model_id query parameter. Both custom models must be based on the same version of the same base model.
  • The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the strict parameter to false to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed.
POST /v1/acoustic_customizations/{customization_id}/train
TrainAcousticModel(string customizationId, string customLanguageModelId = null, bool? strict = null)
ServiceCall<TrainingResponse> trainAcousticModel(TrainAcousticModelOptions trainAcousticModelOptions)
trainAcousticModel(params)
train_acoustic_model(
        self,
        customization_id: str,
        *,
        custom_language_model_id: str = None,
        strict: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the TrainAcousticModelOptions.Builder to create a TrainAcousticModelOptions object that contains the parameter values for the trainAcousticModel method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

Query Parameters

  • The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources. The custom language model must be based on the same version of the same base model as the custom acoustic model, and the custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If false, allows training of the custom acoustic model to proceed as long as the model contains at least one valid audio resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom acoustic model fails (status code 400) if the model contains one or more invalid audio resources.

    Default: true

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources. The custom language model must be based on the same version of the same base model as the custom acoustic model, and the custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If false, allows training of the custom acoustic model to proceed as long as the model contains at least one valid audio resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom acoustic model fails (status code 400) if the model contains one or more invalid audio resources.

    Default: true

The trainAcousticModel options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources. The custom language model must be based on the same version of the same base model as the custom acoustic model, and the custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If false, allows training of the custom acoustic model to proceed as long as the model contains at least one valid audio resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom acoustic model fails (status code 400) if the model contains one or more invalid audio resources.

    Default: true

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources. The custom language model must be based on the same version of the same base model as the custom acoustic model, and the custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If false, allows training of the custom acoustic model to proceed as long as the model contains at least one valid audio resource. The method returns an array of TrainingWarning objects that lists any invalid resources. By default (true), training of a custom acoustic model fails (status code 400) if the model contains one or more invalid audio resources.

    Default: true

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/train?custom_language_model_id={customization_id}"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/train?custom_language_model_id={customization_id}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.TrainAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for acoustic model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.TrainAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for acoustic model status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    TrainAcousticModelOptions trainAcousticModelOptions =
      new TrainAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.trainAcousticModel(trainAcousticModelOptions).execute();
    // Poll for acoustic model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    TrainAcousticModelOptions trainAcousticModelOptions =
      new TrainAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.trainAcousticModel(trainAcousticModelOptions).execute();
    // Poll for acoustic model status.
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const trainAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.trainAcousticModel(trainAcousticModelParams)
      .then(result => {
        // Poll for acoustic model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const trainAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.trainAcousticModel(trainAcousticModelParams)
      .then(result => {
        // Poll for acoustic model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.train_acoustic_model('{customization_id}')
    # Poll for acoustic model status.
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.train_acoustic_model('{customization_id}')
    # Poll for acoustic model status.

Response

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

The response from training of a custom language or custom acoustic model.

Status Code

  • OK. Training of the custom acoustic model started successfully.

  • Bad Request. A required parameter is null or invalid, or the custom model is not ready to be trained. Specific failure messages include:

    • No input data modified since last training
    • The following audio resources are invalid: '{resources}'. Fix errors before training.
    • Malformed GUID: '{customization_id}'
    • The specified custom language model '{customization_id}' is not ready for AM training and/or upgrade. Please make sure it is trained and available.
    • Failed to train. No base model version found in the catalog to match amVersion='{base_model_version}' of the acoustic custom model '{customization_id}' and lmVersion='{base_model_version}' of passed language custom model '{customization_id}'. Upgrading the acoustic custom model may help.
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Reset a custom acoustic model

Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Resetting a custom acoustic model.

Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Resetting a custom acoustic model.

Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Resetting a custom acoustic model.

Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Resetting a custom acoustic model.

Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Resetting a custom acoustic model.

POST /v1/acoustic_customizations/{customization_id}/reset
ResetAcousticModel(string customizationId)
ServiceCall<Void> resetAcousticModel(ResetAcousticModelOptions resetAcousticModelOptions)
resetAcousticModel(params)
reset_acoustic_model(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ResetAcousticModelOptions.Builder to create a ResetAcousticModelOptions object that contains the parameter values for the resetAcousticModel method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The resetAcousticModel options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/reset"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/reset"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ResetAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ResetAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ResetAcousticModelOptions resetAcousticModelOptions =
      new ResetAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.resetAcousticModel(resetAcousticModelOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ResetAcousticModelOptions resetAcousticModelOptions =
      new ResetAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.resetAcousticModel(resetAcousticModelOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const resetAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.resetAcousticModel(resetAcousticModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const resetAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.resetAcousticModel(resetAcousticModelParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.reset_acoustic_model('{customization_id}')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.reset_acoustic_model('{customization_id}')

Response

Response type: object

Status Code

  • OK. The custom acoustic model was successfully reset.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Upgrade a custom acoustic model

Initiates the upgrade of a custom acoustic model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes or hours to complete depending on the amount of data in the custom model and the current load on the service; typically, upgrade takes approximately twice the length of the total audio contained in the custom model. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom acoustic model method to poll the model's status. The method returns an AcousticModel object that includes status and progress fields. Use a loop to check the status once a minute.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes.

If the custom acoustic model was trained with a separately created custom language model, you must use the custom_language_model_id parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Upgrading a custom acoustic model.

Initiates the upgrade of a custom acoustic model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes or hours to complete depending on the amount of data in the custom model and the current load on the service; typically, upgrade takes approximately twice the length of the total audio contained in the custom model. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom acoustic model method to poll the model's status. The method returns an AcousticModel object that includes status and progress fields. Use a loop to check the status once a minute.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes.

