ibm_watson.speech_to_text_v1 module¶
The IBM® 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. For most languages, the service supports two sampling rates, broadband and narrowband. It returns all JSON response content in the UTF-8 character set. 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 is generally available for production use with most supported languages. Acoustic model customization is beta functionality that is available for all supported languages.
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class
SpeechToTextV1(authenticator=None)[source]¶ Bases:
ibm_cloud_sdk_core.base_service.BaseServiceThe Speech to Text V1 service.
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default_service_url= 'https://stream.watsonplatform.net/speech-to-text/api'¶
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list_models(**kwargs)[source]¶ 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. See also: [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
- Parameters
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
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get_model(model_id, **kwargs)[source]¶ 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: [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
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recognize(audio, *, content_type=None, model=None, language_customization_id=None, acoustic_customization_id=None, base_model_version=None, customization_weight=None, inactivity_timeout=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, customization_id=None, grammar_name=None, redaction=None, audio_metrics=None, **kwargs)[source]¶ 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-http#HTTP-basic). ### 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 transmission](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#transmission) * [Timeouts](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts) ### 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: [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#audio-formats). ### Multipart speech recognition
Note: The 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-http#HTTP-multi).
- Parameters
audio (file) – The audio to transcribe.
content_type (str) – (optional) The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.
model (str) – (optional) The identifier of the model that is to be used for the recognition request. See [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
language_customization_id (str) – (optional) 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input). Note: Use this parameter instead of the deprecated customization_id parameter.
acoustic_customization_id (str) – (optional) 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
base_model_version (str) – (optional) 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 [Base model version](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#version).
customization_weight (float) – (optional) 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 it was trained, the default value is 0.3. 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
inactivity_timeout (int) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts-inactivity).
keywords (list[str]) – (optional) 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. You can spot a maximum of 1000 keywords. Omit the parameter or specify an empty array if you do not need to spot keywords. See [Keyword spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
keywords_threshold (float) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
max_alternatives (int) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#max_alternatives).
word_alternatives_threshold (float) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_alternatives).
word_confidence (bool) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_confidence).
timestamps (bool) – (optional) If true, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_timestamps).
profanity_filter (bool) – (optional) 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. Applies to US English transcription only. See [Profanity filtering](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#profanity_filter).
smart_formatting (bool) – (optional) 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: Applies to US English, Japanese, and Spanish transcription only. See [Smart formatting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#smart_formatting).
speaker_labels (bool) – (optional) 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. Note: Applies to US English, Japanese, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. To determine whether a language model supports speaker labels, you can also use the Get a model method and check that the attribute speaker_labels is set to true. See [Speaker labels](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#speaker_labels).
customization_id (str) – (optional) Deprecated. Use the language_customization_id parameter to specify the customization ID (GUID) of a custom language model that is to be used with the recognition request. Do not specify both parameters with a request.
grammar_name (str) – (optional) 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 [Grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#grammars-input).
redaction (bool) – (optional) 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: Applies to US English, Japanese, and Korean transcription only. See [Numeric redaction](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#redaction).
audio_metrics (bool) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
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register_callback(callback_url, *, user_secret=None, **kwargs)[source]¶ Register a callback.
Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or white-list, 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 white-list the URL; it instead sends status code 400 in response to the Register a callback request. If the requested callback URL is already white-listed, 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#register).
- Parameters
callback_url (str) – An HTTP or HTTPS URL to which callback notifications are to be sent. To be white-listed, 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.
user_secret (str) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
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unregister_callback(callback_url, **kwargs)[source]¶ Unregister a callback.
Unregisters a callback URL that was previously white-listed 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#unregister).
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create_job(audio, *, content_type=None, model=None, callback_url=None, events=None, user_token=None, results_ttl=None, language_customization_id=None, acoustic_customization_id=None, base_model_version=None, customization_weight=None, inactivity_timeout=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, customization_id=None, grammar_name=None, redaction=None, processing_metrics=None, processing_metrics_interval=None, audio_metrics=None, **kwargs)[source]¶ 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#create). ### 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 transmission](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#transmission) * [Timeouts](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts) ### 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: [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#audio-formats).
- Parameters
audio (file) – The audio to transcribe.
content_type (str) – (optional) The format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.
model (str) – (optional) The identifier of the model that is to be used for the recognition request. See [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
callback_url (str) – (optional) A URL to which callback notifications are to be sent. The URL must already be successfully white-listed 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.
events (str) – (optional) 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.
user_token (str) – (optional) 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.
results_ttl (int) – (optional) 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.
language_customization_id (str) – (optional) 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input). Note: Use this parameter instead of the deprecated customization_id parameter.
acoustic_customization_id (str) – (optional) 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
base_model_version (str) – (optional) 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 [Base model version](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#version).
customization_weight (float) – (optional) 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 it was trained, the default value is 0.3. 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 [Custom models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
inactivity_timeout (int) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts-inactivity).
keywords (list[str]) – (optional) 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. You can spot a maximum of 1000 keywords. Omit the parameter or specify an empty array if you do not need to spot keywords. See [Keyword spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
keywords_threshold (float) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
max_alternatives (int) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#max_alternatives).
word_alternatives_threshold (float) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_alternatives).
word_confidence (bool) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_confidence).
timestamps (bool) – (optional) If true, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_timestamps).
profanity_filter (bool) – (optional) 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. Applies to US English transcription only. See [Profanity filtering](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#profanity_filter).
smart_formatting (bool) – (optional) 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: Applies to US English, Japanese, and Spanish transcription only. See [Smart formatting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#smart_formatting).
speaker_labels (bool) – (optional) 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. Note: Applies to US English, Japanese, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. To determine whether a language model supports speaker labels, you can also use the Get a model method and check that the attribute speaker_labels is set to true. See [Speaker labels](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#speaker_labels).
customization_id (str) – (optional) Deprecated. Use the language_customization_id parameter to specify the customization ID (GUID) of a custom language model that is to be used with the recognition request. Do not specify both parameters with a request.
grammar_name (str) – (optional) 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 [Grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#grammars-input).
redaction (bool) – (optional) 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: Applies to US English, Japanese, and Korean transcription only. See [Numeric redaction](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#redaction).
processing_metrics (bool) – (optional) 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.
processing_metrics_interval (float) – (optional) 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.
audio_metrics (bool) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
check_jobs(**kwargs)[source]¶ 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 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#jobs).
- Parameters
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
check_job(id, **kwargs)[source]¶ 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#job).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_job(id, **kwargs)[source]¶ 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#delete-async).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
create_language_model(name, base_model_name, *, dialect=None, description=None, **kwargs)[source]¶ 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 credential. 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. See also: [Create a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#createModel-language).
- Parameters
name (str) – A user-defined name for the new custom language model. Use a name that is unique among all custom language models that you own. 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.
base_model_name (str) – 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customization#languageSupport).
dialect (str) – (optional) The dialect of the specified language that is to be used with the custom language model. For most languages, the dialect matches the language of the base model by default. For example, en-US is used for either of the US English language models. For a Spanish language, the service creates a custom language model that is suited for speech in one of the following dialects: * 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) The parameter is meaningful only for Spanish models, for which you can always safely omit the parameter to have the service create the correct mapping. If you specify the dialect parameter for non-Spanish language models, its value must match the language of the base model. If you specify the dialect for Spanish language models, its value must match one of the defined mappings as indicated (es-ES, es-LA, or es-MX). All dialect values are case-insensitive.
description (str) – (optional) A description of the new custom language model. Use a localized description that matches the language of the custom model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_language_models(*, language=None, **kwargs)[source]¶ 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: [Listing custom language models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language).
- Parameters
language (str) – (optional) The identifier of the language for which custom language or custom acoustic models are to be returned (for example, en-US). Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_language_model(customization_id, **kwargs)[source]¶ 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: [Listing custom language models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_language_model(customization_id, **kwargs)[source]¶ 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: [Deleting a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#deleteModel-language).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
train_language_model(customization_id, *, word_type_to_add=None, customization_weight=None, **kwargs)[source]¶ 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. 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. See also: [Train the custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#trainModel-language). ### 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.
