# coding: utf-8 # (C) Copyright IBM Corp. 2015, 2022. # # 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. # IBM OpenAPI SDK Code Generator Version: 3.53.0-9710cac3-20220713-193508 """ The IBM Watson™ Speech to Text service provides APIs that use IBM's speech-recognition capabilities to produce transcripts of spoken audio. The service can transcribe speech from various languages and audio formats. In addition to basic transcription, the service can produce detailed information about many different aspects of the audio. It returns all JSON response content in the UTF-8 character set. The service supports two types of models: previous-generation models that include the terms `Broadband` and `Narrowband` in their names, and next-generation models that include the terms `Multimedia` and `Telephony` in their names. Broadband and multimedia models have minimum sampling rates of 16 kHz. Narrowband and telephony models have minimum sampling rates of 8 kHz. The next-generation models offer high throughput and greater transcription accuracy. Effective 15 March 2022, previous-generation models for all languages other than Arabic and Japanese are deprecated. The deprecated models remain available until 15 September 2022, when they will be removed from the service and the documentation. You must migrate to the equivalent next-generation model by the end of service date. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate).{: deprecated} For speech recognition, the service supports synchronous and asynchronous HTTP Representational State Transfer (REST) interfaces. It also supports a WebSocket interface that provides a full-duplex, low-latency communication channel: Clients send requests and audio to the service and receive results over a single connection asynchronously. The service also offers two customization interfaces. Use language model customization to expand the vocabulary of a base model with domain-specific terminology. Use acoustic model customization to adapt a base model for the acoustic characteristics of your audio. For language model customization, the service also supports grammars. A grammar is a formal language specification that lets you restrict the phrases that the service can recognize. Language model customization and grammars are available for most previous- and next-generation models. Acoustic model customization is available for all previous-generation models. API Version: 1.0.0 See: https://cloud.ibm.com/docs/speech-to-text """ from enum import Enum from typing import BinaryIO, Dict, List import json from ibm_cloud_sdk_core import BaseService, DetailedResponse from ibm_cloud_sdk_core.authenticators.authenticator import Authenticator from ibm_cloud_sdk_core.get_authenticator import get_authenticator_from_environment from ibm_cloud_sdk_core.utils import convert_list, convert_model from .common import get_sdk_headers ############################################################################## # Service ############################################################################## class SpeechToTextV1(BaseService): """The Speech to Text V1 service.""" DEFAULT_SERVICE_URL = 'https://api.us-south.speech-to-text.watson.cloud.ibm.com' DEFAULT_SERVICE_NAME = 'speech_to_text' def __init__( self, authenticator: Authenticator = None, service_name: str = DEFAULT_SERVICE_NAME, ) -> None: """ Construct a new client for the Speech to Text service. :param Authenticator authenticator: The authenticator specifies the authentication mechanism. Get up to date information from https://github.com/IBM/python-sdk-core/blob/main/README.md about initializing the authenticator of your choice. """ if not authenticator: authenticator = get_authenticator_from_environment(service_name) BaseService.__init__(self, service_url=self.DEFAULT_SERVICE_URL, authenticator=authenticator) self.configure_service(service_name) ######################### # Models ######################### def list_models(self, **kwargs) -> DetailedResponse: """ List models. Lists all language models that are available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. The ordering of the list of models can change from call to call; do not rely on an alphabetized or static list of models. **See also:** [Listing all models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-list#models-list-all). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `SpeechModels` object """ headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_models') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/models' request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def get_model(self, model_id: str, **kwargs) -> DetailedResponse: """ Get a model. Gets information for a single specified language model that is available for use with the service. The information includes the name of the model and its minimum sampling rate in Hertz, among other things. **See also:** [Listing a specific model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-list#models-list-specific). :param str model_id: The identifier of the model in the form of its name from the output of the [List models](#listmodels) method. (**Note:** The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `SpeechModel` object """ if model_id is None: raise ValueError('model_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['model_id'] path_param_values = self.encode_path_vars(model_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/models/{model_id}'.format(**path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Synchronous ######################### def recognize(self, audio: BinaryIO, *, content_type: str = None, model: str = None, language_customization_id: str = None, acoustic_customization_id: str = None, base_model_version: str = None, customization_weight: float = None, inactivity_timeout: int = None, keywords: List[str] = None, keywords_threshold: float = None, max_alternatives: int = None, word_alternatives_threshold: float = None, word_confidence: bool = None, timestamps: bool = None, profanity_filter: bool = None, smart_formatting: bool = None, speaker_labels: bool = None, grammar_name: str = None, redaction: bool = None, audio_metrics: bool = None, end_of_phrase_silence_time: float = None, split_transcript_at_phrase_end: bool = None, speech_detector_sensitivity: float = None, background_audio_suppression: float = None, low_latency: bool = None, character_insertion_bias: float = None, **kwargs) -> DetailedResponse: """ Recognize audio. Sends audio and returns transcription results for a recognition request. You can pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the `curl` command, use the `--data-binary` option to upload the file for the request.) **See also:** [Making a basic HTTP request](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-http#HTTP-basic). ### Streaming mode For requests to transcribe live audio as it becomes available, you must set the `Transfer-Encoding` header to `chunked` to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for `inactivity_timeout` seconds of streaming audio; use the `inactivity_timeout` parameter to change the default of 30 seconds. **See also:** * [Audio transmission](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#transmission) * [Timeouts](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#timeouts) ### Audio formats (content types) The service accepts audio in the following formats (MIME types). * For formats that are labeled **Required**, you must use the `Content-Type` header with the request to specify the format of the audio. * For all other formats, you can omit the `Content-Type` header or specify `application/octet-stream` with the header to have the service automatically detect the format of the audio. (With the `curl` command, you can specify either `"Content-Type:"` or `"Content-Type: application/octet-stream"`.) Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio. * `audio/alaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/basic` (**Required.** Use only with narrowband models.) * `audio/flac` * `audio/g729` (Use only with narrowband models.) * `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the number of channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` * `audio/mpeg` * `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/ogg` (The service automatically detects the codec of the input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio with a maximum of nine channels.) * `audio/webm` (The service automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` * `audio/webm;codecs=vorbis` The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails. **See also:** [Supported audio formats](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formats). ### Next-generation models The service supports next-generation `Multimedia` (16 kHz) and `Telephony` (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of `Broadband` and `Narrowband` models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy. You specify a next-generation model by using the `model` query parameter, as you do a previous-generation model. Most next-generation models support the `low_latency` parameter, and all next-generation models support the `character_insertion_bias` parameter. These parameters are not available with previous-generation models. Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters: * `acoustic_customization_id` * `keywords` and `keywords_threshold` * `max_alternatives` * `processing_metrics` and `processing_metrics_interval` * `word_alternatives_threshold` **Important:** Effective 15 March 2022, previous-generation models for all languages other than Arabic and Japanese are deprecated. The deprecated models remain available until 15 September 2022, when they will be removed from the service and the documentation. You must migrate to the equivalent next-generation model by the end of service date. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate). **See also:** * [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng) * [Supported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng#models-ng-features) ### Multipart speech recognition **Note:** The asynchronous HTTP interface, WebSocket interface, and Watson SDKs do not support multipart speech recognition. The HTTP `POST` method of the service also supports multipart speech recognition. With multipart requests, you pass all audio data as multipart form data. You specify some parameters as request headers and query parameters, but you pass JSON metadata as form data to control most aspects of the transcription. You can use multipart recognition to pass multiple audio files with a single request. Use the multipart approach with browsers for which JavaScript is disabled or when the parameters used with the request are greater than the 8 KB limit imposed by most HTTP servers and proxies. You can encounter this limit, for example, if you want to spot a very large number of keywords. **See also:** [Making a multipart HTTP request](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-http#HTTP-multi). :param BinaryIO audio: The audio to transcribe. :param str content_type: (optional) The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. :param str model: (optional) The model to use for speech recognition. If you omit the `model` parameter, the service uses the US English `en-US_BroadbandModel` by default. (The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.) _For IBM Cloud Pak for Data,_ if you do not install the `en-US_BroadbandModel`, you must either specify a model with the request or specify a new default model for your installation of the service. **See also:** * [Using a model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use) * [The default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use#models-use-default). :param str language_customization_id: (optional) The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the `model` parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See [Using a custom language model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse). **Note:** Use this parameter instead of the deprecated `customization_id` parameter. :param str acoustic_customization_id: (optional) The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the `model` parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See [Using a custom acoustic model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticUse). :param str base_model_version: (optional) The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See [Making speech recognition requests with upgraded custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade-use#custom-upgrade-use-recognition). :param float customization_weight: (optional) If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request. Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when it was trained, the default value is 0.3. A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases. See [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse#weight). :param int inactivity_timeout: (optional) The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use `-1` for infinity. See [Inactivity timeout](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#timeouts-inactivity). :param List[str] keywords: (optional) An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords. You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive. See [Keyword spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#keyword-spotting). :param float keywords_threshold: (optional) A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See [Keyword spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#keyword-spotting). :param int max_alternatives: (optional) The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of `0`, the service uses the default value, `1`. See [Maximum alternatives](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#max-alternatives). :param float word_alternatives_threshold: (optional) A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See [Word alternatives](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#word-alternatives). :param bool word_confidence: (optional) If `true`, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See [Word confidence](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#word-confidence). :param bool timestamps: (optional) If `true`, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#word-timestamps). :param bool profanity_filter: (optional) If `true`, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to `false` to return results with no censoring. **Note:** The parameter can be used with US English and Japanese transcription only. See [Profanity filtering](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#profanity-filtering). :param bool smart_formatting: (optional) If `true`, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting. **Note:** The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only. See [Smart formatting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#smart-formatting). :param bool speaker_labels: (optional) If `true`, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. * _For previous-generation models,_ the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. * _For next-generation models,_ the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only. See [Speaker labels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-speaker-labels). :param str grammar_name: (optional) The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the `language_customization_id` parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource. See [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse). :param bool redaction: (optional) If `true`, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an `X` character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction. When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the `keywords` and `keywords_threshold` parameters) and returns only a single final transcript (forces the `max_alternatives` parameter to be `1`). **Note:** The parameter can be used with US English, Japanese, and Korean transcription only. See [Numeric redaction](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#numeric-redaction). :param bool audio_metrics: (optional) If `true`, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics. See [Audio metrics](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metrics#audio-metrics). :param float end_of_phrase_silence_time: (optional) If `true`, specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases. Specify a value for the pause interval in the range of 0.0 to 120.0. * A value greater than 0 specifies the interval that the service is to use for speech recognition. * A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter. The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds. See [End of phrase silence time](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#silence-time). :param bool split_transcript_at_phrase_end: (optional) If `true`, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript. By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, `end_of_phrase_silence_time` has precedence over `split_transcript_at_phrase_end`. See [Split transcript at phrase end](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#split-transcript). :param float speech_detector_sensitivity: (optional) The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity. Specify a value between 0.0 and 1.0: * 0.0 suppresses all audio (no speech is transcribed). * 0.5 (the default) provides a reasonable compromise for the level of sensitivity. * 1.0 suppresses no audio (speech detection sensitivity is disabled). The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, `0.55`) is typically more than sufficient. The parameter is supported with all next-generation models and with most previous-generation models. See [Speech detector sensitivity](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-parameters-sensitivity) and [Language model support](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-support). :param float background_audio_suppression: (optional) The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise. Specify a value in the range of 0.0 to 1.0: * 0.0 (the default) provides no suppression (background audio suppression is disabled). * 0.5 provides a reasonable level of audio suppression for general usage. * 1.0 suppresses all audio (no audio is transcribed). The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, `0.55`) is typically more than sufficient. The parameter is supported with all next-generation models and with most previous-generation models. See [Background audio suppression](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-parameters-suppression) and [Language model support](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-support). :param bool low_latency: (optional) If `true` for next-generation `Multimedia` and `Telephony` models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The `low_latency` parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used. The parameter is not available for previous-generation `Broadband` and `Narrowband` models. It is available for most next-generation models. * For a list of next-generation models that support low latency, see [Supported next-generation language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng#models-ng-supported). * For more information about the `low_latency` parameter, see [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interim#low-latency). :param float character_insertion_bias: (optional) For next-generation `Multimedia` and `Telephony` models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized for each individual model to balance its recognition of strings of different lengths. The model-specific bias is equivalent to 0.0. The value that you specify represents a change from a model's default bias. The allowable range of values is -1.0 to 1.0. * Negative values bias the service to favor hypotheses with shorter strings of characters. * Positive values bias the service to favor hypotheses with longer strings of characters. As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments. The parameter is not available for previous-generation `Broadband` and `Narrowband` models. See [Character insertion bias](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#insertion-bias). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `SpeechRecognitionResults` object """ if audio is None: raise ValueError('audio must be provided') headers = {'Content-Type': content_type} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='recognize') headers.update(sdk_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': 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, 'grammar_name': grammar_name, 'redaction': redaction, 'audio_metrics': audio_metrics, 'end_of_phrase_silence_time': end_of_phrase_silence_time, 'split_transcript_at_phrase_end': split_transcript_at_phrase_end, 'speech_detector_sensitivity': speech_detector_sensitivity, 'background_audio_suppression': background_audio_suppression, 'low_latency': low_latency, 'character_insertion_bias': character_insertion_bias } data = audio if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/recognize' request = self.prepare_request(method='POST', url=url, headers=headers, params=params, data=data) response = self.send(request, **kwargs) return response ######################### # Asynchronous ######################### def register_callback(self, callback_url: str, *, user_secret: str = None, **kwargs) -> DetailedResponse: """ Register a callback. Registers a callback URL with the service for use with subsequent asynchronous recognition requests. The service attempts to register, or allowlist, the callback URL if it is not already registered by sending a `GET` request to the callback URL. The service passes a random alphanumeric challenge string via the `challenge_string` parameter of the request. The request includes an `Accept` header that specifies `text/plain` as the required response type. To be registered successfully, the callback URL must respond to the `GET` request from the service. The response must send status code 200 and must include the challenge string in its body. Set the `Content-Type` response header to `text/plain`. Upon receiving this response, the service responds to the original registration request with response code 201. The service sends only a single `GET` request to the callback URL. If the service does not receive a reply with a response code of 200 and a body that echoes the challenge string sent by the service within five seconds, it does not allowlist the URL; it instead sends status code 400 in response to the request to register a callback. If the requested callback URL is already allowlisted, the service responds to the initial registration request with response code 200. If you specify a user secret with the request, the service uses it as a key to calculate an HMAC-SHA1 signature of the challenge string in its response to the `POST` request. It sends this signature in the `X-Callback-Signature` header of its `GET` request to the URL during registration. It also uses the secret to calculate a signature over the payload of every callback notification that uses the URL. The signature provides authentication and data integrity for HTTP communications. After you successfully register a callback URL, you can use it with an indefinite number of recognition requests. You can register a maximum of 20 callback URLS in a one-hour span of time. **See also:** [Registering a callback URL](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#register). :param str callback_url: An HTTP or HTTPS URL to which callback notifications are to be sent. To be allowlisted, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the `X-Callback-Signature` header to verify the origin of the request. :param str user_secret: (optional) A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the `X-Callback-Signature` header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RegisterStatus` object """ if callback_url is None: raise ValueError('callback_url must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='register_callback') headers.update(sdk_headers) params = {'callback_url': callback_url, 'user_secret': user_secret} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/register_callback' request = self.prepare_request(method='POST', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def unregister_callback(self, callback_url: str, **kwargs) -> DetailedResponse: """ Unregister a callback. Unregisters a callback URL that was previously allowlisted with a [Register a callback](#registercallback) request for use with the asynchronous interface. Once unregistered, the URL can no longer be used with asynchronous recognition requests. **See also:** [Unregistering a callback URL](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='unregister_callback') headers.update(sdk_headers) params = {'callback_url': callback_url} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] url = '/v1/unregister_callback' request = self.prepare_request(method='POST', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def create_job(self, audio: BinaryIO, *, content_type: str = None, model: str = None, callback_url: str = None, events: str = None, user_token: str = None, results_ttl: int = None, language_customization_id: str = None, acoustic_customization_id: str = None, base_model_version: str = None, customization_weight: float = None, inactivity_timeout: int = None, keywords: List[str] = None, keywords_threshold: float = None, max_alternatives: int = None, word_alternatives_threshold: float = None, word_confidence: bool = None, timestamps: bool = None, profanity_filter: bool = None, smart_formatting: bool = None, speaker_labels: bool = None, grammar_name: str = None, redaction: bool = None, processing_metrics: bool = None, processing_metrics_interval: float = None, audio_metrics: bool = None, end_of_phrase_silence_time: float = None, split_transcript_at_phrase_end: bool = None, speech_detector_sensitivity: float = None, background_audio_suppression: float = None, low_latency: bool = None, character_insertion_bias: float = None, **kwargs) -> DetailedResponse: """ Create a job. Creates a job for a new asynchronous recognition request. The job is owned by the instance of the service whose credentials are used to create it. How you learn the status and results of a job depends on the parameters you include with the job creation request: * By callback notification: Include the `callback_url` parameter to specify a URL to which the service is to send callback notifications when the status of the job changes. Optionally, you can also include the `events` and `user_token` parameters to subscribe to specific events and to specify a string that is to be included with each notification for the job. * By polling the service: Omit the `callback_url`, `events`, and `user_token` parameters. You must then use the [Check jobs](#checkjobs) or [Check a job](#checkjob) 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](#checkjob) method to retrieve results is more secure than receiving them via callback notification over HTTP because it provides confidentiality in addition to authentication and data integrity. The method supports the same basic parameters as other HTTP and WebSocket recognition requests. It also supports the following parameters specific to the asynchronous interface: * `callback_url` * `events` * `user_token` * `results_ttl` You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request. The service automatically detects the endianness of the incoming audio and, for audio that includes multiple channels, downmixes the audio to one-channel mono during transcoding. The method returns only final results; to enable interim results, use the WebSocket API. (With the `curl` command, use the `--data-binary` option to upload the file for the request.) **See also:** [Creating a job](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#create). ### Streaming mode For requests to transcribe live audio as it becomes available, you must set the `Transfer-Encoding` header to `chunked` to use streaming mode. In streaming mode, the service closes the connection (status code 408) if it does not receive at least 15 seconds of audio (including silence) in any 30-second period. The service also closes the connection (status code 400) if it detects no speech for `inactivity_timeout` seconds of streaming audio; use the `inactivity_timeout` parameter to change the default of 30 seconds. **See also:** * [Audio transmission](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#transmission) * [Timeouts](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#timeouts) ### Audio formats (content types) The service accepts audio in the following formats (MIME types). * For formats that are labeled **Required**, you must use the `Content-Type` header with the request to specify the format of the audio. * For all other formats, you can omit the `Content-Type` header or specify `application/octet-stream` with the header to have the service automatically detect the format of the audio. (With the `curl` command, you can specify either `"Content-Type:"` or `"Content-Type: application/octet-stream"`.) Where indicated, the format that you specify must include the sampling rate and can optionally include the number of channels and the endianness of the audio. * `audio/alaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/basic` (**Required.