If the custom acoustic model was trained with a separately created custom language model, you must use the custom_language_model_id parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Upgrading a custom acoustic model.

Initiates the upgrade of a custom acoustic model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes or hours to complete depending on the amount of data in the custom model and the current load on the service; typically, upgrade takes approximately twice the length of the total audio contained in the custom model. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom acoustic model method to poll the model's status. The method returns an AcousticModel object that includes status and progress fields. Use a loop to check the status once a minute.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes.

If the custom acoustic model was trained with a separately created custom language model, you must use the custom_language_model_id parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Upgrading a custom acoustic model.

Initiates the upgrade of a custom acoustic model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes or hours to complete depending on the amount of data in the custom model and the current load on the service; typically, upgrade takes approximately twice the length of the total audio contained in the custom model. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom acoustic model method to poll the model's status. The method returns an AcousticModel object that includes status and progress fields. Use a loop to check the status once a minute.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes.

If the custom acoustic model was trained with a separately created custom language model, you must use the custom_language_model_id parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Upgrading a custom acoustic model.

Initiates the upgrade of a custom acoustic model to the latest version of its base language model. The upgrade method is asynchronous. It can take on the order of minutes or hours to complete depending on the amount of data in the custom model and the current load on the service; typically, upgrade takes approximately twice the length of the total audio contained in the custom model. A custom model must be in the ready or available state to be upgraded. You must use credentials for the instance of the service that owns a model to upgrade it.

The method returns an HTTP 200 response code to indicate that the upgrade process has begun successfully. You can monitor the status of the upgrade by using the Get a custom acoustic model method to poll the model's status. The method returns an AcousticModel object that includes status and progress fields. Use a loop to check the status once a minute.

While it is being upgraded, the custom model has the status upgrading. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes.

If the custom acoustic model was trained with a separately created custom language model, you must use the custom_language_model_id parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Upgrading a custom acoustic model.

POST /v1/acoustic_customizations/{customization_id}/upgrade_model
UpgradeAcousticModel(string customizationId, string customLanguageModelId = null, bool? force = null)
ServiceCall<Void> upgradeAcousticModel(UpgradeAcousticModelOptions upgradeAcousticModelOptions)
upgradeAcousticModel(params)
upgrade_acoustic_model(
        self,
        customization_id: str,
        *,
        custom_language_model_id: str = None,
        force: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the UpgradeAcousticModelOptions.Builder to create a UpgradeAcousticModelOptions object that contains the parameter values for the upgradeAcousticModel method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

Query Parameters

  • If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. The custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If true, forces the upgrade of a custom acoustic model for which no input data has been modified since it was last trained. Use this parameter only to force the upgrade of a custom acoustic model that is trained with a custom language model, and only if you receive a 400 response code and the message No input data modified since last training. See Upgrading a custom acoustic model.

    Default: false

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. The custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If true, forces the upgrade of a custom acoustic model for which no input data has been modified since it was last trained. Use this parameter only to force the upgrade of a custom acoustic model that is trained with a custom language model, and only if you receive a 400 response code and the message No input data modified since last training. See Upgrading a custom acoustic model.

    Default: false

The upgradeAcousticModel options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. The custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If true, forces the upgrade of a custom acoustic model for which no input data has been modified since it was last trained. Use this parameter only to force the upgrade of a custom acoustic model that is trained with a custom language model, and only if you receive a 400 response code and the message No input data modified since last training. See Upgrading a custom acoustic model.

    Default: false

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. The custom language model must be fully trained and available. The credentials specified with the request must own both custom models.

  • If true, forces the upgrade of a custom acoustic model for which no input data has been modified since it was last trained. Use this parameter only to force the upgrade of a custom acoustic model that is trained with a custom language model, and only if you receive a 400 response code and the message No input data modified since last training. See Upgrading a custom acoustic model.

    Default: false

  • curl -X POST -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/upgrade_model"
  • curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/upgrade_model"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UpgradeAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for acoustic model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.UpgradeAcousticModel(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
    // Poll for acoustic model status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UpgradeAcousticModelOptions upgradeAcousticModelOptions =
      new UpgradeAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.upgradeAcousticModel(upgradeAcousticModelOptions).execute();
    // Poll for acoustic model status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    UpgradeAcousticModelOptions upgradeAcousticModelOptions =
      new UpgradeAcousticModelOptions.Builder()
        .customizationId("{customizationId}")
        .build();
    
    speechToText.upgradeAcousticModel(upgradeAcousticModelOptions).execute();
    // Poll for acoustic model status.
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const upgradeAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.upgradeAcousticModel(upgradeAcousticModelParams)
      .then(result => {
        // Poll for acoustic model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const upgradeAcousticModelParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.upgradeAcousticModel(upgradeAcousticModelParams)
      .then(result => {
        // Poll for acoustic model status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.upgrade_acoustic_model('{customization_id}')
    # Poll for acoustic model status.
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.upgrade_acoustic_model('{customization_id}')
    # Poll for acoustic model status.

Response

Response type: object

Status Code

  • OK. Upgrade of the custom acoustic model started successfully.