- Parameters
customization_id (str) – 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.
word_type_to_add (str) – (optional) 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 new words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.
customization_weight (float) – (optional) 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 is 0.3. 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
reset_language_model(customization_id, **kwargs)[source]¶ 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: [Resetting a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#resetModel-language).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
upgrade_language_model(customization_id, **kwargs)[source]¶ 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. See also: [Upgrading a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeLanguage).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_corpora(customization_id, **kwargs)[source]¶ List corpora.
Lists information about all corpora from a custom language model. The information includes the total number of words and out-of-vocabulary (OOV) words, name, and status of each 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#listCorpora).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
add_corpus(customization_id, corpus_name, corpus_file, *, allow_overwrite=None, **kwargs)[source]¶ 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 extract 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 the contents of the corpus and automatically extracts new words that it finds. This can take on the order of a minute or two to complete depending on the total number of words and the number of new words in the corpus, 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 corpus for the current request completes. Use the List a corpus method to check the status of the analysis. The service auto-populates the model’s words resource with words from the corpus that are not found in its 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. You can use other words method to eliminate typos and modify how words are pronounced 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 OOV words anew. Before doing so, it removes any OOV words 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. Also, 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: * [Working with corpora](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingCorpora) * [Add a corpus to the custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addCorpus).
- Parameters
customization_id (str) – 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.
corpus_name (str) – 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.
corpus_file (file) – 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 encoding when working with the words in the custom language model. For more information, see [Character encoding](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding). With the curl command, use the –data-binary option to upload the file for the request.
allow_overwrite (bool) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_corpus(customization_id, corpus_name, **kwargs)[source]¶ Get a corpus.
Gets information about a corpus from a custom language model. The information includes the total number of words and out-of-vocabulary (OOV) words, name, and status of 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#listCorpora).
- Parameters
customization_id (str) – 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.
corpus_name (str) – The name of the corpus for the custom language model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_corpus(customization_id, corpus_name, **kwargs)[source]¶ Delete a corpus.
Deletes an existing corpus from a custom language model. 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. 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. See also: [Deleting a corpus from a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#deleteCorpus).
- Parameters
customization_id (str) – 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.
corpus_name (str) – The name of the corpus for the custom language model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_words(customization_id, *, word_type=None, sort=None, **kwargs)[source]¶ 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 only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#listWords).
- Parameters
customization_id (str) – 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.
word_type (str) – (optional) 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.
sort (str) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
add_words(customization_id, words, **kwargs)[source]¶ Add custom words.
Adds one or more custom words to a custom language model. 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 add additional words or to modify existing words in the words resource. 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. You must 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 sounds_like and display_as fields for each word. * 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. * 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(trademark) is to be displayed as IBM™. 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 List 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 List 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: * [Working with custom words](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingWords) * [Add words to the custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addWords).
- Parameters
customization_id (str) – 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.
words (list[CustomWord]) – 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
add_word(customization_id, word_name, *, word=None, sounds_like=None, display_as=None, **kwargs)[source]¶ Add a custom word.
Adds a custom word to a custom language model. 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 add a word or to modify an existing word in the words resource. 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 sounds_like and display_as fields for the word. * 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. * 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(trademark) is to be displayed as IBM™. 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 List a custom word method to review the word that you add. See also: * [Working with custom words](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingWords) * [Add words to the custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addWords).
- Parameters
customization_id (str) – 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.
word_name (str) – The custom word that is to be added to or updated in the custom language model. Do not include spaces in the word. 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
word (str) – (optional) 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 include spaces in the word. Use a - (dash) or _ (underscore) to connect the tokens of compound words. Omit this parameter for the Add a custom word method.
sounds_like (list[str]) – (optional) An 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 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 spaces.
display_as (str) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_word(customization_id, word_name, **kwargs)[source]¶ 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#listWords).
- Parameters
customization_id (str) – 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.
word_name (str) – 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_word(customization_id, word_name, **kwargs)[source]¶ 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 only the custom pronunciation for the word; 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#deleteWord).
- Parameters
customization_id (str) – 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.
word_name (str) – 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_grammars(customization_id, **kwargs)[source]¶ List grammars.
Lists information about all grammars from a custom language model. The information includes the total number of out-of-vocabulary (OOV) words, name, and status of each grammar. You must use credentials for the instance of the service that owns a model to list its grammars. See also: [Listing grammars from a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
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add_grammar(customization_id, grammar_name, grammar_file, content_type, *, allow_overwrite=None, **kwargs)[source]¶ 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 can take a few seconds 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. 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. 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. 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: * [Understanding grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-grammarUnderstand#grammarUnderstand) * [Add a grammar to the custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-grammarAdd#addGrammar).
- Parameters
customization_id (str) – 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.
grammar_name (str) – 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.
grammar_file (str) – 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.
content_type (str) – 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.
allow_overwrite (bool) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_grammar(customization_id, grammar_name, **kwargs)[source]¶ Get a grammar.
Gets information about a grammar from a custom language model. The information includes the total number of out-of-vocabulary (OOV) words, name, and status of the grammar. You must use credentials for the instance of the service that owns a model to list its grammars. See also: [Listing grammars from a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars).
- Parameters
customization_id (str) – 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.
grammar_name (str) – The name of the grammar for the custom language model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_grammar(customization_id, grammar_name, **kwargs)[source]¶ Delete a grammar.
Deletes an existing grammar from a custom language model. 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: [Deleting a grammar from a custom language model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#deleteGrammar).
- Parameters
customization_id (str) – 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.
grammar_name (str) – The name of the grammar for the custom language model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
create_acoustic_model(name, base_model_name, *, description=None, **kwargs)[source]¶ 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 credential. 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. See also: [Create a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#createModel-acoustic).
- Parameters
name (str) – A user-defined name for the new custom acoustic model. Use a name that is unique among all custom acoustic models that you own. 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.
base_model_name (str) – 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customization#languageSupport).
description (str) – (optional) A description of the new custom acoustic model. Use a localized description that matches the language of the custom model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_acoustic_models(*, language=None, **kwargs)[source]¶ 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. See also: [Listing custom acoustic models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic).
- Parameters
language (str) – (optional) The identifier of the language for which custom language or custom acoustic models are to be returned (for example, en-US). Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_acoustic_model(customization_id, **kwargs)[source]¶ 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. See also: [Listing custom acoustic models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_acoustic_model(customization_id, **kwargs)[source]¶ 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. See also: [Deleting a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#deleteModel-acoustic).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
train_acoustic_model(customization_id, *, custom_language_model_id=None, **kwargs)[source]¶ 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. It can take on the order of minutes or hours to complete depending on the total amount of audio data on which the custom acoustic model is being trained and the current load on the service. Typically, training a custom acoustic model takes approximately two to four times the length of its audio data. The range of time depends on the model being trained and the nature of the audio, such as whether the audio is clean or noisy. 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. Both of the custom models must be based on the same version of the same base model for training to succeed. See also: * [Train the custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#trainModel-acoustic) * [Using custom acoustic and custom language models together](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-useBoth#useBoth) ### 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 or more than 200 hours of audio data. * 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.
- Parameters
customization_id (str) – 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.
custom_language_model_id (str) – (optional) 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. The credentials specified with the request must own both custom models.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
reset_acoustic_model(customization_id, **kwargs)[source]¶ 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. See also: [Resetting a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#resetModel-acoustic).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
upgrade_acoustic_model(customization_id, *, custom_language_model_id=None, force=None, **kwargs)[source]¶ 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. See also: [Upgrading a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeAcoustic).
- Parameters
customization_id (str) – 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.
custom_language_model_id (str) – (optional) 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 credentials specified with the request must own both custom models.
force (bool) – (optional) 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](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeAcoustic).
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
list_audio(customization_id, **kwargs)[source]¶ 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. See also: [Listing audio resources for a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#listAudio).
- Parameters
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
add_audio(customization_id, audio_name, audio_resource, *, content_type=None, contained_content_type=None, allow_overwrite=None, **kwargs)[source]¶ 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 and a maximum of 200 hours 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. The method is asynchronous. It can take several seconds 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. See also: [Add audio to the custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#addAudio). ### 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: [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#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).
