# coding: utf-8 # Copyright 2018 IBM All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ 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. It 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. 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. """ from __future__ import absolute_import import json from os.path import basename from .watson_service import WatsonService ############################################################################## # Service ############################################################################## class SpeechToTextV1(WatsonService): """The Speech to Text V1 service.""" default_url = 'https://stream.watsonplatform.net/speech-to-text/api' def __init__( self, url=default_url, username=None, password=None, iam_apikey=None, iam_access_token=None, iam_url=None, ): """ Construct a new client for the Speech to Text service. :param str url: The base url to use when contacting the service (e.g. "https://stream.watsonplatform.net/speech-to-text/api"). The base url may differ between Bluemix regions. :param str username: The username used to authenticate with the service. Username and password credentials are only required to run your application locally or outside of Bluemix. When running on Bluemix, the credentials will be automatically loaded from the `VCAP_SERVICES` environment variable. :param str password: The password used to authenticate with the service. Username and password credentials are only required to run your application locally or outside of Bluemix. When running on Bluemix, the credentials will be automatically loaded from the `VCAP_SERVICES` environment variable. :param str iam_apikey: An API key that can be used to request IAM tokens. If this API key is provided, the SDK will manage the token and handle the refreshing. :param str iam_access_token: An IAM access token is fully managed by the application. Responsibility falls on the application to refresh the token, either before it expires or reactively upon receiving a 401 from the service as any requests made with an expired token will fail. :param str iam_url: An optional URL for the IAM service API. Defaults to 'https://iam.bluemix.net/identity/token'. """ WatsonService.__init__( self, vcap_services_name='speech_to_text', url=url, username=username, password=password, iam_apikey=iam_apikey, iam_access_token=iam_access_token, iam_url=iam_url, use_vcap_services=True) ######################### # Models ######################### def get_model(self, model_id, **kwargs): """ 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](/docs/services/speech-to-text/input.html#models). :param str model_id: The identifier of the model in the form of its name from the output of the **Get a model** method. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if model_id is None: raise ValueError('model_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/models/{0}'.format(*self._encode_path_vars(model_id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_models(self, **kwargs): """ 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](/docs/services/speech-to-text/input.html#models). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/models' response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response ######################### # Synchronous ######################### def recognize(self, 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, **kwargs): """ 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. **See also:** [Making a basic HTTP request](/docs/services/speech-to-text/http.html#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 server closes the connection (status code 408) if the service receives no data chunk for 30 seconds and it has no audio to transcribe for 30 seconds. The server also closes the connection (status code 400) if no speech is detected for `inactivity_timeout` seconds of audio (not processing time); use the `inactivity_timeout` parameter to change the default of 30 seconds. **See also:** * [Audio transmission](/docs/services/speech-to-text/input.html#transmission) * [Timeouts](/docs/services/speech-to-text/input.html#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/basic` (**Required.** Use only with narrowband models.) * `audio/flac` * `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the number of channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` * `audio/mpeg` * `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/ogg` (The service automatically detects the codec of the input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio with a maximum of nine channels.) * `audio/webm` (The service automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` * `audio/webm;codecs=vorbis` **See also:** [Audio formats](/docs/services/speech-to-text/audio-formats.html). ### 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. The multipart approach is intended for use 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](/docs/services/speech-to-text/http.html#HTTP-multi). :param file audio: The audio to transcribe. :param str content_type: The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. :param str model: The identifier of the model that is to be used for the recognition request. :param str language_customization_id: 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 service credentials created for the instance of the service that owns the custom model. By default, no custom language model is used. See [Custom models](/docs/services/speech-to-text/input.html#custom). **Note:** Use this parameter instead of the deprecated `customization_id` parameter. :param str acoustic_customization_id: 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 service credentials created for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See [Custom models](/docs/services/speech-to-text/input.html#custom). :param str base_model_version: The version of the specified base model that is to be used with 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](/docs/services/speech-to-text/input.html#version). :param float customization_weight: 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](/docs/services/speech-to-text/input.html#custom). :param int inactivity_timeout: The time in seconds after which, if only silence (no speech) is detected in submitted 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 [Timeouts](/docs/services/speech-to-text/input.html#timeouts). :param list[str] keywords: 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](/docs/services/speech-to-text/output.html#keyword_spotting). :param float keywords_threshold: A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords. See [Keyword spotting](/docs/services/speech-to-text/output.html#keyword_spotting). :param int max_alternatives: The maximum number of alternative transcripts that the service is to return. By default, a single transcription is returned. See [Maximum alternatives](/docs/services/speech-to-text/output.html#max_alternatives). :param float word_alternatives_threshold: 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. No alternative words are computed if you omit the parameter. See [Word alternatives](/docs/services/speech-to-text/output.html#word_alternatives). :param bool word_confidence: If `true`, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, no word confidence measures are returned. See [Word confidence](/docs/services/speech-to-text/output.html#word_confidence). :param bool timestamps: If `true`, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](/docs/services/speech-to-text/output.html#word_timestamps). :param bool profanity_filter: 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](/docs/services/speech-to-text/output.html#profanity_filter). :param bool smart_formatting: If `true`, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, no smart formatting is performed. Applies to US English, Japanese, and Spanish transcription only. See [Smart formatting](/docs/services/speech-to-text/output.html#smart_formatting). :param bool speaker_labels: If `true`, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, no speaker labels are returned. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. To determine whether a language model supports speaker labels, use the **Get a model** method and check that the attribute `speaker_labels` is set to `true`. See [Speaker labels](/docs/services/speech-to-text/output.html#speaker_labels). :param str customization_id: **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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if audio is None: raise ValueError('audio must be provided') headers = {'Content-Type': content_type} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = { 'model': model, 'language_customization_id': language_customization_id, 'acoustic_customization_id': acoustic_customization_id, 'base_model_version': base_model_version, 'customization_weight': customization_weight, 'inactivity_timeout': inactivity_timeout, 'keywords': self._convert_list(keywords), 'keywords_threshold': keywords_threshold, 'max_alternatives': max_alternatives, 'word_alternatives_threshold': word_alternatives_threshold, 'word_confidence': word_confidence, 'timestamps': timestamps, 'profanity_filter': profanity_filter, 'smart_formatting': smart_formatting, 'speaker_labels': speaker_labels, 'customization_id': customization_id } data = audio url = '/v1/recognize' response = self.request( method='POST', url=url, headers=headers, params=params, data=data, accept_json=True) return response ######################### # Asynchronous ######################### def check_job(self, id, **kwargs): """ 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 submit the request with the service credentials of the user who created the job. 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 caller. **See also:** [Checking the status and retrieving the results of a job](/docs/services/speech-to-text/async.html#job). :param str id: The identifier of the asynchronous job that is to be used for the request. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if id is None: raise ValueError('id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/recognitions/{0}'.format(*self._encode_path_vars(id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def check_jobs(self, **kwargs): """ Check jobs. Returns the ID and status of the latest 100 outstanding jobs associated with the service 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](/docs/services/speech-to-text/async.html#jobs). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/recognitions' response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def create_job(self, 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, **kwargs): """ Create a job. Creates a job for a new asynchronous recognition request. The job is owned by the user whose service 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 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. **See also:** [Creating a job](/docs/services/speech-to-text/async.html#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 server closes the connection (status code 408) if the service receives no data chunk for 30 seconds and it has no audio to transcribe for 30 seconds. The server also closes the connection (status code 400) if no speech is detected for `inactivity_timeout` seconds of audio (not processing time); use the `inactivity_timeout` parameter to change the default of 30 seconds. **See also:** * [Audio transmission](/docs/services/speech-to-text/input.html#transmission) * [Timeouts](/docs/services/speech-to-text/input.html#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/basic` (**Required.