  • Bad Request. A parameter is null or invalid, or the specified custom model cannot be upgraded. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Custom model is up-to-date
    • No input data available to upgrade the model
    • No input data modified since last training
    • Cannot upgrade failed custom model
    • The passed language custom model needs to be upgraded in order to upgrade the acoustic custom model.
    • The specified custom language model '{customization_id}' is not ready for AM training and/or upgrade. Please make sure it is trained and available.
    • Base model name mismatch detected. Please make sure that the base model name of the language custom model matches the base model name of the acoustic custom model.
    • Invalid model type for customization_id '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

List audio resources

Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

GET /v1/acoustic_customizations/{customization_id}/audio
ListAudio(string customizationId)
ServiceCall<AudioResources> listAudio(ListAudioOptions listAudioOptions)
listAudio(params)
list_audio(
        self,
        customization_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the ListAudioOptions.Builder to create a ListAudioOptions object that contains the parameter values for the listAudio method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

The listAudio options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/audio"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/audio"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListAudio(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.ListAudio(
        customizationId: "{customizationId}"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListAudioOptions listAudioOptions = new ListAudioOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    AudioResources audioResources =
      speechToText.listAudio(listAudioOptions).execute().getResult();
    System.out.println(audioResources);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    ListAudioOptions listAudioOptions = new ListAudioOptions.Builder()
      .customizationId("{customizationId}")
      .build();
    
    AudioResources audioResources =
      speechToText.listAudio(listAudioOptions).execute().getResult();
    System.out.println(audioResources);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const listAudioParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listAudio(listAudioParams)
      .then(audioResources => {
        console.log(JSON.stringify(audioResources, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const listAudioParams = {
      customizationId: '{customization_id}',
    };
    
    speechToText.listAudio(listAudioParams)
      .then(audioResources => {
        console.log(JSON.stringify(audioResources, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    audio_resources = speech_to_text.list_audio('{customization_id}').get_result()
    print(json.dumps(audio_resources, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    audio_resources = speech_to_text.list_audio('{customization_id}').get_result()
    print(json.dumps(audio_resources, indent=2))

Response

Information about the audio resources from a custom acoustic model.

Information about the audio resources from a custom acoustic model.

Information about the audio resources from a custom acoustic model.

Information about the audio resources from a custom acoustic model.

Information about the audio resources from a custom acoustic model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID is invalid:

    • Malformed GUID: '{customization_id}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "total_minutes_of_audio": 11.45,
      "audio": [
        {
          "duration": 131,
          "name": "audio1",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 22050
          },
          "status": "ok"
        },
        {
          "duration": 556,
          "name": "audio2",
          "details": {
            "type": "archive",
            "compression": "zip"
          },
          "status": "ok"
        },
        {
          "duration": 0,
          "name": "audio3",
          "details": {},
          "status": "being_processed"
        }
      ]
    }
  • {
      "total_minutes_of_audio": 11.45,
      "audio": [
        {
          "duration": 131,
          "name": "audio1",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 22050
          },
          "status": "ok"
        },
        {
          "duration": 556,
          "name": "audio2",
          "details": {
            "type": "archive",
            "compression": "zip"
          },
          "status": "ok"
        },
        {
          "duration": 0,
          "name": "audio3",
          "details": {},
          "status": "being_processed"
        }
      ]
    }

Add an audio resource

Adds an audio resource to a custom acoustic model. Add audio content that reflects the acoustic characteristics of the audio that you plan to transcribe. You must use credentials for the instance of the service that owns a model to add an audio resource to it. Adding audio data does not affect the custom acoustic model until you train the model for the new data by using the Train a custom acoustic model method.

You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition.

You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the allow_overwrite parameter to true; otherwise, the request fails. A custom model can contain no more than 50 hours of audio (for IBM Cloud) or 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.

The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes.

To determine the status of the service's analysis of the audio, use the Get an audio resource method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes ok.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Add audio to the custom acoustic model.

Content types for audio-type resources

You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the Content-Type parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated.

  • audio/alaw (Specify the sampling rate (rate) of the audio.)
  • audio/basic (Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid.

See also: Supported audio formats.

Content types for archive-type resources

You can add an archive file (.zip or .tar.gz file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the Content-Type parameter to specify the media type of the archive file:

  • application/zip for a .zip file
  • application/gzip for a .tar.gz file.

When you add an archive-type resource, the Contained-Content-Type header is optional depending on the format of the files that you are adding:

  • For audio files of type audio/alaw, audio/basic, audio/l16, or audio/mulaw, you must use the Contained-Content-Type header to specify the format of the contained audio files. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format.
  • For audio files of all other types, you can omit the Contained-Content-Type header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format.

Do not use the Contained-Content-Type header when adding an audio-type resource.

Naming restrictions for embedded audio files

The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes).

Adds an audio resource to a custom acoustic model. Add audio content that reflects the acoustic characteristics of the audio that you plan to transcribe. You must use credentials for the instance of the service that owns a model to add an audio resource to it. Adding audio data does not affect the custom acoustic model until you train the model for the new data by using the Train a custom acoustic model method.

You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition.

You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the allow_overwrite parameter to true; otherwise, the request fails. A custom model can contain no more than 50 hours of audio (for IBM Cloud) or 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.

The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes.

To determine the status of the service's analysis of the audio, use the Get an audio resource method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes ok.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Add audio to the custom acoustic model.

Content types for audio-type resources

You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the Content-Type parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated.

  • audio/alaw (Specify the sampling rate (rate) of the audio.)
  • audio/basic (Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid.

See also: Supported audio formats.

Content types for archive-type resources

You can add an archive file (.zip or .tar.gz file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the Content-Type parameter to specify the media type of the archive file:

  • application/zip for a .zip file
  • application/gzip for a .tar.gz file.

When you add an archive-type resource, the Contained-Content-Type header is optional depending on the format of the files that you are adding:

  • For audio files of type audio/alaw, audio/basic, audio/l16, or audio/mulaw, you must use the Contained-Content-Type header to specify the format of the contained audio files. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format.
  • For audio files of all other types, you can omit the Contained-Content-Type header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format.

Do not use the Contained-Content-Type header when adding an audio-type resource.

Naming restrictions for embedded audio files

The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes).