- Parameters
customization_id (str) – 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.
audio_name (str) – 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.
audio_resource (file) – 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.
content_type (str) – (optional) 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.
contained_content_type (str) – (optional) 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.
allow_overwrite (bool) – (optional) 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.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
get_audio(customization_id, audio_name, **kwargs)[source]¶ 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. See also: [Listing audio resources for a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#listAudio).
- Parameters
customization_id (str) – 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.
audio_name (str) – The name of the audio resource for the custom acoustic model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_audio(customization_id, audio_name, **kwargs)[source]¶ 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. See also: [Deleting an audio resource from a custom acoustic model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#deleteAudio).
- Parameters
customization_id (str) – 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.
audio_name (str) – The name of the audio resource for the custom acoustic model.
headers (dict) – A dict containing the request headers
- Returns
A DetailedResponse containing the result, headers and HTTP status code.
- Return type
DetailedResponse
-
delete_user_data(customer_id, **kwargs)[source]¶ 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. See also: [Information security](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-information-security#information-security).
-
-
class
GetModelEnums[source]¶ Bases:
object-
class
ModelId[source]¶ Bases:
enum.EnumThe identifier of the model in the form of its name from the output of the Get a model method.
-
AR_AR_BROADBANDMODEL= 'ar-AR_BroadbandModel'¶
-
DE_DE_BROADBANDMODEL= 'de-DE_BroadbandModel'¶
-
DE_DE_NARROWBANDMODEL= 'de-DE_NarrowbandModel'¶
-
EN_GB_BROADBANDMODEL= 'en-GB_BroadbandModel'¶
-
EN_GB_NARROWBANDMODEL= 'en-GB_NarrowbandModel'¶
-
EN_US_BROADBANDMODEL= 'en-US_BroadbandModel'¶
-
EN_US_NARROWBANDMODEL= 'en-US_NarrowbandModel'¶
-
EN_US_SHORTFORM_NARROWBANDMODEL= 'en-US_ShortForm_NarrowbandModel'¶
-
ES_AR_BROADBANDMODEL= 'es-AR_BroadbandModel'¶
-
ES_AR_NARROWBANDMODEL= 'es-AR_NarrowbandModel'¶
-
ES_CL_BROADBANDMODEL= 'es-CL_BroadbandModel'¶
-
ES_CL_NARROWBANDMODEL= 'es-CL_NarrowbandModel'¶
-
ES_CO_BROADBANDMODEL= 'es-CO_BroadbandModel'¶
-
ES_CO_NARROWBANDMODEL= 'es-CO_NarrowbandModel'¶
-
ES_ES_BROADBANDMODEL= 'es-ES_BroadbandModel'¶
-
ES_ES_NARROWBANDMODEL= 'es-ES_NarrowbandModel'¶
-
ES_MX_BROADBANDMODEL= 'es-MX_BroadbandModel'¶
-
ES_MX_NARROWBANDMODEL= 'es-MX_NarrowbandModel'¶
-
ES_PE_BROADBANDMODEL= 'es-PE_BroadbandModel'¶
-
ES_PE_NARROWBANDMODEL= 'es-PE_NarrowbandModel'¶
-
FR_FR_BROADBANDMODEL= 'fr-FR_BroadbandModel'¶
-
FR_FR_NARROWBANDMODEL= 'fr-FR_NarrowbandModel'¶
-
JA_JP_BROADBANDMODEL= 'ja-JP_BroadbandModel'¶
-
JA_JP_NARROWBANDMODEL= 'ja-JP_NarrowbandModel'¶
-
KO_KR_BROADBANDMODEL= 'ko-KR_BroadbandModel'¶
-
KO_KR_NARROWBANDMODEL= 'ko-KR_NarrowbandModel'¶
-
PT_BR_BROADBANDMODEL= 'pt-BR_BroadbandModel'¶
-
PT_BR_NARROWBANDMODEL= 'pt-BR_NarrowbandModel'¶
-
ZH_CN_BROADBANDMODEL= 'zh-CN_BroadbandModel'¶
-
ZH_CN_NARROWBANDMODEL= 'zh-CN_NarrowbandModel'¶
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-
class
-
class
RecognizeEnums[source]¶ Bases:
object-
class
ContentType[source]¶ Bases:
enum.EnumThe format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.
-
APPLICATION_OCTET_STREAM= 'application/octet-stream'¶
-
AUDIO_ALAW= 'audio/alaw'¶
-
AUDIO_BASIC= 'audio/basic'¶
-
AUDIO_FLAC= 'audio/flac'¶
-
AUDIO_G729= 'audio/g729'¶
-
AUDIO_L16= 'audio/l16'¶
-
AUDIO_MP3= 'audio/mp3'¶
-
AUDIO_MPEG= 'audio/mpeg'¶
-
AUDIO_MULAW= 'audio/mulaw'¶
-
AUDIO_OGG= 'audio/ogg'¶
-
AUDIO_OGG_CODECS_OPUS= 'audio/ogg;codecs=opus'¶
-
AUDIO_OGG_CODECS_VORBIS= 'audio/ogg;codecs=vorbis'¶
-
AUDIO_WAV= 'audio/wav'¶
-
AUDIO_WEBM= 'audio/webm'¶
-
AUDIO_WEBM_CODECS_OPUS= 'audio/webm;codecs=opus'¶
-
AUDIO_WEBM_CODECS_VORBIS= 'audio/webm;codecs=vorbis'¶
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-
class
Model[source]¶ Bases:
enum.EnumThe identifier of the model that is to be used for the recognition request. See [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
-
AR_AR_BROADBANDMODEL= 'ar-AR_BroadbandModel'¶
-
DE_DE_BROADBANDMODEL= 'de-DE_BroadbandModel'¶
-
DE_DE_NARROWBANDMODEL= 'de-DE_NarrowbandModel'¶
-
EN_GB_BROADBANDMODEL= 'en-GB_BroadbandModel'¶
-
EN_GB_NARROWBANDMODEL= 'en-GB_NarrowbandModel'¶
-
EN_US_BROADBANDMODEL= 'en-US_BroadbandModel'¶
-
EN_US_NARROWBANDMODEL= 'en-US_NarrowbandModel'¶
-
EN_US_SHORTFORM_NARROWBANDMODEL= 'en-US_ShortForm_NarrowbandModel'¶
-
ES_AR_BROADBANDMODEL= 'es-AR_BroadbandModel'¶
-
ES_AR_NARROWBANDMODEL= 'es-AR_NarrowbandModel'¶
-
ES_CL_BROADBANDMODEL= 'es-CL_BroadbandModel'¶
-
ES_CL_NARROWBANDMODEL= 'es-CL_NarrowbandModel'¶
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ES_CO_BROADBANDMODEL= 'es-CO_BroadbandModel'¶
-
ES_CO_NARROWBANDMODEL= 'es-CO_NarrowbandModel'¶
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ES_ES_BROADBANDMODEL= 'es-ES_BroadbandModel'¶
-
ES_ES_NARROWBANDMODEL= 'es-ES_NarrowbandModel'¶
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ES_MX_BROADBANDMODEL= 'es-MX_BroadbandModel'¶
-
ES_MX_NARROWBANDMODEL= 'es-MX_NarrowbandModel'¶
-
ES_PE_BROADBANDMODEL= 'es-PE_BroadbandModel'¶
-
ES_PE_NARROWBANDMODEL= 'es-PE_NarrowbandModel'¶
-
FR_FR_BROADBANDMODEL= 'fr-FR_BroadbandModel'¶
-
FR_FR_NARROWBANDMODEL= 'fr-FR_NarrowbandModel'¶
-
JA_JP_BROADBANDMODEL= 'ja-JP_BroadbandModel'¶
-
JA_JP_NARROWBANDMODEL= 'ja-JP_NarrowbandModel'¶
-
KO_KR_BROADBANDMODEL= 'ko-KR_BroadbandModel'¶
-
KO_KR_NARROWBANDMODEL= 'ko-KR_NarrowbandModel'¶
-
PT_BR_BROADBANDMODEL= 'pt-BR_BroadbandModel'¶
-
PT_BR_NARROWBANDMODEL= 'pt-BR_NarrowbandModel'¶
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ZH_CN_BROADBANDMODEL= 'zh-CN_BroadbandModel'¶
-
ZH_CN_NARROWBANDMODEL= 'zh-CN_NarrowbandModel'¶
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-
class
-
class
CreateJobEnums[source]¶ Bases:
object-
class
ContentType[source]¶ Bases:
enum.EnumThe format (MIME type) of the audio. For more information about specifying an audio format, see Audio formats (content types) in the method description.