** Use only with narrowband models.) * `audio/flac` * `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the number of channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` * `audio/mpeg` * `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/ogg` (The service automatically detects the codec of the input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio with a maximum of nine channels.) * `audio/webm` (The service automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` * `audio/webm;codecs=vorbis` **See also:** [Audio formats](/docs/services/speech-to-text/audio-formats.html). :param file audio: The audio to transcribe. :param str content_type: The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. :param str model: The identifier of the model that is to be used for the recognition request. :param str callback_url: 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. :param str events: 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. :param str user_token: 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. :param int results_ttl: 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. :param str language_customization_id: 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 service credentials created for the instance of the service that owns the custom model. By default, no custom language model is used. See [Custom models](/docs/services/speech-to-text/input.html#custom). **Note:** Use this parameter instead of the deprecated `customization_id` parameter. :param str acoustic_customization_id: 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 service credentials created for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See [Custom models](/docs/services/speech-to-text/input.html#custom). :param str base_model_version: The version of the specified base model that is to be used with 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](/docs/services/speech-to-text/input.html#version). :param float customization_weight: 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](/docs/services/speech-to-text/input.html#custom). :param int inactivity_timeout: The time in seconds after which, if only silence (no speech) is detected in submitted 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 [Timeouts](/docs/services/speech-to-text/input.html#timeouts). :param list[str] keywords: 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](/docs/services/speech-to-text/output.html#keyword_spotting). :param float keywords_threshold: A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords. See [Keyword spotting](/docs/services/speech-to-text/output.html#keyword_spotting). :param int max_alternatives: The maximum number of alternative transcripts that the service is to return. By default, a single transcription is returned. See [Maximum alternatives](/docs/services/speech-to-text/output.html#max_alternatives). :param float word_alternatives_threshold: 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. No alternative words are computed if you omit the parameter. See [Word alternatives](/docs/services/speech-to-text/output.html#word_alternatives). :param bool word_confidence: If `true`, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, no word confidence measures are returned. See [Word confidence](/docs/services/speech-to-text/output.html#word_confidence). :param bool timestamps: If `true`, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](/docs/services/speech-to-text/output.html#word_timestamps). :param bool profanity_filter: 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](/docs/services/speech-to-text/output.html#profanity_filter). :param bool smart_formatting: If `true`, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, no smart formatting is performed. Applies to US English, Japanese, and Spanish transcription only. See [Smart formatting](/docs/services/speech-to-text/output.html#smart_formatting). :param bool speaker_labels: If `true`, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, no speaker labels are returned. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. To determine whether a language model supports speaker labels, use the **Get a model** method and check that the attribute `speaker_labels` is set to `true`. See [Speaker labels](/docs/services/speech-to-text/output.html#speaker_labels). :param str customization_id: **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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if audio is None: raise ValueError('audio must be provided') headers = {'Content-Type': content_type} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = { 'model': model, 'callback_url': callback_url, 'events': events, 'user_token': user_token, 'results_ttl': results_ttl, 'language_customization_id': language_customization_id, 'acoustic_customization_id': acoustic_customization_id, 'base_model_version': base_model_version, 'customization_weight': customization_weight, 'inactivity_timeout': inactivity_timeout, 'keywords': self._convert_list(keywords), 'keywords_threshold': keywords_threshold, 'max_alternatives': max_alternatives, 'word_alternatives_threshold': word_alternatives_threshold, 'word_confidence': word_confidence, 'timestamps': timestamps, 'profanity_filter': profanity_filter, 'smart_formatting': smart_formatting, 'speaker_labels': speaker_labels, 'customization_id': customization_id } data = audio url = '/v1/recognitions' response = self.request( method='POST', url=url, headers=headers, params=params, data=data, accept_json=True) return response def delete_job(self, id, **kwargs): """ 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 submit the request with the service credentials of the user who created the job. **See also:** [Deleting a job](/docs/services/speech-to-text/async.html#delete). :param str id: The identifier of the asynchronous job that is to be used for the request. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if id is None: raise ValueError('id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/recognitions/{0}'.format(*self._encode_path_vars(id)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def register_callback(self, callback_url, user_secret=None, **kwargs): """ 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](/docs/services/speech-to-text/async.html#register). :param str callback_url: 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. :param str user_secret: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if callback_url is None: raise ValueError('callback_url must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'callback_url': callback_url, 'user_secret': user_secret} url = '/v1/register_callback' response = self.request( method='POST', url=url, headers=headers, params=params, accept_json=True) return response def unregister_callback(self, callback_url, **kwargs): """ 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](/docs/services/speech-to-text/async.html#unregister). :param str callback_url: The callback URL that is to be unregistered. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if callback_url is None: raise ValueError('callback_url must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'callback_url': callback_url} url = '/v1/unregister_callback' response = self.request( method='POST', url=url, headers=headers, params=params, accept_json=True) return response ######################### # Custom language models ######################### def create_language_model(self, name, base_model_name, dialect=None, description=None, **kwargs): """ 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. **See also:** [Create a custom language model](/docs/services/speech-to-text/language-create.html#createModel). :param str name: 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`. :param str base_model_name: The name of the base language model that is to be customized by the new custom language model. The new custom model can be used only with the base model that it customizes. To determine whether a base model supports language model customization, use the **Get a model** method and check that the attribute `custom_language_model` is set to `true`. You can also refer to [Language support for customization](/docs/services/speech-to-text/custom.html#languageSupport). :param str dialect: The dialect of the specified language that is to be used with the custom language model. The parameter is meaningful only for Spanish models, for which the service creates a custom language model that is suited for speech in one of the following dialects: * `es-ES` for Castilian Spanish (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish A specified dialect must be valid for the base model. By default, the dialect matches the language of the base model; for example, `en-US` for either of the US English language models. :param str description: A description of the new custom language model. Use a localized description that matches the language of the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if name is None: raise ValueError('name must be provided') if base_model_name is None: raise ValueError('base_model_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) data = { 'name': name, 'base_model_name': base_model_name, 'dialect': dialect, 'description': description } url = '/v1/customizations' response = self.request( method='POST', url=url, headers=headers, json=data, accept_json=True) return response def delete_language_model(self, customization_id, **kwargs): """ 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 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](/docs/services/speech-to-text/language-models.html#deleteModel). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}'.format( *self._encode_path_vars(customization_id)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def get_language_model(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/language-models.html#listModels). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}'.format( *self._encode_path_vars(customization_id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_language_models(self, language=None, **kwargs): """ 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](/docs/services/speech-to-text/language-models.html#listModels). :param str language: 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 owned by the requesting service credentials. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'language': language} url = '/v1/customizations' response = self.request( method='GET', url=url, headers=headers, params=params, accept_json=True) return response def reset_language_model(self, customization_id, **kwargs): """ Reset a custom language model. Resets a custom language model by removing all corpora 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](/docs/services/speech-to-text/language-models.html#resetModel). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/reset'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, accept_json=True) return response def train_language_model(self, customization_id, word_type_to_add=None, customization_weight=None, **kwargs): """ Train a custom language model. Initiates the training of a custom language model with new corpora, custom words, or both. After adding, modifying, or deleting corpora or words 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. 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 corpora or words, until the existing request completes. 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 words to the model. * No training data (corpora or words) have been added to the custom model. * One or more words that were added to the custom model have invalid sounds-like pronunciations that you must fix. **See also:** [Train the custom language model](/docs/services/speech-to-text/language-create.html#trainModel). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str word_type_to_add: 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 were added or modified by the user. * `user` trains the model only on new words that were added or modified by the user; the model is not trained on new words extracted from corpora. :param float customization_weight: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = { 'word_type_to_add': word_type_to_add, 'customization_weight': customization_weight } url = '/v1/customizations/{0}/train'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, params=params, accept_json=True) return response def upgrade_language_model(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/custom-upgrade.html#upgradeLanguage). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/upgrade_model'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, accept_json=True) return response ######################### # Custom corpora ######################### def add_corpus(self, customization_id, corpus_name, corpus_file, allow_overwrite=None, corpus_filename=None, **kwargs): """ 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 corpora or words 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 any word that is 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, using 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 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 corpora combined. Also, you can add no more than 30 thousand custom (OOV) words to a model; this includes words that the service extracts from corpora and words that you add directly. **See also:** * [Working with corpora](/docs/services/speech-to-text/language-resource.html#workingCorpora) * [Add corpora to the custom language model](/docs/services/speech-to-text/language-create.html#addCorpora). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str corpus_name: 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 include spaces, slashes, or backslashes in the name. * Do not use the name of a corpus that has already been added to 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. :param file corpus_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](/docs/services/speech-to-text/language-resource.html#charEncoding). With the `curl` command, use the `--data-binary` option to upload the file for the request. :param bool allow_overwrite: 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. :param str corpus_filename: The filename for corpus_file. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if corpus_name is None: raise ValueError('corpus_name must be provided') if corpus_file is None: raise ValueError('corpus_file must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'allow_overwrite': allow_overwrite} form_data = {} if not corpus_filename and hasattr(corpus_file, 'name'): corpus_filename = basename(corpus_file.name) form_data['corpus_file'] = (corpus_filename, corpus_file, 'text/plain') url = '/v1/customizations/{0}/corpora/{1}'.format( *self._encode_path_vars(customization_id, corpus_name)) response = self.request( method='POST', url=url, headers=headers, params=params, files=form_data, accept_json=True) return response def delete_corpus(self, customization_id, corpus_name, **kwargs): """ Delete a corpus. Deletes an existing corpus from a custom language model. The service removes any out-of-vocabulary (OOV) words associated with the corpus from the custom model's words resource unless they were also added by another corpus or they have been 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](/docs/services/speech-to-text/language-corpora.html#deleteCorpus). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str corpus_name: The name of the corpus for the custom language model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if corpus_name is None: raise ValueError('corpus_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/corpora/{1}'.format( *self._encode_path_vars(customization_id, corpus_name)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def get_corpus(self, customization_id, corpus_name, **kwargs): """ 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](/docs/services/speech-to-text/language-corpora.html#listCorpora). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str corpus_name: The name of the corpus for the custom language model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if corpus_name is None: raise ValueError('corpus_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/corpora/{1}'.format( *self._encode_path_vars(customization_id, corpus_name)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_corpora(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/language-corpora.html#listCorpora). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/corpora'.format( *self._encode_path_vars(customization_id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response ######################### # Custom words ######################### def add_word(self, customization_id, word_name, word=None, sounds_like=None, display_as=None, **kwargs): """ 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 found in each corpus 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 30 thousand custom (OOV) words, including words that the service extracts from corpora 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 corpora 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](/docs/services/speech-to-text/language-resource.html#workingWords) * [Add words to the custom language model](/docs/services/speech-to-text/language-create.html#addWords). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str word_name: 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](/docs/services/speech-to-text/language-resource.html#charEncoding). :param str word: 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. :param list[str] sounds_like: 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. :param str display_as: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if word_name is None: raise ValueError('word_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) data = { 'word': word, 'sounds_like': sounds_like, 'display_as': display_as } url = '/v1/customizations/{0}/words/{1}'.format( *self._encode_path_vars(customization_id, word_name)) response = self.request( method='PUT', url=url, headers=headers, json=data, accept_json=True) return response def add_words(self, customization_id, words, **kwargs): """ 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 found in each corpus 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 30 thousand custom (OOV) words, including words that the service extracts from corpora 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 corpora 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 training a model. 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 corpora or words 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](/docs/services/speech-to-text/language-resource.html#workingWords) * [Add words to the custom language model](/docs/services/speech-to-text/language-create.html#addWords). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param list[CustomWord] words: An array of objects that provides information about each custom word that is to be added to or updated in the custom language model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if words is None: raise ValueError('words must be provided') words = [self._convert_model(x, CustomWord) for x in words] headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) data = {'words': words} url = '/v1/customizations/{0}/words'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, json=data, accept_json=True) return response def delete_word(self, customization_id, word_name, **kwargs): """ 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](/docs/services/speech-to-text/language-words.html#deleteWord). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str word_name: 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](/docs/services/speech-to-text/language-resource.html#charEncoding). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if word_name is None: raise ValueError('word_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/words/{1}'.format( *self._encode_path_vars(customization_id, word_name)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def get_word(self, customization_id, word_name, **kwargs): """ 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 query information about its words. **See also:** [Listing words from a custom language model](/docs/services/speech-to-text/language-words.html#listWords). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str word_name: 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](/docs/services/speech-to-text/language-resource.html#charEncoding). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if word_name is None: raise ValueError('word_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/customizations/{0}/words/{1}'.format( *self._encode_path_vars(customization_id, word_name)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_words(self, customization_id, word_type=None, sort=None, **kwargs): """ 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. You can also indicate the order in which the service is to return words; by default, words are listed in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to query information about its words. **See also:** [Listing words from a custom language model](/docs/services/speech-to-text/language-words.html#listWords). :param str customization_id: The customization ID (GUID) of the custom language model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str word_type: 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. * `corpora` shows only OOV that were extracted from corpora. :param str sort: 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`. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'word_type': word_type, 'sort': sort} url = '/v1/customizations/{0}/words'.format( *self._encode_path_vars(customization_id)) response = self.request( method='GET', url=url, headers=headers, params=params, accept_json=True) return response ######################### # Custom acoustic models ######################### def create_acoustic_model(self, name, base_model_name, description=None, **kwargs): """ 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. **See also:** [Create a custom acoustic model](/docs/services/speech-to-text/acoustic-create.html#createModel). :param str name: 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`. :param str base_model_name: The name of the base language model that is to be customized by the new custom acoustic model. The new custom model can be used only with the base model that it customizes. To determine whether a base model supports acoustic model customization, refer to [Language support for customization](/docs/services/speech-to-text/custom.html#languageSupport). :param str description: A description of the new custom acoustic model. Use a localized description that matches the language of the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if name is None: raise ValueError('name must be provided') if base_model_name is None: raise ValueError('base_model_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) data = { 'name': name, 'base_model_name': base_model_name, 'description': description } url = '/v1/acoustic_customizations' response = self.request( method='POST', url=url, headers=headers, json=data, accept_json=True) return response def delete_acoustic_model(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/acoustic-models.html#deleteModel). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}'.format( *self._encode_path_vars(customization_id)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def get_acoustic_model(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/acoustic-models.html#listModels). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}'.format( *self._encode_path_vars(customization_id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_acoustic_models(self, language=None, **kwargs): """ 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](/docs/services/speech-to-text/acoustic-models.html#listModels). :param str language: 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 owned by the requesting service credentials. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'language': language} url = '/v1/acoustic_customizations' response = self.request( method='GET', url=url, headers=headers, params=params, accept_json=True) return response def reset_acoustic_model(self, customization_id, **kwargs): """ 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. You must use credentials for the instance of the service that owns a model to reset it. **See also:** [Resetting a custom acoustic model](/docs/services/speech-to-text/acoustic-models.html#resetModel). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}/reset'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, accept_json=True) return response def train_acoustic_model(self, customization_id, custom_language_model_id=None, **kwargs): """ 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 accept subsequent training requests, or requests to add new audio resources, until the existing 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. Specify a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For more information, see the **Create a custom language model** method. 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 50 hours of audio data. * One or more of the custom model's audio resources is invalid. **See also:** [Train the custom acoustic model](/docs/services/speech-to-text/acoustic-create.html#trainModel). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str custom_language_model_id: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'custom_language_model_id': custom_language_model_id} url = '/v1/acoustic_customizations/{0}/train'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, params=params, accept_json=True) return response def upgrade_acoustic_model(self, customization_id, custom_language_model_id=None, **kwargs): """ 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 accept subsequent requests for the model until the upgrade 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](/docs/services/speech-to-text/custom-upgrade.html#upgradeAcoustic). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str custom_language_model_id: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'custom_language_model_id': custom_language_model_id} url = '/v1/acoustic_customizations/{0}/upgrade_model'.format( *self._encode_path_vars(customization_id)) response = self.request( method='POST', url=url, headers=headers, params=params, accept_json=True) return response ######################### # Custom audio resources ######################### def add_audio(self, customization_id, audio_name, audio_resource, content_type=None, contained_content_type=None, allow_overwrite=None, **kwargs): """ 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. But the addition of one audio resource must be fully complete before you can add another. You must add a minimum of 10 minutes and a maximum of 50 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 add additional audio resources to a custom acoustic model, or to train the model, until the service's analysis of all audio files for the current request 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](/docs/services/speech-to-text/acoustic-create.html#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/basic` (Use only with narrowband models.) * `audio/flac` * `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` **See also:** [Audio formats](/docs/services/speech-to-text/audio-formats.html). **Note:** 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`. ### 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. All audio files contained in the archive must have the same audio format. Use the `Contained-Content-Type` parameter to specify the format of the contained audio files. The parameter accepts all of the audio formats supported for use with speech recognition and with the `Content-Type` header, including the `rate`, `channels`, and `endianness` parameters that are used with some formats. The default contained audio format is `audio/wav`. ### Naming restrictions for embedded audio files The name of an audio file that is embedded within an archive-type resource must meet the following restrictions: * Include a maximum of 128 characters in the file name; this includes the file extension. * Do not include spaces, slashes, or backslashes in the file name. * Do not use the name of an audio file that has already been added to the custom model as part of an archive-type resource. :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str audio_name: 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 include spaces, slashes, or backslashes in the name. * Do not use the name of an audio resource that has already been added to the custom model. :param file audio_resource: The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file. :param str content_type: 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. :param str contained_content_type: For an archive-type resource, specifies the format of the audio files that are contained in the archive file. The parameter accepts all of the audio formats that are supported for use with speech recognition, including the `rate`, `channels`, and `endianness` parameters that are used with some formats. For more information, see **Content types for audio-type resources** in the method description. :param bool allow_overwrite: 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. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if audio_name is None: raise ValueError('audio_name must be provided') if audio_resource is None: raise ValueError('audio_resource must be provided') headers = { 'Content-Type': content_type, 'Contained-Content-Type': contained_content_type } if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'allow_overwrite': allow_overwrite} data = audio_resource url = '/v1/acoustic_customizations/{0}/audio/{1}'.format( *self._encode_path_vars(customization_id, audio_name)) response = self.request( method='POST', url=url, headers=headers, params=params, data=data, accept_json=True) return response def delete_audio(self, customization_id, audio_name, **kwargs): """ 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 current interface 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 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](/docs/services/speech-to-text/acoustic-audio.html#deleteAudio). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str audio_name: The name of the audio resource for the custom acoustic model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if audio_name is None: raise ValueError('audio_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}/audio/{1}'.format( *self._encode_path_vars(customization_id, audio_name)) response = self.request( method='DELETE', url=url, headers=headers, accept_json=True) return response def get_audio(self, customization_id, audio_name, **kwargs): """ 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](/docs/services/speech-to-text/acoustic-audio.html#listAudio). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param str audio_name: The name of the audio resource for the custom acoustic model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') if audio_name is None: raise ValueError('audio_name must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}/audio/{1}'.format( *self._encode_path_vars(customization_id, audio_name)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response def list_audio(self, customization_id, **kwargs): """ 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](/docs/services/speech-to-text/acoustic-audio.html#listAudio). :param str customization_id: The customization ID (GUID) of the custom acoustic model that is to be used for the request. You must make the request with service credentials created for the instance of the service that owns the custom model. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) url = '/v1/acoustic_customizations/{0}/audio'.format( *self._encode_path_vars(customization_id)) response = self.request( method='GET', url=url, headers=headers, accept_json=True) return response ######################### # User data ######################### def delete_user_data(self, customer_id, **kwargs): """ 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](/docs/services/speech-to-text/information-security.html). :param str customer_id: The customer ID for which all data is to be deleted. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse """ if customer_id is None: raise ValueError('customer_id must be provided') headers = {} if 'headers' in kwargs: headers.update(kwargs.get('headers')) params = {'customer_id': customer_id} url = '/v1/user_data' response = self.request( method='DELETE', url=url, headers=headers, params=params, accept_json=True) return response ############################################################################## # Models ############################################################################## class AcousticModel(object): """ AcousticModel. :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 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 service 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` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that 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. """ def __init__(self, customization_id, created=None, language=None, versions=None, owner=None, name=None, description=None, base_model_name=None, status=None, progress=None, warnings=None): """ Initialize a AcousticModel object. :param 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. :param 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`). :param str language: (optional) The language identifier of the custom acoustic model (for example, `en-US`). :param 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. :param str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom acoustic model. :param str name: (optional) The name of the custom acoustic model. :param str description: (optional) The description of the custom acoustic model. :param str base_model_name: (optional) The name of the language model for which the custom acoustic model was created. :param str status: (optional) The current status of the custom acoustic model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed. :param 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. :param 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. """ self.customization_id = customization_id self.created = created self.language = language self.versions = versions self.owner = owner self.name = name self.description = description self.base_model_name = base_model_name self.status = status self.progress = progress self.warnings = warnings @classmethod def _from_dict(cls, _dict): """Initialize a AcousticModel object from a json dictionary.""" args = {} if 'customization_id' in _dict: args['customization_id'] = _dict.get('customization_id') else: raise ValueError( 'Required property \'customization_id\' not present in AcousticModel JSON' ) if 'created' in _dict: args['created'] = _dict.get('created') if 'language' in _dict: args['language'] = _dict.get('language') if 'versions' in _dict: args['versions'] = _dict.get('versions') if 'owner' in _dict: args['owner'] = _dict.get('owner') if 'name' in _dict: args['name'] = _dict.get('name') if 'description' in _dict: args['description'] = _dict.get('description') if 'base_model_name' in _dict: args['base_model_name'] = _dict.get('base_model_name') if 'status' in _dict: args['status'] = _dict.get('status') if 'progress' in _dict: args['progress'] = _dict.get('progress') if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'customization_id') and self.customization_id is not None: _dict['customization_id'] = self.customization_id if hasattr(self, 'created') and self.created is not None: _dict['created'] = self.created if hasattr(self, 'language') and self.language is not None: _dict['language'] = self.language if hasattr(self, 'versions') and self.versions is not None: _dict['versions'] = self.versions if hasattr(self, 'owner') and self.owner is not None: _dict['owner'] = self.owner if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'description') and self.description is not None: _dict['description'] = self.description if hasattr(self, 'base_model_name') and self.base_model_name is not None: _dict['base_model_name'] = self.base_model_name if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'progress') and self.progress is not None: _dict['progress'] = self.progress if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def __str__(self): """Return a `str` version of this AcousticModel object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AcousticModels(object): """ AcousticModels. :attr list[AcousticModel] customizations: An array of objects that provides information about each available custom acoustic model. The array is empty if the requesting service credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language. """ def __init__(self, customizations): """ Initialize a AcousticModels object. :param list[AcousticModel] customizations: An array of objects that provides information about each available custom acoustic model. The array is empty if the requesting service credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language. """ self.customizations = customizations @classmethod def _from_dict(cls, _dict): """Initialize a AcousticModels object from a json dictionary.""" args = {} if 'customizations' in _dict: args['customizations'] = [ AcousticModel._from_dict(x) for x in (_dict.get('customizations')) ] else: raise ValueError( 'Required property \'customizations\' not present in AcousticModels JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'customizations') and self.customizations is not None: _dict['customizations'] = [ x._to_dict() for x in self.customizations ] return _dict def __str__(self): """Return a `str` version of this AcousticModels object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioDetails(object): """ AudioDetails. :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. """ def __init__(self, type=None, codec=None, frequency=None, compression=None): """ Initialize a AudioDetails object. :param 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). :param str codec: (optional) **For an audio-type resource,** the codec in which the audio is encoded. Omitted for an archive-type resource. :param 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. :param 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. """ self.type = type self.codec = codec self.frequency = frequency self.compression = compression @classmethod def _from_dict(cls, _dict): """Initialize a AudioDetails object from a json dictionary.""" args = {} if 'type' in _dict: args['type'] = _dict.get('type') if 'codec' in _dict: args['codec'] = _dict.get('codec') if 'frequency' in _dict: args['frequency'] = _dict.get('frequency') if 'compression' in _dict: args['compression'] = _dict.get('compression') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'type') and self.type is not None: _dict['type'] = self.type if hasattr(self, 'codec') and self.codec is not None: _dict['codec'] = self.codec if hasattr(self, 'frequency') and self.frequency is not None: _dict['frequency'] = self.frequency if hasattr(self, 'compression') and self.compression is not None: _dict['compression'] = self.compression return _dict def __str__(self): """Return a `str` version of this AudioDetails object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioListing(object): """ AudioListing. :attr float duration: (optional) **For an audio-type resource,** the total seconds of audio in the resource. The value is always a whole number. 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` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that 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` indicates that 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. """ def __init__(self, duration=None, name=None, details=None, status=None, container=None, audio=None): """ Initialize a AudioListing object. :param float duration: (optional) **For an audio-type resource,** the total seconds of audio in the resource. The value is always a whole number. Omitted for an archive-type resource. :param str name: (optional) **For an audio-type resource,** the user-specified name of the resource. Omitted for an archive-type resource. :param 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. :param str status: (optional) **For an audio-type resource,** the status of the resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that 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` indicates that 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. :param 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. :param 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. """ self.duration = duration self.name = name self.details = details self.status = status self.container = container self.audio = audio @classmethod def _from_dict(cls, _dict): """Initialize a AudioListing object from a json dictionary.""" args = {} if 'duration' in _dict: args['duration'] = _dict.get('duration') if 'name' in _dict: args['name'] = _dict.get('name') if 'details' in _dict: args['details'] = AudioDetails._from_dict(_dict.get('details')) if 'status' in _dict: args['status'] = _dict.get('status') if 'container' in _dict: args['container'] = AudioResource._from_dict(_dict.get('container')) if 'audio' in _dict: args['audio'] = [ AudioResource._from_dict(x) for x in (_dict.get('audio')) ] return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'duration') and self.duration is not None: _dict['duration'] = self.duration if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'details') and self.details is not None: _dict['details'] = self.details._to_dict() if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'container') and self.container is not None: _dict['container'] = self.container._to_dict() if hasattr(self, 'audio') and self.audio is not None: _dict['audio'] = [x._to_dict() for x in self.audio] return _dict def __str__(self): """Return a `str` version of this AudioListing object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioResource(object): """ AudioResource. :attr float duration: The total seconds of audio in the audio resource. The value is always a whole number. :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` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that 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` indicates that 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. """ def __init__(self, duration, name, details, status): """ Initialize a AudioResource object. :param float duration: The total seconds of audio in the audio resource. The value is always a whole number. :param 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. :param AudioDetails details: An `AudioDetails` object that provides detailed information about the audio resource. The object is empty until the service finishes processing the audio. :param str status: The status of the audio resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that 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` indicates that 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. """ self.duration = duration self.name = name self.details = details self.status = status @classmethod def _from_dict(cls, _dict): """Initialize a AudioResource object from a json dictionary.""" args = {} if 'duration' in _dict: args['duration'] = _dict.get('duration') else: raise ValueError( 'Required property \'duration\' not present in AudioResource JSON' ) if 'name' in _dict: args['name'] = _dict.get('name') else: raise ValueError( 'Required property \'name\' not present in AudioResource JSON') if 'details' in _dict: args['details'] = AudioDetails._from_dict(_dict.get('details')) else: raise ValueError( 'Required property \'details\' not present in AudioResource JSON' ) if 'status' in _dict: args['status'] = _dict.get('status') else: raise ValueError( 'Required property \'status\' not present in AudioResource JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'duration') and self.duration is not None: _dict['duration'] = self.duration if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'details') and self.details is not None: _dict['details'] = self.details._to_dict() if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status return _dict def __str__(self): """Return a `str` version of this AudioResource object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioResources(object): """ AudioResources. :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 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. """ def __init__(self, total_minutes_of_audio, audio): """ Initialize a AudioResources object. :param 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. :param list[AudioResource] audio: An array of 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. """ self.total_minutes_of_audio = total_minutes_of_audio self.audio = audio @classmethod def _from_dict(cls, _dict): """Initialize a AudioResources object from a json dictionary.""" args = {} if 'total_minutes_of_audio' in _dict: args['total_minutes_of_audio'] = _dict.get('total_minutes_of_audio') else: raise ValueError( 'Required property \'total_minutes_of_audio\' not present in AudioResources JSON' ) if 'audio' in _dict: args['audio'] = [ AudioResource._from_dict(x) for x in (_dict.get('audio')) ] else: raise ValueError( 'Required property \'audio\' not present in AudioResources JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'total_minutes_of_audio' ) and self.total_minutes_of_audio is not None: _dict['total_minutes_of_audio'] = self.total_minutes_of_audio if hasattr(self, 'audio') and self.audio is not None: _dict['audio'] = [x._to_dict() for x in self.audio] return _dict def __str__(self): """Return a `str` version of this AudioResources object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Corpora(object): """ Corpora. :attr list[Corpus] corpora: An array of objects that provides information about the corpora for the custom model. The array is empty if the custom model has no corpora. """ def __init__(self, corpora): """ Initialize a Corpora object. :param list[Corpus] corpora: An array of objects that provides information about the corpora for the custom model. The array is empty if the custom model has no corpora. """ self.corpora = corpora @classmethod def _from_dict(cls, _dict): """Initialize a Corpora object from a json dictionary.""" args = {} if 'corpora' in _dict: args['corpora'] = [ Corpus._from_dict(x) for x in (_dict.get('corpora')) ] else: raise ValueError( 'Required property \'corpora\' not present in Corpora JSON') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'corpora') and self.corpora is not None: _dict['corpora'] = [x._to_dict() for x in self.corpora] return _dict def __str__(self): """Return a `str` version of this Corpora object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Corpus(object): """ Corpus. :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` indicates that the service has successfully analyzed the corpus; the custom model can be trained with data from the corpus. * `being_processed` indicates that the service is still analyzing the corpus; the service cannot accept requests to add new corpora or words, or to train the custom model. * `undetermined` indicates that the service encountered an error while processing the corpus. :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'`. """ def __init__(self, name, total_words, out_of_vocabulary_words, status, error=None): """ Initialize a Corpus object. :param str name: The name of the corpus. :param int total_words: The total number of words in the corpus. The value is `0` while the corpus is being processed. :param int out_of_vocabulary_words: The number of OOV words in the corpus. The value is `0` while the corpus is being processed. :param str status: The status of the corpus: * `analyzed` indicates that the service has successfully analyzed the corpus; the custom model can be trained with data from the corpus. * `being_processed` indicates that the service is still analyzing the corpus; the service cannot accept requests to add new corpora or words, or to train the custom model. * `undetermined` indicates that the service encountered an error while processing the corpus. :param 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'`. """ self.name = name self.total_words = total_words self.out_of_vocabulary_words = out_of_vocabulary_words self.status = status self.error = error @classmethod def _from_dict(cls, _dict): """Initialize a Corpus object from a json dictionary.""" args = {} if 'name' in _dict: args['name'] = _dict.get('name') else: raise ValueError( 'Required property \'name\' not present in Corpus JSON') if 'total_words' in _dict: args['total_words'] = _dict.get('total_words') else: raise ValueError( 'Required property \'total_words\' not present in Corpus JSON') if 'out_of_vocabulary_words' in _dict: args['out_of_vocabulary_words'] = _dict.get( 'out_of_vocabulary_words') else: raise ValueError( 'Required property \'out_of_vocabulary_words\' not