Adds an audio resource to a custom acoustic model. Add audio content that reflects the acoustic characteristics of the audio that you plan to transcribe. You must use credentials for the instance of the service that owns a model to add an audio resource to it. Adding audio data does not affect the custom acoustic model until you train the model for the new data by using the Train a custom acoustic model method.

You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition.

You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the allow_overwrite parameter to true; otherwise, the request fails. A custom model can contain no more than 50 hours of audio (for IBM Cloud) or 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.

The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes.

To determine the status of the service's analysis of the audio, use the Get an audio resource method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes ok.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Add audio to the custom acoustic model.

Content types for audio-type resources

You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the Content-Type parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated.

  • audio/alaw (Specify the sampling rate (rate) of the audio.)
  • audio/basic (Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid.

See also: Supported audio formats.

Content types for archive-type resources

You can add an archive file (.zip or .tar.gz file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the Content-Type parameter to specify the media type of the archive file:

  • application/zip for a .zip file
  • application/gzip for a .tar.gz file.

When you add an archive-type resource, the Contained-Content-Type header is optional depending on the format of the files that you are adding:

  • For audio files of type audio/alaw, audio/basic, audio/l16, or audio/mulaw, you must use the Contained-Content-Type header to specify the format of the contained audio files. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format.
  • For audio files of all other types, you can omit the Contained-Content-Type header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format.

Do not use the Contained-Content-Type header when adding an audio-type resource.

Naming restrictions for embedded audio files

The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes).

Adds an audio resource to a custom acoustic model. Add audio content that reflects the acoustic characteristics of the audio that you plan to transcribe. You must use credentials for the instance of the service that owns a model to add an audio resource to it. Adding audio data does not affect the custom acoustic model until you train the model for the new data by using the Train a custom acoustic model method.

You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition.

You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the allow_overwrite parameter to true; otherwise, the request fails. A custom model can contain no more than 50 hours of audio (for IBM Cloud) or 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.

The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes.

To determine the status of the service's analysis of the audio, use the Get an audio resource method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes ok.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Add audio to the custom acoustic model.

Content types for audio-type resources

You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the Content-Type parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated.

  • audio/alaw (Specify the sampling rate (rate) of the audio.)
  • audio/basic (Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid.

See also: Supported audio formats.

Content types for archive-type resources

You can add an archive file (.zip or .tar.gz file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the Content-Type parameter to specify the media type of the archive file:

  • application/zip for a .zip file
  • application/gzip for a .tar.gz file.

When you add an archive-type resource, the Contained-Content-Type header is optional depending on the format of the files that you are adding:

  • For audio files of type audio/alaw, audio/basic, audio/l16, or audio/mulaw, you must use the Contained-Content-Type header to specify the format of the contained audio files. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format.
  • For audio files of all other types, you can omit the Contained-Content-Type header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format.

Do not use the Contained-Content-Type header when adding an audio-type resource.

Naming restrictions for embedded audio files

The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes).

Adds an audio resource to a custom acoustic model. Add audio content that reflects the acoustic characteristics of the audio that you plan to transcribe. You must use credentials for the instance of the service that owns a model to add an audio resource to it. Adding audio data does not affect the custom acoustic model until you train the model for the new data by using the Train a custom acoustic model method.

You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition.

You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the allow_overwrite parameter to true; otherwise, the request fails. A custom model can contain no more than 50 hours of audio (for IBM Cloud) or 200 hours of audio (for IBM Cloud Pak for Data). Note: For IBM Cloud, the maximum hours of audio for a custom acoustic model was reduced from 200 to 50 hours in August and September 2022. For more information, see Maximum hours of audio.

The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes.

To determine the status of the service's analysis of the audio, use the Get an audio resource method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes ok.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Add audio to the custom acoustic model.

Content types for audio-type resources

You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the Content-Type parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated.

  • audio/alaw (Specify the sampling rate (rate) of the audio.)
  • audio/basic (Use only with narrowband models.)
  • audio/flac
  • audio/g729 (Use only with narrowband models.)
  • audio/l16 (Specify the sampling rate (rate) and optionally the number of channels (channels) and endianness (endianness) of the audio.)
  • audio/mp3
  • audio/mpeg
  • audio/mulaw (Specify the sampling rate (rate) of the audio.)
  • audio/ogg (The service automatically detects the codec of the input audio.)
  • audio/ogg;codecs=opus
  • audio/ogg;codecs=vorbis
  • audio/wav (Provide audio with a maximum of nine channels.)
  • audio/webm (The service automatically detects the codec of the input audio.)
  • audio/webm;codecs=opus
  • audio/webm;codecs=vorbis

The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid.

See also: Supported audio formats.

Content types for archive-type resources

You can add an archive file (.zip or .tar.gz file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the Content-Type parameter to specify the media type of the archive file:

  • application/zip for a .zip file
  • application/gzip for a .tar.gz file.

When you add an archive-type resource, the Contained-Content-Type header is optional depending on the format of the files that you are adding:

  • For audio files of type audio/alaw, audio/basic, audio/l16, or audio/mulaw, you must use the Contained-Content-Type header to specify the format of the contained audio files. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format.
  • For audio files of all other types, you can omit the Contained-Content-Type header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format.

Do not use the Contained-Content-Type header when adding an audio-type resource.

Naming restrictions for embedded audio files

The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes).

POST /v1/acoustic_customizations/{customization_id}/audio/{audio_name}
AddAudio(string customizationId, string audioName, System.IO.MemoryStream audioResource, string contentType = null, string containedContentType = null, bool? allowOverwrite = null)
ServiceCall<Void> addAudio(AddAudioOptions addAudioOptions)
addAudio(params)
add_audio(
        self,
        customization_id: str,
        audio_name: str,
        audio_resource: BinaryIO,
        *,
        content_type: str = None,
        contained_content_type: str = None,
        allow_overwrite: bool = None,
        **kwargs,
    ) -> DetailedResponse

Request

Use the AddAudioOptions.Builder to create a AddAudioOptions object that contains the parameter values for the addAudio method.