-
APPLICATION_OCTET_STREAM= 'application/octet-stream'¶
-
AUDIO_ALAW= 'audio/alaw'¶
-
AUDIO_BASIC= 'audio/basic'¶
-
AUDIO_FLAC= 'audio/flac'¶
-
AUDIO_G729= 'audio/g729'¶
-
AUDIO_L16= 'audio/l16'¶
-
AUDIO_MP3= 'audio/mp3'¶
-
AUDIO_MPEG= 'audio/mpeg'¶
-
AUDIO_MULAW= 'audio/mulaw'¶
-
AUDIO_OGG= 'audio/ogg'¶
-
AUDIO_OGG_CODECS_OPUS= 'audio/ogg;codecs=opus'¶
-
AUDIO_OGG_CODECS_VORBIS= 'audio/ogg;codecs=vorbis'¶
-
AUDIO_WAV= 'audio/wav'¶
-
AUDIO_WEBM= 'audio/webm'¶
-
AUDIO_WEBM_CODECS_OPUS= 'audio/webm;codecs=opus'¶
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AUDIO_WEBM_CODECS_VORBIS= 'audio/webm;codecs=vorbis'¶
-
-
class
Model[source]¶ Bases:
enum.EnumThe identifier of the model that is to be used for the recognition request. See [Languages and models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
-
AR_AR_BROADBANDMODEL= 'ar-AR_BroadbandModel'¶
-
DE_DE_BROADBANDMODEL= 'de-DE_BroadbandModel'¶
-
DE_DE_NARROWBANDMODEL= 'de-DE_NarrowbandModel'¶
-
EN_GB_BROADBANDMODEL= 'en-GB_BroadbandModel'¶
-
EN_GB_NARROWBANDMODEL= 'en-GB_NarrowbandModel'¶
-
EN_US_BROADBANDMODEL= 'en-US_BroadbandModel'¶
-
EN_US_NARROWBANDMODEL= 'en-US_NarrowbandModel'¶
-
EN_US_SHORTFORM_NARROWBANDMODEL= 'en-US_ShortForm_NarrowbandModel'¶
-
ES_AR_BROADBANDMODEL= 'es-AR_BroadbandModel'¶
-
ES_AR_NARROWBANDMODEL= 'es-AR_NarrowbandModel'¶
-
ES_CL_BROADBANDMODEL= 'es-CL_BroadbandModel'¶
-
ES_CL_NARROWBANDMODEL= 'es-CL_NarrowbandModel'¶
-
ES_CO_BROADBANDMODEL= 'es-CO_BroadbandModel'¶
-
ES_CO_NARROWBANDMODEL= 'es-CO_NarrowbandModel'¶
-
ES_ES_BROADBANDMODEL= 'es-ES_BroadbandModel'¶
-
ES_ES_NARROWBANDMODEL= 'es-ES_NarrowbandModel'¶
-
ES_MX_BROADBANDMODEL= 'es-MX_BroadbandModel'¶
-
ES_MX_NARROWBANDMODEL= 'es-MX_NarrowbandModel'¶
-
ES_PE_BROADBANDMODEL= 'es-PE_BroadbandModel'¶
-
ES_PE_NARROWBANDMODEL= 'es-PE_NarrowbandModel'¶
-
FR_FR_BROADBANDMODEL= 'fr-FR_BroadbandModel'¶
-
FR_FR_NARROWBANDMODEL= 'fr-FR_NarrowbandModel'¶
-
JA_JP_BROADBANDMODEL= 'ja-JP_BroadbandModel'¶
-
JA_JP_NARROWBANDMODEL= 'ja-JP_NarrowbandModel'¶
-
KO_KR_BROADBANDMODEL= 'ko-KR_BroadbandModel'¶
-
KO_KR_NARROWBANDMODEL= 'ko-KR_NarrowbandModel'¶
-
PT_BR_BROADBANDMODEL= 'pt-BR_BroadbandModel'¶
-
PT_BR_NARROWBANDMODEL= 'pt-BR_NarrowbandModel'¶
-
ZH_CN_BROADBANDMODEL= 'zh-CN_BroadbandModel'¶
-
ZH_CN_NARROWBANDMODEL= 'zh-CN_NarrowbandModel'¶
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-
class
Events[source]¶ Bases:
enum.EnumIf 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.
-
RECOGNITIONS_STARTED= 'recognitions.started'¶
-
RECOGNITIONS_COMPLETED= 'recognitions.completed'¶
-
RECOGNITIONS_COMPLETED_WITH_RESULTS= 'recognitions.completed_with_results'¶
-
RECOGNITIONS_FAILED= 'recognitions.failed'¶
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-
class
-
class
TrainLanguageModelEnums[source]¶ Bases:
object-
class
WordTypeToAdd[source]¶ Bases:
enum.EnumThe 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 new words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars.
-
ALL= 'all'¶
-
USER= 'user'¶
-
-
class
-
class
ListWordsEnums[source]¶ Bases:
object-
class
WordType[source]¶ Bases:
enum.EnumThe 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.
-
ALL= 'all'¶
-
USER= 'user'¶
-
CORPORA= 'corpora'¶
-
GRAMMARS= 'grammars'¶
-
-
class
Sort[source]¶ Bases:
enum.EnumIndicates 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.
-
ALPHABETICAL= 'alphabetical'¶
-
COUNT= 'count'¶
-
-
class
-
class
AddGrammarEnums[source]¶ Bases:
object-
class
ContentType[source]¶ Bases:
enum.EnumThe 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.
-
APPLICATION_SRGS= 'application/srgs'¶
-
APPLICATION_SRGS_XML= 'application/srgs+xml'¶
-
-
class
-
class
AddAudioEnums[source]¶ Bases:
object-
class
ContentType[source]¶ Bases:
enum.EnumFor 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.
-
APPLICATION_ZIP= 'application/zip'¶
-
APPLICATION_GZIP= 'application/gzip'¶
-
AUDIO_ALAW= 'audio/alaw'¶
-
AUDIO_BASIC= 'audio/basic'¶
-
AUDIO_FLAC= 'audio/flac'¶
-
AUDIO_G729= 'audio/g729'¶
-
AUDIO_L16= 'audio/l16'¶
-
AUDIO_MP3= 'audio/mp3'¶
-
AUDIO_MPEG= 'audio/mpeg'¶
-
AUDIO_MULAW= 'audio/mulaw'¶
-
AUDIO_OGG= 'audio/ogg'¶
-
AUDIO_OGG_CODECS_OPUS= 'audio/ogg;codecs=opus'¶
-
AUDIO_OGG_CODECS_VORBIS= 'audio/ogg;codecs=vorbis'¶
-
AUDIO_WAV= 'audio/wav'¶
-
AUDIO_WEBM= 'audio/webm'¶
-
AUDIO_WEBM_CODECS_OPUS= 'audio/webm;codecs=opus'¶
-
AUDIO_WEBM_CODECS_VORBIS= 'audio/webm;codecs=vorbis'¶
-
-
class
ContainedContentType[source]¶ Bases:
enum.EnumFor 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.