present in Corpus JSON' ) if 'status' in _dict: args['status'] = _dict.get('status') else: raise ValueError( 'Required property \'status\' not present in Corpus JSON') if 'error' in _dict: args['error'] = _dict.get('error') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'total_words') and self.total_words is not None: _dict['total_words'] = self.total_words if hasattr(self, 'out_of_vocabulary_words' ) and self.out_of_vocabulary_words is not None: _dict['out_of_vocabulary_words'] = self.out_of_vocabulary_words if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'error') and self.error is not None: _dict['error'] = self.error return _dict def __str__(self): """Return a `str` version of this Corpus object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class CustomWord(object): """ CustomWord. :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. """ def __init__(self, word=None, sounds_like=None, display_as=None): """ Initialize a CustomWord object. :param 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. :param 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. :param 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. """ self.word = word self.sounds_like = sounds_like self.display_as = display_as @classmethod def _from_dict(cls, _dict): """Initialize a CustomWord object from a json dictionary.""" args = {} if 'word' in _dict: args['word'] = _dict.get('word') if 'sounds_like' in _dict: args['sounds_like'] = _dict.get('sounds_like') if 'display_as' in _dict: args['display_as'] = _dict.get('display_as') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'word') and self.word is not None: _dict['word'] = self.word if hasattr(self, 'sounds_like') and self.sounds_like is not None: _dict['sounds_like'] = self.sounds_like if hasattr(self, 'display_as') and self.display_as is not None: _dict['display_as'] = self.display_as return _dict def __str__(self): """Return a `str` version of this CustomWord object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class KeywordResult(object): """ KeywordResult. :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. """ def __init__(self, normalized_text, start_time, end_time, confidence): """ Initialize a KeywordResult object. :param str normalized_text: A specified keyword normalized to the spoken phrase that matched in the audio input. :param float start_time: The start time in seconds of the keyword match. :param float end_time: The end time in seconds of the keyword match. :param float confidence: A confidence score for the keyword match in the range of 0.0 to 1.0. """ self.normalized_text = normalized_text self.start_time = start_time self.end_time = end_time self.confidence = confidence @classmethod def _from_dict(cls, _dict): """Initialize a KeywordResult object from a json dictionary.""" args = {} if 'normalized_text' in _dict: args['normalized_text'] = _dict.get('normalized_text') else: raise ValueError( 'Required property \'normalized_text\' not present in KeywordResult JSON' ) if 'start_time' in _dict: args['start_time'] = _dict.get('start_time') else: raise ValueError( 'Required property \'start_time\' not present in KeywordResult JSON' ) if 'end_time' in _dict: args['end_time'] = _dict.get('end_time') else: raise ValueError( 'Required property \'end_time\' not present in KeywordResult JSON' ) if 'confidence' in _dict: args['confidence'] = _dict.get('confidence') else: raise ValueError( 'Required property \'confidence\' not present in KeywordResult JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'normalized_text') and self.normalized_text is not None: _dict['normalized_text'] = self.normalized_text if hasattr(self, 'start_time') and self.start_time is not None: _dict['start_time'] = self.start_time if hasattr(self, 'end_time') and self.end_time is not None: _dict['end_time'] = self.end_time if hasattr(self, 'confidence') and self.confidence is not None: _dict['confidence'] = self.confidence return _dict def __str__(self): """Return a `str` version of this KeywordResult object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class LanguageModel(object): """ LanguageModel. :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 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. By default, the dialect 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 (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish. :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 service 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` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that 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 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. """ def __init__(self, customization_id, created=None, language=None, dialect=None, versions=None, owner=None, name=None, description=None, base_model_name=None, status=None, progress=None, warnings=None): """ Initialize a LanguageModel object. :param 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. :param 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`). :param str language: (optional) The language identifier of the custom language model (for example, `en-US`). :param str dialect: (optional) The dialect of the language for the custom language model. By default, the dialect 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 (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish. :param 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. :param str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom language model. :param str name: (optional) The name of the custom language model. :param str description: (optional) The description of the custom language model. :param str base_model_name: (optional) The name of the language model for which the custom language model was created. :param str status: (optional) The current status of the custom language model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed. :param 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. :param 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. """ self.customization_id = customization_id self.created = created self.language = language self.dialect = dialect self.versions = versions self.owner = owner self.name = name self.description = description self.base_model_name = base_model_name self.status = status self.progress = progress self.warnings = warnings @classmethod def _from_dict(cls, _dict): """Initialize a LanguageModel object from a json dictionary.""" args = {} if 'customization_id' in _dict: args['customization_id'] = _dict.get('customization_id') else: raise ValueError( 'Required property \'customization_id\' not present in LanguageModel JSON' ) if 'created' in _dict: args['created'] = _dict.get('created') if 'language' in _dict: args['language'] = _dict.get('language') if 'dialect' in _dict: args['dialect'] = _dict.get('dialect') if 'versions' in _dict: args['versions'] = _dict.get('versions') if 'owner' in _dict: args['owner'] = _dict.get('owner') if 'name' in _dict: args['name'] = _dict.get('name') if 'description' in _dict: args['description'] = _dict.get('description') if 'base_model_name' in _dict: args['base_model_name'] = _dict.get('base_model_name') if 'status' in _dict: args['status'] = _dict.get('status') if 'progress' in _dict: args['progress'] = _dict.get('progress') if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'customization_id') and self.customization_id is not None: _dict['customization_id'] = self.customization_id if hasattr(self, 'created') and self.created is not None: _dict['created'] = self.created if hasattr(self, 'language') and self.language is not None: _dict['language'] = self.language if hasattr(self, 'dialect') and self.dialect is not None: _dict['dialect'] = self.dialect if hasattr(self, 'versions') and self.versions is not None: _dict['versions'] = self.versions if hasattr(self, 'owner') and self.owner is not None: _dict['owner'] = self.owner if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'description') and self.description is not None: _dict['description'] = self.description if hasattr(self, 'base_model_name') and self.base_model_name is not None: _dict['base_model_name'] = self.base_model_name if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'progress') and self.progress is not None: _dict['progress'] = self.progress if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def __str__(self): """Return a `str` version of this LanguageModel object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class LanguageModels(object): """ LanguageModels. :attr list[LanguageModel] customizations: An array of objects that provides information about each available custom language model. The array is empty if the requesting service credentials own no custom language models (if no language is specified) or own no custom language models for the specified language. """ def __init__(self, customizations): """ Initialize a LanguageModels object. :param list[LanguageModel] customizations: An array of objects that provides information about each available custom language model. The array is empty if the requesting service credentials own no custom language models (if no language is specified) or own no custom language models for the specified language. """ self.customizations = customizations @classmethod def _from_dict(cls, _dict): """Initialize a LanguageModels object from a json dictionary.""" args = {} if 'customizations' in _dict: args['customizations'] = [ LanguageModel._from_dict(x) for x in (_dict.get('customizations')) ] else: raise ValueError( 'Required property \'customizations\' not present in LanguageModels JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'customizations') and self.customizations is not None: _dict['customizations'] = [ x._to_dict() for x in self.customizations ] return _dict def __str__(self): """Return a `str` version of this LanguageModels object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RecognitionJob(object): """ RecognitionJob. :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. """ def __init__(self, id, status, created, updated=None, url=None, user_token=None, results=None, warnings=None): """ Initialize a RecognitionJob object. :param str id: The ID of the asynchronous job. :param 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. :param 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`). :param 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. :param 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. :param 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. :param 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. :param 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. """ self.id = id self.status = status self.created = created self.updated = updated self.url = url self.user_token = user_token self.results = results self.warnings = warnings @classmethod def _from_dict(cls, _dict): """Initialize a RecognitionJob object from a json dictionary.""" args = {} if 'id' in _dict: args['id'] = _dict.get('id') else: raise ValueError( 'Required property \'id\' not present in RecognitionJob JSON') if 'status' in _dict: args['status'] = _dict.get('status') else: raise ValueError( 'Required property \'status\' not present in RecognitionJob JSON' ) if 'created' in _dict: args['created'] = _dict.get('created') else: raise ValueError( 'Required property \'created\' not present in RecognitionJob JSON' ) if 'updated' in _dict: args['updated'] = _dict.get('updated') if 'url' in _dict: args['url'] = _dict.get('url') if 'user_token' in _dict: args['user_token'] = _dict.get('user_token') if 'results' in _dict: args['results'] = [ SpeechRecognitionResults._from_dict(x) for x in (_dict.get('results')) ] if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'id') and self.id is not None: _dict['id'] = self.id if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'created') and self.created is not None: _dict['created'] = self.created if hasattr(self, 'updated') and self.updated is not None: _dict['updated'] = self.updated if hasattr(self, 'url') and self.url is not None: _dict['url'] = self.url if hasattr(self, 'user_token') and self.user_token is not None: _dict['user_token'] = self.user_token if hasattr(self, 'results') and