Custom Headers

  • For an archive-type resource, specify the format of the audio files that are contained in the archive file if they are of type audio/alaw, audio/basic, audio/l16, or audio/mulaw. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type.

    For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph.

    The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see Content types for audio-type resources in the method description.

    For an audio-type resource, omit the header.

    Allowable values: [audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • For an audio-type resource, the format (MIME type) of the audio. For more information, see Content types for audio-type resources in the method description.

    For an archive-type resource, the media type of the archive file. For more information, see Content types for archive-type resources in the method description.

    Allowable values: [application/zip,application/gzip,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new audio resource for the custom acoustic model. Use a localized name that matches the language of the custom model and reflects the contents of the resource.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an audio resource that has already been added to the custom model.

Query Parameters

  • If true, the specified audio resource overwrites an existing audio resource with the same name. If false, the request fails if an audio resource with the same name already exists. The parameter has no effect if an audio resource with the same name does not already exist.

    Default: false

The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file.

With the curl command, use the --data-binary option to upload the file for the request.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new audio resource for the custom acoustic model. Use a localized name that matches the language of the custom model and reflects the contents of the resource.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an audio resource that has already been added to the custom model.
  • The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file.

    With the curl command, use the --data-binary option to upload the file for the request.

  • For an audio-type resource, the format (MIME type) of the audio. For more information, see Content types for audio-type resources in the method description.

    For an archive-type resource, the media type of the archive file. For more information, see Content types for archive-type resources in the method description.

    Allowable values: [application/zip,application/gzip,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • For an archive-type resource, specify the format of the audio files that are contained in the archive file if they are of type audio/alaw, audio/basic, audio/l16, or audio/mulaw. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type.

    For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph.

    The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see Content types for audio-type resources in the method description.

    For an audio-type resource, omit the header.

    Allowable values: [audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • If true, the specified audio resource overwrites an existing audio resource with the same name. If false, the request fails if an audio resource with the same name already exists. The parameter has no effect if an audio resource with the same name does not already exist.

    Default: false

The addAudio options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new audio resource for the custom acoustic model. Use a localized name that matches the language of the custom model and reflects the contents of the resource.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an audio resource that has already been added to the custom model.
  • The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file.

    With the curl command, use the --data-binary option to upload the file for the request.

  • For an audio-type resource, the format (MIME type) of the audio. For more information, see Content types for audio-type resources in the method description.

    For an archive-type resource, the media type of the archive file. For more information, see Content types for archive-type resources in the method description.

    Allowable values: [application/zip,application/gzip,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • For an archive-type resource, specify the format of the audio files that are contained in the archive file if they are of type audio/alaw, audio/basic, audio/l16, or audio/mulaw. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type.

    For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph.

    The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see Content types for audio-type resources in the method description.

    For an audio-type resource, omit the header.

    Allowable values: [audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • If true, the specified audio resource overwrites an existing audio resource with the same name. If false, the request fails if an audio resource with the same name already exists. The parameter has no effect if an audio resource with the same name does not already exist.

    Default: false

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the new audio resource for the custom acoustic model. Use a localized name that matches the language of the custom model and reflects the contents of the resource.

    • Include a maximum of 128 characters in the name.
    • Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.)
    • Do not use the name of an audio resource that has already been added to the custom model.
  • The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file.

    With the curl command, use the --data-binary option to upload the file for the request.

  • For an audio-type resource, the format (MIME type) of the audio. For more information, see Content types for audio-type resources in the method description.

    For an archive-type resource, the media type of the archive file. For more information, see Content types for archive-type resources in the method description.

    Allowable values: [application/zip,application/gzip,audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • For an archive-type resource, specify the format of the audio files that are contained in the archive file if they are of type audio/alaw, audio/basic, audio/l16, or audio/mulaw. Include the rate, channels, and endianness parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type.

    For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph.

    The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see Content types for audio-type resources in the method description.

    For an audio-type resource, omit the header.

    Allowable values: [audio/alaw,audio/basic,audio/flac,audio/g729,audio/l16,audio/mp3,audio/mpeg,audio/mulaw,audio/ogg,audio/ogg;codecs=opus,audio/ogg;codecs=vorbis,audio/wav,audio/webm,audio/webm;codecs=opus,audio/webm;codecs=vorbis]

  • If true, the specified audio resource overwrites an existing audio resource with the same name. If false, the request fails if an audio resource with the same name already exists. The parameter has no effect if an audio resource with the same name does not already exist.

    Default: false

  • curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/wav" --data-binary @audio1.wav "{url}/v1/acoustic_customizations/{customization_id}/audio/audio1"
  • curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/wav" --data-binary @audio1.wav "{url}/v1/acoustic_customizations/{customization_id}/audio/audio1"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddAudio(
        customizationId: "{customizationId}",
        contentType: "audio/wav",
        audioResource: new MemoryStream(File.ReadAllBytes("audio1.wav")),
        audioName: "audio1"
        );
    
    Console.WriteLine(result.Response);
    // Poll for audio status.
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.AddAudio(
        customizationId: "{customizationId}",
        contentType: "audio/wav",
        audioResource: new MemoryStream(File.ReadAllBytes("audio1.wav")),
        audioName: "audio1"
        );
    