** Use only with narrowband models.) * `audio/flac` * `audio/g729` (Use only with narrowband models.) * `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the number of channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` * `audio/mpeg` * `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.) * `audio/ogg` (The service automatically detects the codec of the input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio with a maximum of nine channels.) * `audio/webm` (The service automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` * `audio/webm;codecs=vorbis` The sampling rate of the audio must match the sampling rate of the model for the recognition request: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the request fails. **See also:** [Supported audio formats](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formats). ### Next-generation models The service supports next-generation `Multimedia` (16 kHz) and `Telephony` (8 kHz) models for many languages. Next-generation models have higher throughput than the service's previous generation of `Broadband` and `Narrowband` models. When you use next-generation models, the service can return transcriptions more quickly and also provide noticeably better transcription accuracy. You specify a next-generation model by using the `model` query parameter, as you do a previous-generation model. Most next-generation models support the `low_latency` parameter, and all next-generation models support the `character_insertion_bias` parameter. These parameters are not available with previous-generation models. Next-generation models do not support all of the speech recognition parameters that are available for use with previous-generation models. Next-generation models do not support the following parameters: * `acoustic_customization_id` * `keywords` and `keywords_threshold` * `max_alternatives` * `processing_metrics` and `processing_metrics_interval` * `word_alternatives_threshold` **Important:** Effective 15 March 2022, previous-generation models for all languages other than Arabic and Japanese are deprecated. The deprecated models remain available until 15 September 2022, when they will be removed from the service and the documentation. You must migrate to the equivalent next-generation model by the end of service date. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate). **See also:** * [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng) * [Supported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng#models-ng-features). :param BinaryIO audio: The audio to transcribe. :param str content_type: (optional) The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. :param str model: (optional) The model to use for speech recognition. If you omit the `model` parameter, the service uses the US English `en-US_BroadbandModel` by default. (The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.) _For IBM Cloud Pak for Data,_ if you do not install the `en-US_BroadbandModel`, you must either specify a model with the request or specify a new default model for your installation of the service. **See also:** * [Using a model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use) * [The default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use#models-use-default). :param str callback_url: (optional) A URL to which callback notifications are to be sent. The URL must already be successfully allowlisted by using the [Register a callback](#registercallback) 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: (optional) If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are * `recognitions.started` generates a callback notification when the service begins to process the job. * `recognitions.completed` generates a callback notification when the job is complete. You must use the [Check a job](#checkjob) 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: (optional) If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter. :param int results_ttl: (optional) The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL. :param str language_customization_id: (optional) The customization ID (GUID) of a custom language model that is to be used with the recognition request. The base model of the specified custom language model must match the model specified with the `model` parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom language model is used. See [Using a custom language model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse). **Note:** Use this parameter instead of the deprecated `customization_id` parameter. :param str acoustic_customization_id: (optional) The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request. The base model of the specified custom acoustic model must match the model specified with the `model` parameter. You must make the request with credentials for the instance of the service that owns the custom model. By default, no custom acoustic model is used. See [Using a custom acoustic model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticUse). :param str base_model_version: (optional) The version of the specified base model that is to be used with the recognition request. Multiple versions of a base model can exist when a model is updated for internal improvements. The parameter is intended primarily for use with custom models that have been upgraded for a new base model. The default value depends on whether the parameter is used with or without a custom model. See [Making speech recognition requests with upgraded custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade-use#custom-upgrade-use-recognition). :param float customization_weight: (optional) If you specify the customization ID (GUID) of a custom language model with the recognition request, the customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for the current request. Specify a value between 0.0 and 1.0. Unless a different customization weight was specified for the custom model when it was trained, the default value is 0.3. A customization weight that you specify overrides a weight that was specified when the custom model was trained. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases. See [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse#weight). :param int inactivity_timeout: (optional) The time in seconds after which, if only silence (no speech) is detected in streaming audio, the connection is closed with a 400 error. The parameter is useful for stopping audio submission from a live microphone when a user simply walks away. Use `-1` for infinity. See [Inactivity timeout](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-input#timeouts-inactivity). :param List[str] keywords: (optional) An array of keyword strings to spot in the audio. Each keyword string can include one or more string tokens. Keywords are spotted only in the final results, not in interim hypotheses. If you specify any keywords, you must also specify a keywords threshold. Omit the parameter or specify an empty array if you do not need to spot keywords. You can spot a maximum of 1000 keywords with a single request. A single keyword can have a maximum length of 1024 characters, though the maximum effective length for double-byte languages might be shorter. Keywords are case-insensitive. See [Keyword spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#keyword-spotting). :param float keywords_threshold: (optional) A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. If you specify a threshold, you must also specify one or more keywords. The service performs no keyword spotting if you omit either parameter. See [Keyword spotting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#keyword-spotting). :param int max_alternatives: (optional) The maximum number of alternative transcripts that the service is to return. By default, the service returns a single transcript. If you specify a value of `0`, the service uses the default value, `1`. See [Maximum alternatives](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#max-alternatives). :param float word_alternatives_threshold: (optional) A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as "Confusion Networks"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0.0 and 1.0. By default, the service computes no alternative words. See [Word alternatives](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-spotting#word-alternatives). :param bool word_confidence: (optional) If `true`, the service returns a confidence measure in the range of 0.0 to 1.0 for each word. By default, the service returns no word confidence scores. See [Word confidence](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#word-confidence). :param bool timestamps: (optional) If `true`, the service returns time alignment for each word. By default, no timestamps are returned. See [Word timestamps](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metadata#word-timestamps). :param bool profanity_filter: (optional) If `true`, the service filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to `false` to return results with no censoring. **Note:** The parameter can be used with US English and Japanese transcription only. See [Profanity filtering](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#profanity-filtering). :param bool smart_formatting: (optional) If `true`, the service converts dates, times, series of digits and numbers, phone numbers, currency values, and internet addresses into more readable, conventional representations in the final transcript of a recognition request. For US English, the service also converts certain keyword strings to punctuation symbols. By default, the service performs no smart formatting. **Note:** The parameter can be used with US English, Japanese, and Spanish (all dialects) transcription only. See [Smart formatting](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#smart-formatting). :param bool speaker_labels: (optional) If `true`, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, the service returns no speaker labels. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. * _For previous-generation models,_ the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. * _For next-generation models,_ the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only. See [Speaker labels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-speaker-labels). :param str grammar_name: (optional) The name of a grammar that is to be used with the recognition request. If you specify a grammar, you must also use the `language_customization_id` parameter to specify the name of the custom language model for which the grammar is defined. The service recognizes only strings that are recognized by the specified grammar; it does not recognize other custom words from the model's words resource. See [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse). :param bool redaction: (optional) If `true`, the service redacts, or masks, numeric data from final transcripts. The feature redacts any number that has three or more consecutive digits by replacing each digit with an `X` character. It is intended to redact sensitive numeric data, such as credit card numbers. By default, the service performs no redaction. When you enable redaction, the service automatically enables smart formatting, regardless of whether you explicitly disable that feature. To ensure maximum security, the service also disables keyword spotting (ignores the `keywords` and `keywords_threshold` parameters) and returns only a single final transcript (forces the `max_alternatives` parameter to be `1`). **Note:** The parameter can be used with US English, Japanese, and Korean transcription only. See [Numeric redaction](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-formatting#numeric-redaction). :param bool processing_metrics: (optional) If `true`, requests processing metrics about the service's transcription of the input audio. The service returns processing metrics at the interval specified by the `processing_metrics_interval` parameter. It also returns processing metrics for transcription events, for example, for final and interim results. By default, the service returns no processing metrics. See [Processing metrics](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metrics#processing-metrics). :param float processing_metrics_interval: (optional) Specifies the interval in real wall-clock seconds at which the service is to return processing metrics. The parameter is ignored unless the `processing_metrics` parameter is set to `true`. The parameter accepts a minimum value of 0.1 seconds. The level of precision is not restricted, so you can specify values such as 0.25 and 0.125. The service does not impose a maximum value. If you want to receive processing metrics only for transcription events instead of at periodic intervals, set the value to a large number. If the value is larger than the duration of the audio, the service returns processing metrics only for transcription events. See [Processing metrics](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metrics#processing-metrics). :param bool audio_metrics: (optional) If `true`, requests detailed information about the signal characteristics of the input audio. The service returns audio metrics with the final transcription results. By default, the service returns no audio metrics. See [Audio metrics](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-metrics#audio-metrics). :param float end_of_phrase_silence_time: (optional) If `true`, specifies the duration of the pause interval at which the service splits a transcript into multiple final results. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. Silence indicates a point at which the speaker pauses between spoken words or phrases. Specify a value for the pause interval in the range of 0.0 to 120.0. * A value greater than 0 specifies the interval that the service is to use for speech recognition. * A value of 0 indicates that the service is to use the default interval. It is equivalent to omitting the parameter. The default pause interval for most languages is 0.8 seconds; the default for Chinese is 0.6 seconds. See [End of phrase silence time](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#silence-time). :param bool split_transcript_at_phrase_end: (optional) If `true`, directs the service to split the transcript into multiple final results based on semantic features of the input, for example, at the conclusion of meaningful phrases such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript. By default, the service splits transcripts based solely on the pause interval. If the parameters are used together on the same request, `end_of_phrase_silence_time` has precedence over `split_transcript_at_phrase_end`. See [Split transcript at phrase end](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#split-transcript). :param float speech_detector_sensitivity: (optional) The sensitivity of speech activity detection that the service is to perform. Use the parameter to suppress word insertions from music, coughing, and other non-speech events. The service biases the audio it passes for speech recognition by evaluating the input audio against prior models of speech and non-speech activity. Specify a value between 0.0 and 1.0: * 0.0 suppresses all audio (no speech is transcribed). * 0.5 (the default) provides a reasonable compromise for the level of sensitivity. * 1.0 suppresses no audio (speech detection sensitivity is disabled). The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, `0.55`) is typically more than sufficient. The parameter is supported with all next-generation models and with most previous-generation models. See [Speech detector sensitivity](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-parameters-sensitivity) and [Language model support](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-support). :param float background_audio_suppression: (optional) The level to which the service is to suppress background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise. Specify a value in the range of 0.0 to 1.0: * 0.0 (the default) provides no suppression (background audio suppression is disabled). * 0.5 provides a reasonable level of audio suppression for general usage. * 1.0 suppresses all audio (no audio is transcribed). The values increase on a monotonic curve. Specifying one or two decimal places of precision (for example, `0.55`) is typically more than sufficient. The parameter is supported with all next-generation models and with most previous-generation models. See [Background audio suppression](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-parameters-suppression) and [Language model support](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-detection#detection-support). :param bool low_latency: (optional) If `true` for next-generation `Multimedia` and `Telephony` models that support low latency, directs the service to produce results even more quickly than it usually does. Next-generation models produce transcription results faster than previous-generation models. The `low_latency` parameter causes the models to produce results even more quickly, though the results might be less accurate when the parameter is used. The parameter is not available for previous-generation `Broadband` and `Narrowband` models. It is available for most next-generation models. * For a list of next-generation models that support low latency, see [Supported next-generation language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng#models-ng-supported). * For more information about the `low_latency` parameter, see [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interim#low-latency). :param float character_insertion_bias: (optional) For next-generation `Multimedia` and `Telephony` models, an indication of whether the service is biased to recognize shorter or longer strings of characters when developing transcription hypotheses. By default, the service is optimized for each individual model to balance its recognition of strings of different lengths. The model-specific bias is equivalent to 0.0. The value that you specify represents a change from a model's default bias. The allowable range of values is -1.0 to 1.0. * Negative values bias the service to favor hypotheses with shorter strings of characters. * Positive values bias the service to favor hypotheses with longer strings of characters. As the value approaches -1.0 or 1.0, the impact of the parameter becomes more pronounced. To determine the most effective value for your scenario, start by setting the value of the parameter to a small increment, such as -0.1, -0.05, 0.05, or 0.1, and assess how the value impacts the transcription results. Then experiment with different values as necessary, adjusting the value by small increments. The parameter is not available for previous-generation `Broadband` and `Narrowband` models. See [Character insertion bias](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-parsing#insertion-bias). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RecognitionJob` object """ if audio is None: raise ValueError('audio must be provided') headers = {'Content-Type': content_type} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='create_job') headers.update(sdk_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': 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, 'grammar_name': grammar_name, 'redaction': redaction, 'processing_metrics': processing_metrics, 'processing_metrics_interval': processing_metrics_interval, 'audio_metrics': audio_metrics, 'end_of_phrase_silence_time': end_of_phrase_silence_time, 'split_transcript_at_phrase_end': split_transcript_at_phrase_end, 'speech_detector_sensitivity': speech_detector_sensitivity, 'background_audio_suppression': background_audio_suppression, 'low_latency': low_latency, 'character_insertion_bias': character_insertion_bias } data = audio if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/recognitions' request = self.prepare_request(method='POST', url=url, headers=headers, params=params, data=data) response = self.send(request, **kwargs) return response def check_jobs(self, **kwargs) -> DetailedResponse: """ Check jobs. Returns the ID and status of the latest 100 outstanding jobs associated with the credentials with which it is called. The method also returns the creation and update times of each job, and, if a job was created with a callback URL and a user token, the user token for the job. To obtain the results for a job whose status is `completed` or not one of the latest 100 outstanding jobs, use the [Check a job[(#checkjob) method. A job and its results remain available until you delete them with the [Delete a job](#deletejob) method or until the job's time to live expires, whichever comes first. **See also:** [Checking the status of the latest jobs](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#jobs). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RecognitionJobs` object """ headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='check_jobs') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/recognitions' request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def check_job(self, id: str, **kwargs) -> DetailedResponse: """ Check a job. Returns information about the specified job. The response always includes the status of the job and its creation and update times. If the status is `completed`, the response includes the results of the recognition request. You must use credentials for the instance of the service that owns a job to list information about it. You can use the method to retrieve the results of any job, regardless of whether it was submitted with a callback URL and the `recognitions.completed_with_results` event, and you can retrieve the results multiple times for as long as they remain available. Use the [Check jobs](#checkjobs) method to request information about the most recent jobs associated with the calling credentials. **See also:** [Checking the status and retrieving the results of a job](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#job). :param str id: The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `RecognitionJob` object """ if id is None: raise ValueError('id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='check_job') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['id'] path_param_values = self.encode_path_vars(id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/recognitions/{id}'.format(**path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_job(self, id: str, **kwargs) -> DetailedResponse: """ Delete a job. Deletes the specified job. You cannot delete a job that the service is actively processing. Once you delete a job, its results are no longer available. The service automatically deletes a job and its results when the time to live for the results expires. You must use credentials for the instance of the service that owns a job to delete it. **See also:** [Deleting a job](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-async#delete-async). :param str id: The identifier of the asynchronous job that is to be used for the request. You must make the request with credentials for the instance of the service that owns the job. :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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_job') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] path_param_keys = ['id'] path_param_values = self.encode_path_vars(id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/recognitions/{id}'.format(**path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Custom language models ######################### def create_language_model(self, name: str, base_model_name: str, *, dialect: str = None, description: str = None, **kwargs) -> DetailedResponse: """ Create a custom language model. Creates a new custom language model for a specified base model. The custom language model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it. You can create a maximum of 1024 custom language models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit. **Important:** Effective 15 March 2022, previous-generation models for all languages other than Arabic and Japanese are deprecated. The deprecated models remain available until 15 September 2022, when they will be removed from the service and the documentation. You must migrate to the equivalent next-generation model by the end of service date. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate). **See also:** * [Create a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate#createModel-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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](#getmodel) method and check that the attribute `custom_language_model` is set to `true`. You can also refer to [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :param str dialect: (optional) The dialect of the specified language that is to be used with the custom language model. _For all languages, it is always safe to omit this field._ The service automatically uses the language identifier from the name of the base model. For example, the service automatically uses `en-US` for all US English models. If you specify the `dialect` for a new custom model, follow these guidelines. _For non-Spanish previous-generation models and for next-generation models,_ you must specify a value that matches the five-character language identifier from the name of the base model. _For Spanish previous-generation models,_ you must specify one of the following values: * `es-ES` for Castilian Spanish (`es-ES` models) * `es-LA` for Latin American Spanish (`es-AR`, `es-CL`, `es-CO`, and `es-PE` models) * `es-US` for Mexican (North American) Spanish (`es-MX` models) All values that you pass for the `dialect` field are case-insensitive. :param str description: (optional) 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 with `dict` result representing a `LanguageModel` object """ 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='create_language_model') headers.update(sdk_headers) data = { 'name': name, 'base_model_name': base_model_name, 'dialect': dialect, 'description': description } data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/customizations' request = self.prepare_request(method='POST', url=url, headers=headers, data=data) response = self.send(request, **kwargs) return response def list_language_models(self, *, language: str = None, **kwargs) -> DetailedResponse: """ List custom language models. Lists information about all custom language models that are owned by an instance of the service. Use the `language` parameter to see all custom language models for the specified language. Omit the parameter to see all custom language models for all languages. You must use credentials for the instance of the service that owns a model to list information about it. **See also:** * [Listing custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :param str language: (optional) The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify `en-US` to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials. (**Note:** The identifier `ar-AR` is deprecated; use `ar-MS` instead.) To determine the languages for which customization is available, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `LanguageModels` object """ headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_language_models') headers.update(sdk_headers) params = {'language': language} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/customizations' request = self.prepare_request(method='GET', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def get_language_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Get a custom language model. Gets information about a specified custom language model. You must use credentials for the instance of the service that owns a model to list information about it. **See also:** * [Listing custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials 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 with `dict` result representing a `LanguageModel` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_language_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}'.format(**path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_language_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Delete a custom language model. Deletes an existing custom language model. The custom model cannot be deleted if another request, such as adding a corpus or grammar to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it. **See also:** * [Deleting a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageLanguageModels#deleteModel-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_language_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}'.format(**path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response def train_language_model(self, customization_id: str, *, word_type_to_add: str = None, customization_weight: float = None, strict: bool = None, **kwargs) -> DetailedResponse: """ Train a custom language model. Initiates the training of a custom language model with new resources such as corpora, grammars, and custom words. After adding, modifying, or deleting resources for a custom language model, use this method to begin the actual training of the model on the latest data. You can specify whether the custom language model is to be trained with all words from its words resource or only with words that were added or modified by the user directly. You must use credentials for the instance of the service that owns a model to train it. The training method is asynchronous. It can take on the order of minutes to complete depending on the amount of