-
AUDIO_ALAW= 'audio/alaw'¶
-
AUDIO_BASIC= 'audio/basic'¶
-
AUDIO_FLAC= 'audio/flac'¶
-
AUDIO_G729= 'audio/g729'¶
-
AUDIO_L16= 'audio/l16'¶
-
AUDIO_MP3= 'audio/mp3'¶
-
AUDIO_MPEG= 'audio/mpeg'¶
-
AUDIO_MULAW= 'audio/mulaw'¶
-
AUDIO_OGG= 'audio/ogg'¶
-
AUDIO_OGG_CODECS_OPUS= 'audio/ogg;codecs=opus'¶
-
AUDIO_OGG_CODECS_VORBIS= 'audio/ogg;codecs=vorbis'¶
-
AUDIO_WAV= 'audio/wav'¶
-
AUDIO_WEBM= 'audio/webm'¶
-
AUDIO_WEBM_CODECS_OPUS= 'audio/webm;codecs=opus'¶
-
AUDIO_WEBM_CODECS_VORBIS= 'audio/webm;codecs=vorbis'¶
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-
class
-
class
AcousticModel(customization_id, *, created=None, updated=None, language=None, versions=None, owner=None, name=None, description=None, base_model_name=None, status=None, progress=None, warnings=None)[source]¶ Bases:
objectInformation about an existing custom acoustic model.
- Attr str customization_id
The customization ID (GUID) of the custom acoustic model. The Create a custom acoustic model method returns only this field of the object; it does not return the other fields.
- Attr str created
(optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was created. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
- Attr str updated
(optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was last modified. The created and updated fields are equal when an acoustic model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
- Attr str language
(optional) The language identifier of the custom acoustic model (for example, en-US).
- Attr list[str] versions
(optional) A list of the available versions of the custom acoustic model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
- Attr str owner
(optional) The GUID of the credentials for the instance of the service that owns the custom acoustic model.
- Attr str name
(optional) The name of the custom acoustic model.
- Attr str description
(optional) The description of the custom acoustic model.
- Attr str base_model_name
(optional) The name of the language model for which the custom acoustic model was created.
- Attr str status
(optional) The current status of the custom acoustic model: * pending: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * ready: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the strict parameter to false for the training to proceed. * training: The model is currently being trained. * available: The model is trained and ready to use. * upgrading: The model is currently being upgraded. * failed: Training of the model failed.
- Attr int progress
(optional) A percentage that indicates the progress of the custom acoustic model’s current training. A value of 100 means that the model is fully trained. Note: The progress field does not currently reflect the progress of the training. The field changes from 0 to 100 when training is complete.
- Attr str warnings
(optional) If the request included unknown parameters, the following message: Unexpected query parameter(s) [‘parameters’] detected, where parameters is a list that includes a quoted string for each unknown parameter.
-
class
StatusEnum[source]¶ Bases:
enum.EnumThe current status of the custom acoustic model: * pending: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * ready: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the strict parameter to false for the training to proceed. * training: The model is currently being trained. * available: The model is trained and ready to use. * upgrading: The model is currently being upgraded. * failed: Training of the model failed.
-
PENDING= 'pending'¶
-
READY= 'ready'¶
-
TRAINING= 'training'¶
-
AVAILABLE= 'available'¶
-
UPGRADING= 'upgrading'¶
-
FAILED= 'failed'¶
-
-
class
AcousticModels(customizations)[source]¶ Bases:
objectInformation about existing custom acoustic models.
- Attr list[AcousticModel] customizations
An array of AcousticModel objects that provides information about each available custom acoustic model. The array is empty if the requesting credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language.
-
class
AudioDetails(*, type=None, codec=None, frequency=None, compression=None)[source]¶ Bases:
objectInformation about an audio resource from a custom acoustic model.
- Attr str type
(optional) The type of the audio resource: * audio for an individual audio file * archive for an archive (.zip or .tar.gz) file that contains audio files * undetermined for a resource that the service cannot validate (for example, if the user mistakenly passes a file that does not contain audio, such as a JPEG file).
- Attr str codec
(optional) For an audio-type resource, the codec in which the audio is encoded. Omitted for an archive-type resource.
- Attr int frequency
(optional) For an audio-type resource, the sampling rate of the audio in Hertz (samples per second). Omitted for an archive-type resource.
- Attr str compression
(optional) For an archive-type resource, the format of the compressed archive: * zip for a .zip file * gzip for a .tar.gz file Omitted for an audio-type resource.
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class
TypeEnum[source]¶ Bases:
enum.EnumThe type of the audio resource: * audio for an individual audio file * archive for an archive (.zip or .tar.gz) file that contains audio files * undetermined for a resource that the service cannot validate (for example, if the user mistakenly passes a file that does not contain audio, such as a JPEG file).
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AUDIO= 'audio'¶
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ARCHIVE= 'archive'¶
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UNDETERMINED= 'undetermined'¶
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class
AudioListing(*, duration=None, name=None, details=None, status=None, container=None, audio=None)[source]¶ Bases:
objectInformation about an audio resource from a custom acoustic model.
- Attr int duration
(optional) For an audio-type resource, the total seconds of audio in the resource. Omitted for an archive-type resource.
- Attr str name
(optional) For an audio-type resource, the user-specified name of the resource. Omitted for an archive-type resource.
- Attr AudioDetails details
(optional) For an audio-type resource, an AudioDetails object that provides detailed information about the resource. The object is empty until the service finishes processing the audio. Omitted for an archive-type resource.
- Attr str status
(optional) For an audio-type resource, the status of the resource: * ok: The service successfully analyzed the audio data. The data can be used to train the custom model. * being_processed: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * invalid: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource.
- Attr AudioResource container
(optional) For an archive-type resource, an object of type AudioResource that provides information about the resource. Omitted for an audio-type resource.
- Attr list[AudioResource] audio
(optional) For an archive-type resource, an array of AudioResource objects that provides information about the audio-type resources that are contained in the resource. Omitted for an audio-type resource.
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class
StatusEnum[source]¶ Bases:
enum.EnumFor an audio-type resource, the status of the resource: * ok: The service successfully analyzed the audio data. The data can be used to train the custom model. * being_processed: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * invalid: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource.
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OK= 'ok'¶
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BEING_PROCESSED= 'being_processed'¶
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INVALID= 'invalid'¶
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class
AudioMetrics(sampling_interval, accumulated)[source]¶ Bases:
objectIf audio metrics are requested, information about the signal characteristics of the input audio.
- Attr float sampling_interval
The interval in seconds (typically 0.1 seconds) at which the service calculated the audio metrics. In other words, how often the service calculated the metrics. A single unit in each histogram (see the AudioMetricsHistogramBin object) is calculated based on a sampling_interval length of audio.
- Attr AudioMetricsDetails accumulated
Detailed information about the signal characteristics of the input audio.
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class
AudioMetricsDetails(final, end_time, speech_ratio, high_frequency_loss, direct_current_offset, clipping_rate, speech_level, non_speech_level, *, signal_to_noise_ratio=None)[source]¶ Bases:
objectDetailed information about the signal characteristics of the input audio.
- Attr bool final
If true, indicates the end of the audio stream, meaning that transcription is complete. Currently, the field is always true. The service returns metrics just once per audio stream. The results provide aggregated audio metrics that pertain to the complete audio stream.
- Attr float end_time
The end time in seconds of the block of audio to which the metrics apply.
- Attr float signal_to_noise_ratio
(optional) The signal-to-noise ratio (SNR) for the audio signal. The value indicates the ratio of speech to noise in the audio. A valid value lies in the range of 0 to 100 decibels (dB). The service omits the field if it cannot compute the SNR for the audio.
- Attr float speech_ratio
The ratio of speech to non-speech segments in the audio signal. The value lies in the range of 0.0 to 1.0.
- Attr float high_frequency_loss
The probability that the audio signal is missing the upper half of its frequency content. * A value close to 1.0 typically indicates artificially up-sampled audio, which negatively impacts the accuracy of the transcription results. * A value at or near 0.0 indicates that the audio signal is good and has a full spectrum. * A value around 0.5 means that detection of the frequency content is unreliable or not available.
- Attr list[AudioMetricsHistogramBin] direct_current_offset
An array of AudioMetricsHistogramBin objects that defines a histogram of the cumulative direct current (DC) component of the audio signal.