    Console.WriteLine(result.Response);
    // Poll for audio status.
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddAudioOptions addAudioOptions = new AddAudioOptions.Builder()
        .customizationId("{customizationId}")
        .contentType("audio/wav")
        .audioResource(new File("audio1.wav"))
        .audioName("audio1")
        .build();
    
      speechToText.addAudio(addAudioOptions).execute();
      // Poll for audio status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    try {
      AddAudioOptions addAudioOptions = new AddAudioOptions.Builder()
        .customizationId("{customizationId}")
        .contentType("audio/wav")
        .audioResource(new File("audio1.wav"))
        .audioName("audio1")
        .build();
    
      speechToText.addAudio(addAudioOptions).execute();
      // Poll for audio status.
    } catch (FileNotFoundException e) {
      e.printStackTrace();
    }
  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const addAudioParams = {
      customizationId: '{customization_id}',
      contentType: 'audio/wav',
      audioResource: fs.createReadStream('./audio1.wav'),
      audioName: 'audio1',
    };
    
    speechToText.addAudio(addAudioParams)
      .then(result => {
        // Poll for audio status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const fs = require('fs');
    const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const addAudioParams = {
      customizationId: '{customization_id}',
      contentType: 'audio/wav',
      audioResource: fs.createReadStream('audio1.wav'),
      audioName: 'audio1',
    };
    
    speechToText.addAudio(addAudioParams)
      .then(result => {
        // Poll for audio status.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio1.wav'),
                   'rb') as audio_file:
        speech_to_text.add_audio(
            '{customization_id}',
            'audio1',
            audio_file,
            content_type='audio/wav'
        )
    # Poll for audio status.
  • from os.path import join, dirname
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    with open(join(dirname(__file__), './.', 'audio1.wav'),
                   'rb') as audio_file:
        speech_to_text.add_audio(
            '{customization_id}',
            'audio1',
            audio_file,
            content_type='audio/wav'
        )
    # Poll for audio status.

Response

Response type: object

Status Code

  • Created. Addition of the audio resource was successfully started. The service is analyzing the data.

  • Bad Request. A required parameter is null or invalid, the specified customization ID or audio resource is invalid, or the specified audio resource already exists. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Audio file not specified or empty
    • Invalid audio format detected
    • Invalid or missing audio content type
    • Audio '{name}' already exists - change its name, remove existing file before adding new one, or overwrite existing file by setting 'allow_overwrite' flag to 'true'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. The audio resource name includes characters that need to be URL-encoded.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. An internal error prevented the service from satisfying the request. You can also receive status code 500 Forwarding Error if the service is currently busy handling a previous request for the custom model.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Get an audio resource

Gets information about an audio resource from a custom acoustic model. The method returns an AudioListing object whose fields depend on the type of audio resource that you specify with the method's audio_name parameter:

  • For an audio-type resource, the object's fields match those of an AudioResource object: duration, name, details, and status.
  • For an archive-type resource, the object includes a container field whose fields match those of an AudioResource object. It also includes an audio field, which contains an array of AudioResource objects that provides information about the audio files that are contained in the archive.

The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model.

  • For an audio-type resource, the status field is located in the AudioListing object.
  • For an archive-type resource, the status field is located in the AudioResource object that is returned in the container field.

You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Gets information about an audio resource from a custom acoustic model. The method returns an AudioListing object whose fields depend on the type of audio resource that you specify with the method's audio_name parameter:

  • For an audio-type resource, the object's fields match those of an AudioResource object: duration, name, details, and status.
  • For an archive-type resource, the object includes a container field whose fields match those of an AudioResource object. It also includes an audio field, which contains an array of AudioResource objects that provides information about the audio files that are contained in the archive.

The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model.

  • For an audio-type resource, the status field is located in the AudioListing object.
  • For an archive-type resource, the status field is located in the AudioResource object that is returned in the container field.

You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Gets information about an audio resource from a custom acoustic model. The method returns an AudioListing object whose fields depend on the type of audio resource that you specify with the method's audio_name parameter:

  • For an audio-type resource, the object's fields match those of an AudioResource object: duration, name, details, and status.
  • For an archive-type resource, the object includes a container field whose fields match those of an AudioResource object. It also includes an audio field, which contains an array of AudioResource objects that provides information about the audio files that are contained in the archive.

The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model.

  • For an audio-type resource, the status field is located in the AudioListing object.
  • For an archive-type resource, the status field is located in the AudioResource object that is returned in the container field.

You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Gets information about an audio resource from a custom acoustic model. The method returns an AudioListing object whose fields depend on the type of audio resource that you specify with the method's audio_name parameter:

  • For an audio-type resource, the object's fields match those of an AudioResource object: duration, name, details, and status.
  • For an archive-type resource, the object includes a container field whose fields match those of an AudioResource object. It also includes an audio field, which contains an array of AudioResource objects that provides information about the audio files that are contained in the archive.

The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model.

  • For an audio-type resource, the status field is located in the AudioListing object.
  • For an archive-type resource, the status field is located in the AudioResource object that is returned in the container field.

You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

Gets information about an audio resource from a custom acoustic model. The method returns an AudioListing object whose fields depend on the type of audio resource that you specify with the method's audio_name parameter:

  • For an audio-type resource, the object's fields match those of an AudioResource object: duration, name, details, and status.
  • For an archive-type resource, the object includes a container field whose fields match those of an AudioResource object. It also includes an audio field, which contains an array of AudioResource objects that provides information about the audio files that are contained in the archive.

The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model.

  • For an audio-type resource, the status field is located in the AudioListing object.
  • For an archive-type resource, the status field is located in the AudioResource object that is returned in the container field.

You must use credentials for the instance of the service that owns a model to list its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Listing audio resources for a custom acoustic model.