self.results is not None: _dict['results'] = [x._to_dict() for x in self.results] if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def __str__(self): """Return a `str` version of this RecognitionJob object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RecognitionJobs(object): """ RecognitionJobs. :attr list[RecognitionJob] recognitions: An array of objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs. """ def __init__(self, recognitions): """ Initialize a RecognitionJobs object. :param list[RecognitionJob] recognitions: An array of objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs. """ self.recognitions = recognitions @classmethod def _from_dict(cls, _dict): """Initialize a RecognitionJobs object from a json dictionary.""" args = {} if 'recognitions' in _dict: args['recognitions'] = [ RecognitionJob._from_dict(x) for x in (_dict.get('recognitions')) ] else: raise ValueError( 'Required property \'recognitions\' not present in RecognitionJobs JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'recognitions') and self.recognitions is not None: _dict['recognitions'] = [x._to_dict() for x in self.recognitions] return _dict def __str__(self): """Return a `str` version of this RecognitionJobs object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RegisterStatus(object): """ RegisterStatus. :attr str status: The current status of the job: * `created` if the callback URL was successfully white-listed as a result of the call. * `already created` if the URL was already white-listed. :attr str url: The callback URL that is successfully registered. """ def __init__(self, status, url): """ Initialize a RegisterStatus object. :param str status: The current status of the job: * `created` if the callback URL was successfully white-listed as a result of the call. * `already created` if the URL was already white-listed. :param str url: The callback URL that is successfully registered. """ self.status = status self.url = url @classmethod def _from_dict(cls, _dict): """Initialize a RegisterStatus object from a json dictionary.""" args = {} if 'status' in _dict: args['status'] = _dict.get('status') else: raise ValueError( 'Required property \'status\' not present in RegisterStatus JSON' ) if 'url' in _dict: args['url'] = _dict.get('url') else: raise ValueError( 'Required property \'url\' not present in RegisterStatus JSON') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'status') and self.status is not None: _dict['status'] = self.status if hasattr(self, 'url') and self.url is not None: _dict['url'] = self.url return _dict def __str__(self): """Return a `str` version of this RegisterStatus object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeakerLabelsResult(object): """ SpeakerLabelsResult. :attr float from_: 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_results: 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. """ def __init__(self, from_, to, speaker, confidence, final_results): """ Initialize a SpeakerLabelsResult object. :param float from_: The start time of a word from the transcript. The value matches the start time of a word from the `timestamps` array. :param float to: The end time of a word from the transcript. The value matches the end time of a word from the `timestamps` array. :param 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. :param 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. :param bool final_results: 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. """ self.from_ = from_ self.to = to self.speaker = speaker self.confidence = confidence self.final_results = final_results @classmethod def _from_dict(cls, _dict): """Initialize a SpeakerLabelsResult object from a json dictionary.""" args = {} if 'from' in _dict: args['from_'] = _dict.get('from') else: raise ValueError( 'Required property \'from\' not present in SpeakerLabelsResult JSON' ) if 'to' in _dict: args['to'] = _dict.get('to') else: raise ValueError( 'Required property \'to\' not present in SpeakerLabelsResult JSON' ) if 'speaker' in _dict: args['speaker'] = _dict.get('speaker') else: raise ValueError( 'Required property \'speaker\' not present in SpeakerLabelsResult JSON' ) if 'confidence' in _dict: args['confidence'] = _dict.get('confidence') else: raise ValueError( 'Required property \'confidence\' not present in SpeakerLabelsResult JSON' ) if 'final' in _dict or 'final_results' in _dict: args['final_results'] = _dict.get('final') or _dict.get( 'final_results') else: raise ValueError( 'Required property \'final\' not present in SpeakerLabelsResult JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'from_') and self.from_ is not None: _dict['from'] = self.from_ if hasattr(self, 'to') and self.to is not None: _dict['to'] = self.to if hasattr(self, 'speaker') and self.speaker is not None: _dict['speaker'] = self.speaker if hasattr(self, 'confidence') and self.confidence is not None: _dict['confidence'] = self.confidence if hasattr(self, 'final_results') and self.final_results is not None: _dict['final'] = self.final_results return _dict def __str__(self): """Return a `str` version of this SpeakerLabelsResult object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechModel(object): """ SpeechModel. :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: Describes the additional service features that are supported with the model. :attr str description: A brief description of the model. """ def __init__(self, name, language, rate, url, supported_features, description): """ Initialize a SpeechModel object. :param str name: The name of the model for use as an identifier in calls to the service (for example, `en-US_BroadbandModel`). :param str language: The language identifier of the model (for example, `en-US`). :param int rate: The sampling rate (minimum acceptable rate for audio) used by the model in Hertz. :param str url: The URI for the model. :param SupportedFeatures supported_features: Describes the additional service features that are supported with the model. :param str description: A brief description of the model. """ self.name = name self.language = language self.rate = rate self.url = url self.supported_features = supported_features self.description = description @classmethod def _from_dict(cls, _dict): """Initialize a SpeechModel object from a json dictionary.""" args = {} if 'name' in _dict: args['name'] = _dict.get('name') else: raise ValueError( 'Required property \'name\' not present in SpeechModel JSON') if 'language' in _dict: args['language'] = _dict.get('language') else: raise ValueError( 'Required property \'language\' not present in SpeechModel JSON' ) if 'rate' in _dict: args['rate'] = _dict.get('rate') else: raise ValueError( 'Required property \'rate\' not present in SpeechModel JSON') if 'url' in _dict: args['url'] = _dict.get('url') else: raise ValueError( 'Required property \'url\' not present in SpeechModel JSON') if 'supported_features' in _dict: args['supported_features'] = SupportedFeatures._from_dict( _dict.get('supported_features')) else: raise ValueError( 'Required property \'supported_features\' not present in SpeechModel JSON' ) if 'description' in _dict: args['description'] = _dict.get('description') else: raise ValueError( 'Required property \'description\' not present in SpeechModel JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'name') and self.name is not None: _dict['name'] = self.name if hasattr(self, 'language') and self.language is not None: _dict['language'] = self.language if hasattr(self, 'rate') and self.rate is not None: _dict['rate'] = self.rate if hasattr(self, 'url') and self.url is not None: _dict['url'] = self.url if hasattr( self, 'supported_features') and self.supported_features is not None: _dict['supported_features'] = self.supported_features._to_dict() if hasattr(self, 'description') and self.description is not None: _dict['description'] = self.description return _dict def __str__(self): """Return a `str` version of this SpeechModel object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechModels(object): """ SpeechModels. :attr list[SpeechModel] models: An array of objects that provides information about each available model. """ def __init__(self, models): """ Initialize a SpeechModels object. :param list[SpeechModel] models: An array of objects that provides information about each available model. """ self.models = models @classmethod def _from_dict(cls, _dict): """Initialize a SpeechModels object from a json dictionary.""" args = {} if 'models' in _dict: args['models'] = [ SpeechModel._from_dict(x) for x in (_dict.get('models')) ] else: raise ValueError( 'Required property \'models\' not present in SpeechModels JSON') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'models') and self.models is not None: _dict['models'] = [x._to_dict() for x in self.models] return _dict def __str__(self): """Return a `str` version of this SpeechModels object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechRecognitionAlternative(object): """ SpeechRecognitionAlternative. :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. """ def __init__(self, transcript, confidence=None, timestamps=None, word_confidence=None): """ Initialize a SpeechRecognitionAlternative object. :param str transcript: A transcription of the audio. :param 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. :param 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. :param 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. """ self.transcript = transcript self.confidence = confidence self.timestamps = timestamps self.word_confidence = word_confidence @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionAlternative object from a json dictionary.""" args = {} if 'transcript' in _dict: args['transcript'] = _dict.get('transcript') else: raise ValueError( 'Required property \'transcript\' not present in SpeechRecognitionAlternative JSON' ) if 'confidence' in _dict: args['confidence'] = _dict.get('confidence') if 'timestamps' in _dict: args['timestamps'] = _dict.get('timestamps') if 'word_confidence' in _dict: args['word_confidence'] = _dict.get('word_confidence') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'transcript') and self.transcript is not None: _dict['transcript'] = self.transcript if hasattr(self, 'confidence') and self.confidence is not None: _dict['confidence'] = self.confidence if hasattr(self, 'timestamps') and self.timestamps is not None: _dict['timestamps'] = self.timestamps if hasattr(self, 'word_confidence') and self.word_confidence is not None: _dict['word_confidence'] = self.word_confidence return _dict def __str__(self): """Return a `str` version of this SpeechRecognitionAlternative object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechRecognitionResult(object): """ SpeechRecognitionResult. :attr bool final_results: 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. """ def __init__(self, final_results, alternatives, keywords_result=None, word_alternatives=None): """ Initialize a SpeechRecognitionResult object. :param bool final_results: 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. :param list[SpeechRecognitionAlternative] alternatives: An array of alternative transcripts. The `alternatives` array can include additional requested output such as word confidence or timestamps. :param 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. :param list[WordAlternativeResults] word_alternatives: (optional) An array of alternative hypotheses found for words of the input audio if a `word_alternatives_threshold` is specified. """ self.final_results = final_results self.alternatives = alternatives self.keywords_result = keywords_result self.word_alternatives = word_alternatives @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionResult object from a json dictionary.""" args = {} if 'final' in _dict or 'final_results' in _dict: args['final_results'] = _dict.get('final') or _dict.get( 'final_results') else: raise ValueError( 'Required property \'final\' not present in SpeechRecognitionResult JSON' ) if 'alternatives' in _dict: args['alternatives'] = [ SpeechRecognitionAlternative._from_dict(x) for x in (_dict.get('alternatives')) ] else: raise ValueError( 'Required property \'alternatives\' not present