- Attr list[AudioMetricsHistogramBin] clipping_rate
An array of AudioMetricsHistogramBin objects that defines a histogram of the clipping rate for the audio segments. The clipping rate is defined as the fraction of samples in the segment that reach the maximum or minimum value that is offered by the audio quantization range. The service auto-detects either a 16-bit Pulse-Code Modulation(PCM) audio range (-32768 to +32767) or a unit range (-1.0 to +1.0). The clipping rate is between 0.0 and 1.0, with higher values indicating possible degradation of speech recognition.
- Attr list[AudioMetricsHistogramBin] speech_level
An array of AudioMetricsHistogramBin objects that defines a histogram of the signal level in segments of the audio that contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level).
- Attr list[AudioMetricsHistogramBin] non_speech_level
An array of AudioMetricsHistogramBin objects that defines a histogram of the signal level in segments of the audio that do not contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level).
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class
AudioMetricsHistogramBin(begin, end, count)[source]¶ Bases:
objectA bin with defined boundaries that indicates the number of values in a range of signal characteristics for a histogram. The first and last bins of a histogram are the boundary bins. They cover the intervals between negative infinity and the first boundary, and between the last boundary and positive infinity, respectively.
- Attr float begin
The lower boundary of the bin in the histogram.
- Attr float end
The upper boundary of the bin in the histogram.
- Attr int count
The number of values in the bin of the histogram.
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class
AudioResource(duration, name, details, status)[source]¶ Bases:
objectInformation about an audio resource from a custom acoustic model.
- Attr int duration
The total seconds of audio in the audio resource.
- Attr str name
For an archive-type resource, the user-specified name of the resource. For an audio-type resource, the user-specified name of the resource or the name of the audio file that the user added for the resource. The value depends on the method that is called.
- Attr AudioDetails details
An AudioDetails object that provides detailed information about the audio resource. The object is empty until the service finishes processing the audio.
- Attr str status
The status of the audio resource: * ok: The service successfully analyzed the audio data. The data can be used to train the custom model. * being_processed: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * invalid: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid.
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class
StatusEnum[source]¶ Bases:
enum.EnumThe status of the audio resource: * ok: The service successfully analyzed the audio data. The data can be used to train the custom model. * being_processed: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * invalid: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid.
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OK= 'ok'¶
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BEING_PROCESSED= 'being_processed'¶
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INVALID= 'invalid'¶
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class
AudioResources(total_minutes_of_audio, audio)[source]¶ Bases:
objectInformation about the audio resources from a custom acoustic model.
- Attr float total_minutes_of_audio
The total minutes of accumulated audio summed over all of the valid audio resources for the custom acoustic model. You can use this value to determine whether the custom model has too little or too much audio to begin training.
- Attr list[AudioResource] audio
An array of AudioResource objects that provides information about the audio resources of the custom acoustic model. The array is empty if the custom model has no audio resources.
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class
Corpora(corpora)[source]¶ Bases:
objectInformation about the corpora from a custom language model.
- Attr list[Corpus] corpora
An array of Corpus objects that provides information about the corpora for the custom model. The array is empty if the custom model has no corpora.
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class
Corpus(name, total_words, out_of_vocabulary_words, status, *, error=None)[source]¶ Bases:
objectInformation about a corpus from a custom language model.
- Attr str name
The name of the corpus.
- Attr int total_words
The total number of words in the corpus. The value is 0 while the corpus is being processed.
- Attr int out_of_vocabulary_words
The number of OOV words in the corpus. The value is 0 while the corpus is being processed.
- Attr str status
The status of the corpus: * analyzed: The service successfully analyzed the corpus. The custom model can be trained with data from the corpus. * being_processed: The service is still analyzing the corpus. The service cannot accept requests to add new resources or to train the custom model. * undetermined: The service encountered an error while processing the corpus. The error field describes the failure.
- Attr str error
(optional) If the status of the corpus is undetermined, the following message: Analysis of corpus ‘name’ failed. Please try adding the corpus again by setting the ‘allow_overwrite’ flag to ‘true’.
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class
StatusEnum[source]¶ Bases:
enum.EnumThe status of the corpus: * analyzed: The service successfully analyzed the corpus. The custom model can be trained with data from the corpus. * being_processed: The service is still analyzing the corpus. The service cannot accept requests to add new resources or to train the custom model. * undetermined: The service encountered an error while processing the corpus. The error field describes the failure.
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ANALYZED= 'analyzed'¶
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BEING_PROCESSED= 'being_processed'¶
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UNDETERMINED= 'undetermined'¶
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class
CustomWord(*, word=None, sounds_like=None, display_as=None)[source]¶ Bases:
objectInformation about a word that is to be added to a custom language model.
- Attr str word
(optional) 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 include spaces in the word. Use a - (dash) or _ (underscore) to connect the tokens of compound words. Omit this parameter for the Add a custom word method.
- Attr list[str] sounds_like
(optional) An 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 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 spaces.
- Attr str display_as
(optional) 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.
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class
Grammar(name, out_of_vocabulary_words, status, *, error=None)[source]¶ Bases:
objectInformation about a grammar from a custom language model.
- Attr str name
The name of the grammar.
- Attr int out_of_vocabulary_words
The number of OOV words in the grammar. The value is 0 while the grammar is being processed.
- Attr str status
The status of the grammar: * analyzed: The service successfully analyzed the grammar. The custom model can be trained with data from the grammar. * being_processed: The service is still analyzing the grammar. The service cannot accept requests to add new resources or to train the custom model. * undetermined: The service encountered an error while processing the grammar. The error field describes the failure.
- Attr str error
(optional) If the status of the grammar is undetermined, the following message: Analysis of grammar ‘{grammar_name}’ failed. Please try fixing the error or adding the grammar again by setting the ‘allow_overwrite’ flag to ‘true’..
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class
StatusEnum[source]¶ Bases:
enum.EnumThe status of the grammar: * analyzed: The service successfully analyzed the grammar. The custom model can be trained with data from the grammar. * being_processed: The service is still analyzing the grammar. The service cannot accept requests to add new resources or to train the custom model. * undetermined: The service encountered an error while processing the grammar. The error field describes the failure.
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ANALYZED= 'analyzed'¶
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BEING_PROCESSED= 'being_processed'¶
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UNDETERMINED= 'undetermined'¶
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class
Grammars(grammars)[source]¶ Bases:
objectInformation about the grammars from a custom language model.
- Attr list[Grammar] grammars
An array of Grammar objects that provides information about the grammars for the custom model. The array is empty if the custom model has no grammars.
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class
KeywordResult(normalized_text, start_time, end_time, confidence)[source]¶ Bases:
objectInformation about a match for a keyword from speech recognition results.
- Attr str normalized_text
A specified keyword normalized to the spoken phrase that matched in the audio input.
- Attr float start_time
The start time in seconds of the keyword match.
- Attr float end_time
The end time in seconds of the keyword match.
- Attr float confidence
A confidence score for the keyword match in the range of 0.0 to 1.0.
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class
LanguageModel(customization_id, *, created=None, updated=None, language=None, dialect=None, versions=None, owner=None, name=None, description=None, base_model_name=None, status=None, progress=None, error=None, warnings=None)[source]¶ Bases:
objectInformation about an existing custom language model.
- Attr str customization_id
The customization ID (GUID) of the custom language model. The Create a custom language model method returns only this field of the object; it does not return the other fields.
- Attr str created
(optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was created. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
- Attr str updated
(optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was last modified. The created and updated fields are equal when a language model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
- Attr str language
(optional) The language identifier of the custom language model (for example, en-US).
- Attr str dialect
(optional) The dialect of the language for the custom language model. For non-Spanish models, the field matches the language of the base model; for example, en-US for either of the US English language models. For Spanish models, the field indicates the dialect for which the model was created: * 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) Dialect values are case-insensitive.
- Attr list[str] versions
(optional) A list of the available versions of the custom language model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
- Attr str owner
(optional) The GUID of the credentials for the instance of the service that owns the custom language model.
- Attr str name
(optional) The name of the custom language model.
- Attr str description
(optional) The description of the custom language model.