GET /v1/acoustic_customizations/{customization_id}/audio/{audio_name}
GetAudio(string customizationId, string audioName)
ServiceCall<AudioListing> getAudio(GetAudioOptions getAudioOptions)
getAudio(params)
get_audio(
        self,
        customization_id: str,
        audio_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the GetAudioOptions.Builder to create a GetAudioOptions object that contains the parameter values for the getAudio method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

The getAudio options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

  • curl -X GET -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/audio/audio2"
  • curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/audio/audio2"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetAudio(
        customizationId: "{customizationId}",
        audioName: "audio2"
        );
    
    Console.WriteLine(result.Response);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.GetAudio(
        customizationId: "{customizationId}",
        audioName: "audio2"
        );
    
    Console.WriteLine(result.Response);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetAudioOptions getAudioOptions = new GetAudioOptions.Builder()
      .customizationId("{customizationId}")
      .audioName("audio2")
      .build();
    
    AudioListing audioListing =
      speechToText.getAudio(getAudioOptions).execute().getResult();
    System.out.println(audioListing);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    GetAudioOptions getAudioOptions = new GetAudioOptions.Builder()
      .customizationId("{customizationId}")
      .audioName("audio2")
      .build();
    
    AudioListing audioListing =
      speechToText.getAudio(getAudioOptions).execute().getResult();
    System.out.println(audioListing);
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const getAudioParams = {
      customizationId: '{customization_id}',
      audioName: 'audio2',
    };
    
    speechToText.getAudio(getAudioParams)
      .then(audioListing => {
        console.log(JSON.stringify(audioListing, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const getAudioParams = {
      customizationId: '{customization_id}',
      audioName: 'audio2',
    };
    
    speechToText.getAudio(getAudioParams)
      .then(audioListing => {
        console.log(JSON.stringify(audioListing, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    audio_listing = speech_to_text.get_audio(
        '{customization_id}',
        'audio2'
    ).get_result()
    print(json.dumps(audio_listing, indent=2))
  • import json
    from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    audio_listing = speech_to_text.get_audio(
        '{customization_id}',
        'audio2'
    ).get_result()
    print(json.dumps(audio_listing, indent=2))

Response

Information about an audio resource from a custom acoustic model.

Information about an audio resource from a custom acoustic model.

Information about an audio resource from a custom acoustic model.

Information about an audio resource from a custom acoustic model.

Information about an audio resource from a custom acoustic model.

Status Code

  • OK. The request succeeded.

  • Bad Request. The specified customization ID or audio resource name is invalid, including the case where the audio resource does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for audio name '{audio_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {
      "container": {
        "duration": 556,
        "name": "audio2",
        "details": {
          "type": "archive",
          "compression": "zip"
        },
        "status": "ok"
      },
      "audio": [
        {
          "duration": 121,
          "name": "audio-file1.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 133,
          "name": "audio-file2.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 112,
          "name": "audio-file3.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 129,
          "name": "audio-file4.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 61,
          "name": "audio-file5.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        }
      ]
    }
  • {
      "container": {
        "duration": 556,
        "name": "audio2",
        "details": {
          "type": "archive",
          "compression": "zip"
        },
        "status": "ok"
      },
      "audio": [
        {
          "duration": 121,
          "name": "audio-file1.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 133,
          "name": "audio-file2.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 112,
          "name": "audio-file3.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 129,
          "name": "audio-file4.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        },
        {
          "duration": 61,
          "name": "audio-file5.wav",
          "details": {
            "codec": "pcm_s16le",
            "type": "audio",
            "frequency": 16000
          },
          "status": "ok"
        }
      ]
    }

Delete an audio resource

Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource.

Removing an audio resource does not affect the custom model until you train the model on its updated data by using the Train a custom acoustic model method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting an audio resource from a custom acoustic model.

Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource.

Removing an audio resource does not affect the custom model until you train the model on its updated data by using the Train a custom acoustic model method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting an audio resource from a custom acoustic model.

Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource.

Removing an audio resource does not affect the custom model until you train the model on its updated data by using the Train a custom acoustic model method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting an audio resource from a custom acoustic model.

Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource.

Removing an audio resource does not affect the custom model until you train the model on its updated data by using the Train a custom acoustic model method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting an audio resource from a custom acoustic model.

Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource.

Removing an audio resource does not affect the custom model until you train the model on its updated data by using the Train a custom acoustic model method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources.

Note: Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models.

See also: Deleting an audio resource from a custom acoustic model.

DELETE /v1/acoustic_customizations/{customization_id}/audio/{audio_name}
DeleteAudio(string customizationId, string audioName)
ServiceCall<Void> deleteAudio(DeleteAudioOptions deleteAudioOptions)
deleteAudio(params)
delete_audio(
        self,
        customization_id: str,
        audio_name: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteAudioOptions.Builder to create a DeleteAudioOptions object that contains the parameter values for the deleteAudio method.

Path Parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

The deleteAudio options.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

parameters

  • The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with credentials for the instance of the service that owns the custom model.

  • The name of the audio resource for the custom acoustic model.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/acoustic_customizations/{customization_id}/audio/audio1"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations/{customization_id}/audio/audio1"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteAudio(
        customizationId: "{customizationId}",
        audioName: "audio1"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteAudio(
        customizationId: "{customizationId}",
        audioName: "audio1"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteAudioOptions deleteAudioOptions = new DeleteAudioOptions.Builder()
      .customizationId("{customizationId}")
      .audioName("audio1")
      .build();
    
    speechToText.deleteAudio(deleteAudioOptions).execute();
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteAudioOptions deleteAudioOptions = new DeleteAudioOptions.Builder()
      .customizationId("{customizationId}")
      .audioName("audio1")
      .build();
    
    speechToText.deleteAudio(deleteAudioOptions).execute();
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteAudioParams = {
      customizationId: '{customization_id}',
      audioName: 'audio1',
    };
    
    speechToText.deleteAudio(deleteAudioParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteAudioParams = {
      customizationId: '{customization_id}',
      audioName: 'audio1',
    };
    
    speechToText.deleteAudio(deleteAudioParams)
      .then(result => {
        console.log(JSON.stringify(result, null, 2));
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_audio(
        '{customization_id}',
        'audio1'
    )
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_audio(
        '{customization_id}',
        'audio1'
    )

Response

Response type: object

Status Code

  • OK. The audio resource was successfully deleted from the custom acoustic model.