in SpeechRecognitionResult JSON' ) if 'keywords_result' in _dict: args['keywords_result'] = _dict.get('keywords_result') if 'word_alternatives' in _dict: args['word_alternatives'] = [ WordAlternativeResults._from_dict(x) for x in (_dict.get('word_alternatives')) ] return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'final_results') and self.final_results is not None: _dict['final'] = self.final_results if hasattr(self, 'alternatives') and self.alternatives is not None: _dict['alternatives'] = [x._to_dict() for x in self.alternatives] if hasattr(self, 'keywords_result') and self.keywords_result is not None: _dict['keywords_result'] = self.keywords_result if hasattr(self, 'word_alternatives') and self.word_alternatives is not None: _dict['word_alternatives'] = [ x._to_dict() for x in self.word_alternatives ] return _dict def __str__(self): """Return a `str` version of this SpeechRecognitionResult object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechRecognitionResults(object): """ SpeechRecognitionResults. :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 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. """ def __init__(self, results=None, result_index=None, speaker_labels=None, warnings=None): """ Initialize a SpeechRecognitionResults object. :param 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. :param 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. :param 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. :param 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. """ self.results = results self.result_index = result_index self.speaker_labels = speaker_labels self.warnings = warnings @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionResults object from a json dictionary.""" args = {} if 'results' in _dict: args['results'] = [ SpeechRecognitionResult._from_dict(x) for x in (_dict.get('results')) ] if 'result_index' in _dict: args['result_index'] = _dict.get('result_index') if 'speaker_labels' in _dict: args['speaker_labels'] = [ SpeakerLabelsResult._from_dict(x) for x in (_dict.get('speaker_labels')) ] if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'results') and self.results is not None: _dict['results'] = [x._to_dict() for x in self.results] if hasattr(self, 'result_index') and self.result_index is not None: _dict['result_index'] = self.result_index if hasattr(self, 'speaker_labels') and self.speaker_labels is not None: _dict['speaker_labels'] = [ x._to_dict() for x in self.speaker_labels ] if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def __str__(self): """Return a `str` version of this SpeechRecognitionResults object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SupportedFeatures(object): """ Describes the additional 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. """ def __init__(self, custom_language_model, speaker_labels): """ Initialize a SupportedFeatures object. :param bool custom_language_model: Indicates whether the customization interface can be used to create a custom language model based on the language model. :param bool speaker_labels: Indicates whether the `speaker_labels` parameter can be used with the language model. """ self.custom_language_model = custom_language_model self.speaker_labels = speaker_labels @classmethod def _from_dict(cls, _dict): """Initialize a SupportedFeatures object from a json dictionary.""" args = {} if 'custom_language_model' in _dict: args['custom_language_model'] = _dict.get('custom_language_model') else: raise ValueError( 'Required property \'custom_language_model\' not present in SupportedFeatures JSON' ) if 'speaker_labels' in _dict: args['speaker_labels'] = _dict.get('speaker_labels') else: raise ValueError( 'Required property \'speaker_labels\' not present in SupportedFeatures JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'custom_language_model' ) and self.custom_language_model is not None: _dict['custom_language_model'] = self.custom_language_model if hasattr(self, 'speaker_labels') and self.speaker_labels is not None: _dict['speaker_labels'] = self.speaker_labels return _dict def __str__(self): """Return a `str` version of this SupportedFeatures object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Word(object): """ Word. :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. """ def __init__(self, word, sounds_like, display_as, count, source, error=None): """ Initialize a Word object. :param str word: A word from the custom model's words resource. The spelling of the word is used to train the model. :param 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. :param 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. :param 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. :param 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`. :param 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. """ self.word = word self.sounds_like = sounds_like self.display_as = display_as self.count = count self.source = source self.error = error @classmethod def _from_dict(cls, _dict): """Initialize a Word object from a json dictionary.""" args = {} if 'word' in _dict: args['word'] = _dict.get('word') else: raise ValueError( 'Required property \'word\' not present in Word JSON') if 'sounds_like' in _dict: args['sounds_like'] = _dict.get('sounds_like') else: raise ValueError( 'Required property \'sounds_like\' not present in Word JSON') if 'display_as' in _dict: args['display_as'] = _dict.get('display_as') else: raise ValueError( 'Required property \'display_as\' not present in Word JSON') if 'count' in _dict: args['count'] = _dict.get('count') else: raise ValueError( 'Required property \'count\' not present in Word JSON') if 'source' in _dict: args['source'] = _dict.get('source') else: raise ValueError( 'Required property \'source\' not present in Word JSON') if 'error' in _dict: args['error'] = [ WordError._from_dict(x) for x in (_dict.get('error')) ] return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'word') and self.word is not None: _dict['word'] = self.word if hasattr(self, 'sounds_like') and self.sounds_like is not None: _dict['sounds_like'] = self.sounds_like if hasattr(self, 'display_as') and self.display_as is not None: _dict['display_as'] = self.display_as if hasattr(self, 'count') and self.count is not None: _dict['count'] = self.count if hasattr(self, 'source') and self.source is not None: _dict['source'] = self.source if hasattr(self, 'error') and self.error is not None: _dict['error'] = [x._to_dict() for x in self.error] return _dict def __str__(self): """Return a `str` version of this Word object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordAlternativeResult(object): """ WordAlternativeResult. :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. """ def __init__(self, confidence, word): """ Initialize a WordAlternativeResult object. :param float confidence: A confidence score for the word alternative hypothesis in the range of 0.0 to 1.0. :param str word: An alternative hypothesis for a word from the input audio. """ self.confidence = confidence self.word = word @classmethod def _from_dict(cls, _dict): """Initialize a WordAlternativeResult object from a json dictionary.""" args = {} if 'confidence' in _dict: args['confidence'] = _dict.get('confidence') else: raise ValueError( 'Required property \'confidence\' not present in WordAlternativeResult JSON' ) if 'word' in _dict: args['word'] = _dict.get('word') else: raise ValueError( 'Required property \'word\' not present in WordAlternativeResult JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'confidence') and self.confidence is not None: _dict['confidence'] = self.confidence if hasattr(self, 'word') and self.word is not None: _dict['word'] = self.word return _dict def __str__(self): """Return a `str` version of this WordAlternativeResult object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordAlternativeResults(object): """ WordAlternativeResults. :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. """ def __init__(self, start_time, end_time, alternatives): """ Initialize a WordAlternativeResults object. :param float start_time: The start time in seconds of the word from the input audio that corresponds to the word alternatives. :param float end_time: The end time in seconds of the word from the input audio that corresponds to the word alternatives. :param list[WordAlternativeResult] alternatives: An array of alternative hypotheses for a word from the input audio. """ self.start_time = start_time self.end_time = end_time self.alternatives = alternatives @classmethod def _from_dict(cls, _dict): """Initialize a WordAlternativeResults object from a json dictionary.""" args = {} if 'start_time' in _dict: args['start_time'] = _dict.get('start_time') else: raise ValueError( 'Required property \'start_time\' not present in WordAlternativeResults JSON' ) if 'end_time' in _dict: args['end_time'] = _dict.get('end_time') else: raise ValueError( 'Required property \'end_time\' not present in WordAlternativeResults JSON' ) if 'alternatives' in _dict: args['alternatives'] = [ WordAlternativeResult._from_dict(x) for x in (_dict.get('alternatives')) ] else: raise ValueError( 'Required property \'alternatives\' not present in WordAlternativeResults JSON' ) return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'start_time') and self.start_time is not None: _dict['start_time'] = self.start_time if hasattr(self, 'end_time') and self.end_time is not None: _dict['end_time'] = self.end_time if hasattr(self, 'alternatives') and self.alternatives is not None: _dict['alternatives'] = [x._to_dict() for x in self.alternatives] return _dict def __str__(self): """Return a `str` version of this WordAlternativeResults object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordError(object): """ WordError. :attr str element: A key-value pair that describes an error associated with the definition of a word in the words resource. Each 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}'."` You must correct the error before you can train the model. """ def __init__(self, element): """ Initialize a WordError object. :param str element: A key-value pair that describes an error associated with the definition of a word in the words resource. Each 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}'."` You must correct the error before you can train the model. """ self.element = element @classmethod def _from_dict(cls, _dict): """Initialize a WordError object from a json dictionary.""" args = {} if 'element' in _dict: args['element'] = _dict.get('element') else: raise ValueError( 'Required property \'element\' not present in WordError JSON') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'element') and self.element is not None: _dict['element'] = self.element return _dict def __str__(self): """Return a `str` version of this WordError object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Words(object): """ Words. :attr list[Word] words: An array of objects that provides information about each word in the custom model's words resource. The array is empty if the custom model has no words. """ def __init__(self, words): """ Initialize a Words object. :param list[Word] words: An array of objects that provides information about each word in the custom model's words resource. The array is empty if the custom model has no words. """ self.words = words @classmethod def _from_dict(cls, _dict): """Initialize a Words object from a json dictionary.""" args = {} if 'words' in _dict: args['words'] = [Word._from_dict(x) for x in (_dict.get('words'))] else: raise ValueError( 'Required property \'words\' not present in Words JSON') return cls(**args) def _to_dict(self): """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'words') and self.words is not None: _dict['words'] = [x._to_dict() for x in self.words] return _dict def __str__(self): """Return a `str` version of this Words object.""" return json.dumps(self._to_dict(), indent=2) def __eq__(self, other): """Return `true` when self and other are equal, false otherwise.""" if not isinstance(other, self.__class__): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Return `true` when self and other are not equal, false otherwise.""" return not self == other