- Attr str base_model_name
(optional) The name of the language model for which the custom language model was created.
- Attr str status
(optional) The current status of the custom language model: * pending: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * ready: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the strict parameter to false for the training to proceed. * training: The model is currently being trained. * available: The model is trained and ready to use. * upgrading: The model is currently being upgraded. * failed: Training of the model failed.
- Attr int progress
(optional) A percentage that indicates the progress of the custom language model’s current training. A value of 100 means that the model is fully trained. Note: The progress field does not currently reflect the progress of the training. The field changes from 0 to 100 when training is complete.
- Attr str error
(optional) If an error occurred while adding a grammar file to the custom language model, a message that describes an Internal Server Error and includes the string Cannot compile grammar. The status of the custom model is not affected by the error, but the grammar cannot be used with the model.
- Attr str warnings
(optional) If the request included unknown parameters, the following message: Unexpected query parameter(s) [‘parameters’] detected, where parameters is a list that includes a quoted string for each unknown parameter.
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class
StatusEnum[source]¶ Bases:
enum.EnumThe current status of the custom language model: * pending: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * ready: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the strict parameter to false for the training to proceed. * training: The model is currently being trained. * available: The model is trained and ready to use. * upgrading: The model is currently being upgraded. * failed: Training of the model failed.
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PENDING= 'pending'¶
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READY= 'ready'¶
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TRAINING= 'training'¶
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AVAILABLE= 'available'¶
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UPGRADING= 'upgrading'¶
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FAILED= 'failed'¶
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class
LanguageModels(customizations)[source]¶ Bases:
objectInformation about existing custom language models.
- Attr list[LanguageModel] customizations
An array of LanguageModel objects that provides information about each available custom language model. The array is empty if the requesting credentials own no custom language models (if no language is specified) or own no custom language models for the specified language.
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class
ProcessedAudio(received, seen_by_engine, transcription, *, speaker_labels=None)[source]¶ Bases:
objectDetailed timing information about the service’s processing of the input audio.
- Attr float received
The seconds of audio that the service has received as of this response. The value of the field is greater than the values of the transcription and speaker_labels fields during speech recognition processing, since the service first has to receive the audio before it can begin to process it. The final value can also be greater than the value of the transcription and speaker_labels fields by a fractional number of seconds.
- Attr float seen_by_engine
The seconds of audio that the service has passed to its speech-processing engine as of this response. The value of the field is greater than the values of the transcription and speaker_labels fields during speech recognition processing. The received and seen_by_engine fields have identical values when the service has finished processing all audio. This final value can be greater than the value of the transcription and speaker_labels fields by a fractional number of seconds.
- Attr float transcription
The seconds of audio that the service has processed for speech recognition as of this response.
- Attr float speaker_labels
(optional) If speaker labels are requested, the seconds of audio that the service has processed to determine speaker labels as of this response. This value often trails the value of the transcription field during speech recognition processing. The transcription and speaker_labels fields have identical values when the service has finished processing all audio.
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class
ProcessingMetrics(processed_audio, wall_clock_since_first_byte_received, periodic)[source]¶ Bases:
objectIf processing metrics are requested, information about the service’s processing of the input audio. Processing metrics are not available with the synchronous Recognize audio method.
- Attr ProcessedAudio processed_audio
Detailed timing information about the service’s processing of the input audio.
- Attr float wall_clock_since_first_byte_received
The amount of real time in seconds that has passed since the service received the first byte of input audio. Values in this field are generally multiples of the specified metrics interval, with two differences: * Values might not reflect exact intervals (for instance, 0.25, 0.5, and so on). Actual values might be 0.27, 0.52, and so on, depending on when the service receives and processes audio. * The service also returns values for transcription events if you set the interim_results parameter to true. The service returns both processing metrics and transcription results when such events occur.
- Attr bool periodic
An indication of whether the metrics apply to a periodic interval or a transcription event: * true means that the response was triggered by a specified processing interval. The information contains processing metrics only. * false means that the response was triggered by a transcription event. The information contains processing metrics plus transcription results. Use the field to identify why the service generated the response and to filter different results if necessary.
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class
RecognitionJob(id, status, created, *, updated=None, url=None, user_token=None, results=None, warnings=None)[source]¶ Bases:
objectInformation about a current asynchronous speech recognition job.
- Attr str id
The ID of the asynchronous job.
- Attr str status
The current status of the job: * waiting: The service is preparing the job for processing. The service returns this status when the job is initially created or when it is waiting for capacity to process the job. The job remains in this state until the service has the capacity to begin processing it. * processing: The service is actively processing the job. * completed: The service has finished processing the job. If the job specified a callback URL and the event recognitions.completed_with_results, the service sent the results with the callback notification. Otherwise, you must retrieve the results by checking the individual job. * failed: The job failed.
- Attr str created
The date and time in Coordinated Universal Time (UTC) at which the job was created. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
- Attr str updated
(optional) The date and time in Coordinated Universal Time (UTC) at which the job was last updated by the service. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD). This field is returned only by the Check jobs and Check a job methods.
- Attr str url
(optional) The URL to use to request information about the job with the Check a job method. This field is returned only by the Create a job method.
- Attr str user_token
(optional) The user token associated with a job that was created with a callback URL and a user token. This field can be returned only by the Check jobs method.
- Attr list[SpeechRecognitionResults] results
(optional) If the status is completed, the results of the recognition request as an array that includes a single instance of a SpeechRecognitionResults object. This field is returned only by the Check a job method.
- Attr list[str] warnings
(optional) An array of warning messages about invalid parameters included with the request. Each warning includes a descriptive message and a list of invalid argument strings, for example, “unexpected query parameter ‘user_token’, query parameter ‘callback_url’ was not specified”. The request succeeds despite the warnings. This field can be returned only by the Create a job method.
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class
StatusEnum[source]¶ Bases:
enum.EnumThe current status of the job: * waiting: The service is preparing the job for processing. The service returns this status when the job is initially created or when it is waiting for capacity to process the job. The job remains in this state until the service has the capacity to begin processing it. * processing: The service is actively processing the job. * completed: The service has finished processing the job. If the job specified a callback URL and the event recognitions.completed_with_results, the service sent the results with the callback notification. Otherwise, you must retrieve the results by checking the individual job. * failed: The job failed.
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WAITING= 'waiting'¶
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PROCESSING= 'processing'¶
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COMPLETED= 'completed'¶
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FAILED= 'failed'¶
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class
RecognitionJobs(recognitions)[source]¶ Bases:
objectInformation about current asynchronous speech recognition jobs.
- Attr list[RecognitionJob] recognitions
An array of RecognitionJob objects that provides the status for each of the user’s current jobs. The array is empty if the user has no current jobs.
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class
RegisterStatus(status, url)[source]¶ Bases:
objectInformation about a request to register a callback for asynchronous speech recognition.
- Attr str status
The current status of the job: * created: The service successfully white-listed the callback URL as a result of the call. * already created: The URL was already white-listed.
- Attr str url
The callback URL that is successfully registered.
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class
SpeakerLabelsResult(from_, to, speaker, confidence, final)[source]¶ Bases:
objectInformation about the speakers from speech recognition results.
- Attr float fromfrom_
The start time of a word from the transcript. The value matches the start time of a word from the timestamps array.
- Attr float to
The end time of a word from the transcript. The value matches the end time of a word from the timestamps array.
- Attr int speaker
The numeric identifier that the service assigns to a speaker from the audio. Speaker IDs begin at 0 initially but can evolve and change across interim results (if supported by the method) and between interim and final results as the service processes the audio. They are not guaranteed to be sequential, contiguous, or ordered.
- Attr float confidence
A score that indicates the service’s confidence in its identification of the speaker in the range of 0.0 to 1.0.
- Attr bool final
An indication of whether the service might further change word and speaker-label results. A value of true means that the service guarantees not to send any further updates for the current or any preceding results; false means that the service might send further updates to the results.
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class
SpeechModel(name, language, rate, url, supported_features, description)[source]¶ Bases:
objectInformation about an available language model.
- Attr str name
The name of the model for use as an identifier in calls to the service (for example, en-US_BroadbandModel).