  • Bad Request. The specified customization ID or audio resource name is invalid, including the case where the audio resource does not exist for the custom model. Specific failure messages include:

    • Malformed GUID: '{customization_id}'
    • Invalid value for audio name '{audio_name}'
  • Unauthorized. The specified credentials are invalid or the specified customization ID is invalid for the requesting credentials:

    • Invalid customization_id '{customization_id}' for user
  • Method Not Allowed. No audio resource name was specified with the request.

  • Conflict. The service is currently busy handling a previous request for the custom model:

    • Customization '{customization_id}' is currently locked to process your last request.
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

Example responses
  • {}
  • {}

Delete labeled data

Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the X-Watson-Metadata header with a request that passes the data.

Note: If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests.

See also: Information security.

Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the X-Watson-Metadata header with a request that passes the data.

Note: If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests.

See also: Information security.

Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the X-Watson-Metadata header with a request that passes the data.

Note: If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests.

See also: Information security.

Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the X-Watson-Metadata header with a request that passes the data.

Note: If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests.

See also: Information security.

Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the X-Watson-Metadata header with a request that passes the data.

Note: If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests.

See also: Information security.

DELETE /v1/user_data
DeleteUserData(string customerId)
ServiceCall<Void> deleteUserData(DeleteUserDataOptions deleteUserDataOptions)
deleteUserData(params)
delete_user_data(
        self,
        customer_id: str,
        **kwargs,
    ) -> DetailedResponse

Request

Use the DeleteUserDataOptions.Builder to create a DeleteUserDataOptions object that contains the parameter values for the deleteUserData method.

Query Parameters

  • The customer ID for which all data is to be deleted.

parameters

  • The customer ID for which all data is to be deleted.

The deleteUserData options.

parameters

  • The customer ID for which all data is to be deleted.

parameters

  • The customer ID for which all data is to be deleted.

  • curl -X DELETE -u "apikey:{apikey}" "{url}/v1/user_data?customer_id={customer_ID}"
  • curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/user_data?customer_id={customer_ID}"
  • IamAuthenticator authenticator = new IamAuthenticator(
        apikey: "{apikey}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteUserData(
        customerId: "{customerId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator(
        url: "https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize",
        username: "{username}",
        password: "{password}"
        );
    
    SpeechToTextService speechToText = new SpeechToTextService(authenticator);
    speechToText.SetServiceUrl("{url}");
    
    var result = speechToText.DeleteUserData(
        customerId: "{customerId}"
        );
    
    Console.WriteLine(result.StatusCode);
  • IamAuthenticator authenticator = new IamAuthenticator("{apikey}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteUserDataOptions deleteUserDataOptions = new DeleteUserDataOptions.Builder()
      .customerId("{customerId}")
      .build();
    
    speechToText.deleteUserData(deleteUserDataOptions).execute();
    
  • CloudPakForDataAuthenticator authenticator = new CloudPakForDataAuthenticator("https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize", "{username}", "{password}");
    SpeechToText speechToText = new SpeechToText(authenticator);
    speechToText.setServiceUrl("{url}");
    
    DeleteUserDataOptions deleteUserDataOptions = new DeleteUserDataOptions.Builder()
      .customerId("{customerId}")
      .build();
    
    speechToText.deleteUserData(deleteUserDataOptions).execute();
    
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { IamAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new IamAuthenticator({
        apikey: '{apikey}',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteUserDataParams = {
      customerId: '{customer_id}',
    };
    
    speechToText.deleteUserData(deleteUserDataParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • const SpeechToTextV1 = require('ibm-watson/speech-to-text/v1');
    const { CloudPakForDataAuthenticator } = require('ibm-watson/auth');
    
    const speechToText = new SpeechToTextV1({
      authenticator: new CloudPakForDataAuthenticator({
        username: '{username}',
        password: '{password}',
        url: 'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize',
      }),
      serviceUrl: '{url}',
    });
    
    const deleteUserDataParams = {
      customerId: '{customer_id}',
    };
    
    speechToText.deleteUserData(deleteUserDataParams)
      .then(result => {
        // Response is empty.
      })
      .catch(err => {
        console.log('error:', err);
      });
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
    
    authenticator = IAMAuthenticator('{apikey}')
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_user_data('{customer_id}')
  • from ibm_watson import SpeechToTextV1
    from ibm_cloud_sdk_core.authenticators import CloudPakForDataAuthenticator
    
    authenticator = CloudPakForDataAuthenticator(
        '{username}',
        '{password}',
        'https://{cpd_cluster_host}{:port}/icp4d-api/v1/authorize'
    )
    
    speech_to_text = SpeechToTextV1(
        authenticator=authenticator
    )
    
    speech_to_text.set_service_url('{url}')
    
    speech_to_text.delete_user_data('{customer_id}')

Response

Response type: object

Status Code

  • OK. The deletion request was successfully submitted.

  • Bad Request. The request did not pass a customer ID:

    • No customer ID found in the request
  • Internal Server Error. The service experienced an internal error.

  • Service Unavailable. The service is currently unavailable.

No Sample Response

This method does not specify any sample responses.