- Attr str language
The language identifier of the model (for example, en-US).
- Attr int rate
The sampling rate (minimum acceptable rate for audio) used by the model in Hertz.
- Attr str url
The URI for the model.
- Attr SupportedFeatures supported_features
Additional service features that are supported with the model.
- Attr str description
A brief description of the model.
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class
SpeechModels(models)[source]¶ Bases:
objectInformation about the available language models.
- Attr list[SpeechModel] models
An array of SpeechModel objects that provides information about each available model.
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class
SpeechRecognitionAlternative(transcript, *, confidence=None, timestamps=None, word_confidence=None)[source]¶ Bases:
objectAn alternative transcript from speech recognition results.
- Attr str transcript
A transcription of the audio.
- Attr float confidence
(optional) A score that indicates the service’s confidence in the transcript in the range of 0.0 to 1.0. A confidence score is returned only for the best alternative and only with results marked as final.
- Attr list[str] timestamps
(optional) Time alignments for each word from the transcript as a list of lists. Each inner list consists of three elements: the word followed by its start and end time in seconds, for example: [[“hello”,0.0,1.2],[“world”,1.2,2.5]]. Timestamps are returned only for the best alternative.
- Attr list[str] word_confidence
(optional) A confidence score for each word of the transcript as a list of lists. Each inner list consists of two elements: the word and its confidence score in the range of 0.0 to 1.0, for example: [[“hello”,0.95],[“world”,0.866]]. Confidence scores are returned only for the best alternative and only with results marked as final.
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class
SpeechRecognitionResult(final, alternatives, *, keywords_result=None, word_alternatives=None)[source]¶ Bases:
objectComponent results for a speech recognition request.
- Attr bool final
An indication of whether the transcription results are final. If true, the results for this utterance are not updated further; no additional results are sent for a result_index once its results are indicated as final.
- Attr list[SpeechRecognitionAlternative] alternatives
An array of alternative transcripts. The alternatives array can include additional requested output such as word confidence or timestamps.
- Attr dict keywords_result
(optional) A dictionary (or associative array) whose keys are the strings specified for keywords if both that parameter and keywords_threshold are specified. The value for each key is an array of matches spotted in the audio for that keyword. Each match is described by a KeywordResult object. A keyword for which no matches are found is omitted from the dictionary. The dictionary is omitted entirely if no matches are found for any keywords.
- Attr list[WordAlternativeResults] word_alternatives
(optional) An array of alternative hypotheses found for words of the input audio if a word_alternatives_threshold is specified.
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class
SpeechRecognitionResults(*, results=None, result_index=None, speaker_labels=None, processing_metrics=None, audio_metrics=None, warnings=None)[source]¶ Bases:
objectThe complete results for a speech recognition request.
- Attr list[SpeechRecognitionResult] results
(optional) An array of SpeechRecognitionResult objects that can include interim and final results (interim results are returned only if supported by the method). Final results are guaranteed not to change; interim results might be replaced by further interim results and final results. The service periodically sends updates to the results list; the result_index is set to the lowest index in the array that has changed; it is incremented for new results.
- Attr int result_index
(optional) An index that indicates a change point in the results array. The service increments the index only for additional results that it sends for new audio for the same request.
- Attr list[SpeakerLabelsResult] speaker_labels
(optional) An array of SpeakerLabelsResult objects that identifies which words were spoken by which speakers in a multi-person exchange. The array is returned only if the speaker_labels parameter is true. When interim results are also requested for methods that support them, it is possible for a SpeechRecognitionResults object to include only the speaker_labels field.
- Attr ProcessingMetrics processing_metrics
(optional) If processing metrics are requested, information about the service’s processing of the input audio. Processing metrics are not available with the synchronous Recognize audio method.
- Attr AudioMetrics audio_metrics
(optional) If audio metrics are requested, information about the signal characteristics of the input audio.
- Attr list[str] warnings
(optional) An array of warning messages associated with the request: * Warnings for invalid parameters or fields can include a descriptive message and a list of invalid argument strings, for example, “Unknown arguments:” or “Unknown url query arguments:” followed by a list of the form “{invalid_arg_1}, {invalid_arg_2}.” * The following warning is returned if the request passes a custom model that is based on an older version of a base model for which an updated version is available: “Using previous version of base model, because your custom model has been built with it. Please note that this version will be supported only for a limited time. Consider updating your custom model to the new base model. If you do not do that you will be automatically switched to base model when you used the non-updated custom model.” In both cases, the request succeeds despite the warnings.
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class
SupportedFeatures(custom_language_model, speaker_labels)[source]¶ Bases:
objectAdditional service features that are supported with the model.
- Attr bool custom_language_model
Indicates whether the customization interface can be used to create a custom language model based on the language model.
- Attr bool speaker_labels
Indicates whether the speaker_labels parameter can be used with the language model.
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class
TrainingResponse(*, warnings=None)[source]¶ Bases:
objectThe response from training of a custom language or custom acoustic model.
- Attr list[TrainingWarning] warnings
(optional) An array of TrainingWarning objects that lists any invalid resources contained in the custom model. For custom language models, invalid resources are grouped and identified by type of resource. The method can return warnings only if the strict parameter is set to false.
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class
TrainingWarning(code, message)[source]¶ Bases:
objectA warning from training of a custom language or custom acoustic model.
- Attr str code
An identifier for the type of invalid resources listed in the description field.
- Attr str message
A warning message that lists the invalid resources that are excluded from the custom model’s training. The message has the following format: Analysis of the following {resource_type} has not completed successfully: [{resource_names}]. They will be excluded from custom {model_type} model training..
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class
Word(word, sounds_like, display_as, count, source, *, error=None)[source]¶ Bases:
objectInformation about a word from a custom language model.
- Attr str word
A word from the custom model’s words resource. The spelling of the word is used to train the model.
- Attr list[str] sounds_like
An array of pronunciations for the word. The array can include the sounds-like pronunciation automatically generated by the service if none is provided for the word; the service adds this pronunciation when it finishes processing the word.
- Attr str display_as
The spelling of the word that the service uses to display the word in a transcript. The field contains an empty string if no display-as value is provided for the word, in which case the word is displayed as it is spelled.
- Attr int count
A sum of the number of times the word is found across all corpora. For example, if the word occurs five times in one corpus and seven times in another, its count is 12. If you add a custom word to a model before it is added by any corpora, the count begins at 1; if the word is added from a corpus first and later modified, the count reflects only the number of times it is found in corpora.
- Attr list[str] source
An array of sources that describes how the word was added to the custom model’s words resource. For OOV words added from a corpus, includes the name of the corpus; if the word was added by multiple corpora, the names of all corpora are listed. If the word was modified or added by the user directly, the field includes the string user.
- Attr list[WordError] error
(optional) If the service discovered one or more problems that you need to correct for the word’s definition, an array that describes each of the errors.
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class
WordAlternativeResult(confidence, word)[source]¶ Bases:
objectAn alternative hypothesis for a word from speech recognition results.
- Attr float confidence
A confidence score for the word alternative hypothesis in the range of 0.0 to 1.0.
- Attr str word
An alternative hypothesis for a word from the input audio.
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class
WordAlternativeResults(start_time, end_time, alternatives)[source]¶ Bases:
objectInformation about alternative hypotheses for words from speech recognition results.
- Attr float start_time
The start time in seconds of the word from the input audio that corresponds to the word alternatives.
- Attr float end_time
The end time in seconds of the word from the input audio that corresponds to the word alternatives.
- Attr list[WordAlternativeResult] alternatives
An array of alternative hypotheses for a word from the input audio.
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class
WordError(element)[source]¶ Bases:
objectAn error associated with a word from a custom language model.
- Attr str element
A key-value pair that describes an error associated with the definition of a word in the words resource. The pair has the format “element”: “message”, where element is the aspect of the definition that caused the problem and message describes the problem. The following example describes a problem with one of the word’s sounds-like definitions: “{sounds_like_string}”: “Numbers are not allowed in sounds-like. You can try for example ‘{suggested_string}’.”.