data on which the service is being trained and the current load on the service. The method returns an HTTP 200 response code to indicate that the training process has begun. You can monitor the status of the training by using the [Get a custom language model](#getlanguagemodel) method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a `LanguageModel` object that includes `status` and `progress` fields. A status of `available` means that the custom model is trained and ready to use. The service cannot accept subsequent training requests or requests to add new resources until the existing request completes. **See also:** * [Train the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate#trainModel-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support) ### Training failures Training can fail to start for the following reasons: * The service is currently handling another request for the custom model, such as another training request or a request to add a corpus or grammar to the model. * No training data have been added to the custom model. * The custom model contains one or more invalid corpora, grammars, or words (for example, a custom word has an invalid sounds-like pronunciation). You can correct the invalid resources or set the `strict` parameter to `false` to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed. :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 credentials for the instance of the service that owns the custom model. :param str word_type_to_add: (optional) _For custom models that are based on previous-generation models_, the type of words from the custom language model's words resource on which to train the model: * `all` (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user. * `user` trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars. _For custom models that are based on next-generation models_, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary. :param float customization_weight: (optional) Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0; the default is 0.3. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases. The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request. See [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse#weight). :param bool strict: (optional) If `false`, allows training of the custom language model to proceed as long as the model contains at least one valid resource. The method returns an array of `TrainingWarning` objects that lists any invalid resources. By default (`true`), training of a custom language model fails (status code 400) if the model contains one or more invalid resources (corpus files, grammar files, or custom words). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `TrainingResponse` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='train_language_model') headers.update(sdk_headers) params = { 'word_type_to_add': word_type_to_add, 'customization_weight': customization_weight, 'strict': strict } if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/train'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def reset_language_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Reset a custom language model. Resets a custom language model by removing all corpora, grammars, and words from the model. Resetting a custom language model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's words resource is removed and must be re-created. You must use credentials for the instance of the service that owns a model to reset it. **See also:** * [Resetting a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageLanguageModels#resetModel-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='reset_language_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/reset'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers) response = self.send(request, **kwargs) return response def upgrade_language_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ 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](#getlanguagemodel) method to poll the model's status. The method returns a `LanguageModel` object that includes `status` and `progress` fields. Use a loop to check the status every 10 seconds. While it is being upgraded, the custom model has the status `upgrading`. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot accept subsequent requests for the model until the upgrade completes. **See also:** * [Upgrading a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade#custom-upgrade-language) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='upgrade_language_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/upgrade_model'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Custom corpora ######################### def list_corpora(self, customization_id: str, **kwargs) -> DetailedResponse: """ List corpora. Lists information about all corpora from a custom language model. The information includes the name, status, and total number of words for each corpus. _For custom models that are based on previous-generation models_, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora. **See also:** [Listing corpora for a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageCorpora#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 credentials 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 with `dict` result representing a `Corpora` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_corpora') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/corpora'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def add_corpus(self, customization_id: str, corpus_name: str, corpus_file: BinaryIO, *, allow_overwrite: bool = None, **kwargs) -> DetailedResponse: """ 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](#trainlanguagemodel) method. Submit a plain text file that contains sample sentences from the domain of interest to enable the service to parse the words in context. The more sentences you add that represent the context in which speakers use words from the domain, the better the service's recognition accuracy. The call returns an HTTP 201 response code if the corpus is valid. The service then asynchronously processes and automatically extracts data from the contents of the corpus. This operation can take on the order of minutes to complete depending on the current load on the service, the total number of words in the corpus, and, _for custom models that are based on previous-generation models_, the number of new (out-of-vocabulary) words in the corpus. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the corpus for the current request completes. Use the [Get a corpus](#getcorpus) method to check the status of the analysis. _For custom models that are based on previous-generation models_, the service auto-populates the model's words resource with words from the corpus that are not found in its base vocabulary. These words are referred to as out-of-vocabulary (OOV) words. After adding a corpus, you must validate the words resource to ensure that each OOV word's definition is complete and valid. You can use the [List custom words](#listwords) method to examine the words resource. You can use other words method to eliminate typos and modify how words are pronounced and displayed as needed. To add a corpus file that has the same name as an existing corpus, set the `allow_overwrite` parameter to `true`; otherwise, the request fails. Overwriting an existing corpus causes the service to process the corpus text file and extract its data anew. _For a custom model that is based on a previous-generation model_, the service first removes any OOV words that are associated with the existing corpus from the model's words resource unless they were also added by another corpus or grammar, or they have been modified in some way with the [Add custom words](#addwords) or [Add a custom word](#addword) method. The service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. _For a custom model that is based on a previous-generation model_, you can add no more than 90 thousand custom (OOV) words to a model. This includes words that the service extracts from corpora and grammars, and words that you add directly. **See also:** * [Add a corpus to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate#addCorpus) * [Working with corpora for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#workingCorpora) * [Working with corpora for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#workingCorpora-ng) * [Validating a words resource for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#validateModel) * [Validating a words resource for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#validateModel-ng). :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 credentials 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 use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.) * Do not use the name of an existing corpus or grammar that is already defined for the custom model. * Do not use the name `user`, which is reserved by the service to denote custom words that are added or modified by the user. * Do not use the name `base_lm` or `default_lm`. Both names are reserved for future use by the service. :param BinaryIO 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 same encoding when working with the words in the custom language model. For more information, see [Character encoding for custom words](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWords#charEncoding). With the `curl` command, use the `--data-binary` option to upload the file for the request. :param bool allow_overwrite: (optional) If `true`, the specified corpus overwrites an existing corpus with the same name. If `false`, the request fails if a corpus with the same name already exists. The parameter has no effect if a corpus with the same name does not already exist. :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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='add_corpus') headers.update(sdk_headers) params = {'allow_overwrite': allow_overwrite} form_data = [] form_data.append(('corpus_file', (None, corpus_file, 'text/plain'))) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'corpus_name'] path_param_values = self.encode_path_vars(customization_id, corpus_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/corpora/{corpus_name}'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params, files=form_data) response = self.send(request, **kwargs) return response def get_corpus(self, customization_id: str, corpus_name: str, **kwargs) -> DetailedResponse: """ Get a corpus. Gets information about a corpus from a custom language model. The information includes the name, status, and total number of words for the corpus. _For custom models that are based on previous-generation models_, it also includes the number of out-of-vocabulary (OOV) words from the corpus. You must use credentials for the instance of the service that owns a model to list its corpora. **See also:** [Listing corpora for a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageCorpora#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 credentials 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 with `dict` result representing a `Corpus` object """ 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_corpus') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'corpus_name'] path_param_values = self.encode_path_vars(customization_id, corpus_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/corpora/{corpus_name}'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_corpus(self, customization_id: str, corpus_name: str, **kwargs) -> DetailedResponse: """ Delete a corpus. Deletes an existing corpus from a custom language model. Removing a corpus does not affect the custom model until you train the model with the [Train a custom language model](#trainlanguagemodel) method. You must use credentials for the instance of the service that owns a model to delete its corpora. _For custom models that are based on previous-generation models_, the service removes any out-of-vocabulary (OOV) words that are associated with the corpus from the custom model's words resource unless they were also added by another corpus or grammar, or they were modified in some way with the [Add custom words](#addwords) or [Add a custom word](#addword) method. **See also:** [Deleting a corpus from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageCorpora#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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_corpus') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'corpus_name'] path_param_values = self.encode_path_vars(customization_id, corpus_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/corpora/{corpus_name}'.format( **path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Custom words ######################### def list_words(self, customization_id: str, *, word_type: str = None, sort: str = None, **kwargs) -> DetailedResponse: """ List custom words. Lists information about custom words from a custom language model. You can list all words from the custom model's words resource, only custom words that were added or modified by the user, or, _for a custom model that is based on a previous-generation model_, only out-of-vocabulary (OOV) words that were extracted from corpora or are recognized by grammars. _For a custom model that is based on a next-generation model_, you can list all words or only those words that were added directly by a user, which return the same results. You can also indicate the order in which the service is to return words; by default, the service lists words in ascending alphabetical order. You must use credentials for the instance of the service that owns a model to list information about its words. **See also:** [Listing words from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWords#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 credentials for the instance of the service that owns the custom model. :param str word_type: (optional) The type of words to be listed from the custom language model's words resource: * `all` (the default) shows all words. * `user` shows only custom words that were added or modified by the user directly. * `corpora` shows only OOV that were extracted from corpora. * `grammars` shows only OOV words that are recognized by grammars. _For a custom model that is based on a next-generation model_, only `all` and `user` apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models. :param str sort: (optional) Indicates the order in which the words are to be listed, `alphabetical` or by `count`. You can prepend an optional `+` or `-` to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With the `curl` command, URL-encode the `+` symbol as `%2B`. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `Words` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_words') headers.update(sdk_headers) params = {'word_type': word_type, 'sort': sort} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/words'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def add_words(self, customization_id: str, words: List['CustomWord'], **kwargs) -> DetailedResponse: """ Add custom words. Adds one or more custom words to a custom language model. You can use this method to add words or to modify existing words in a custom model's words resource. _For custom models that are based on previous-generation models_, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource. _For a custom model that is based on a previous-generation model_, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly. You must use credentials for the instance of the service that owns a model to add or modify custom words for the model. Adding or modifying custom words does not affect the custom model until you train the model for the new data by using the [Train a custom language model](#trainlanguagemodel) method. You add custom words by providing a `CustomWords` object, which is an array of `CustomWord` objects, one per word. Use the object's `word` parameter to identify the word that is to be added. You can also provide one or both of the optional `display_as` or `sounds_like` fields for each word. * The `display_as` field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word `IBM` is to be displayed as `IBM™`. * The `sounds_like` field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word `IEEE` can sound like `I triple E`. You can specify a maximum of five sounds-like pronunciations for a word. _For a custom model that is based on a previous-generation model_, if you omit the `sounds_like` field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid. If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error with the input data, it returns a failure code and does not add any of the words to the words resource. The call returns an HTTP 201 response code if the input data is valid. It then asynchronously processes the words to add them to the model's words resource. The time that it takes for the analysis to complete depends on the number of new words that you add but is generally faster than adding a corpus or grammar. You can monitor the status of the request by using the [Get a custom language model](#getlanguagemodel) method to poll the model's status. Use a loop to check the status every 10 seconds. The method returns a `Customization` object that includes a `status` field. A status of `ready` means that the words have been added to the custom model. The service cannot accept requests to add new data or to train the model until the existing request completes. You can use the [List custom words](#listwords) or [Get a custom word](#getword) 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:** * [Add words to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate#addWords) * [Working with custom words for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#workingWords) * [Working with custom words for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#workingWords-ng) * [Validating a words resource for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#validateModel) * [Validating a words resource for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#validateModel-ng). :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 credentials for the instance of the service that owns the custom model. :param List[CustomWord] words: An array of `CustomWord` objects that provides information about each custom word that is to be added to or updated in the custom language model. :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 = [convert_model(x) for x in words] headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='add_words') headers.update(sdk_headers) data = {'words': words} data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/words'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, data=data) response = self.send(request, **kwargs) return response def add_word(self, customization_id: str, word_name: str, *, word: str = None, sounds_like: List[str] = None, display_as: str = None, **kwargs) -> DetailedResponse: """ Add a custom word. Adds a custom word to a custom language model. You can use this method to add a word or to modify an existing word in the words resource. _For custom models that are based on previous-generation models_, the service populates the words resource for a custom model with out-of-vocabulary (OOV) words from each corpus or grammar that is added to the model. You can use this method to modify OOV words in the model's words resource. _For a custom model that is based on a previous-generation models_, the words resource for a model can contain a maximum of 90 thousand custom (OOV) words. This includes words that the service extracts from corpora and grammars and words that you add directly. You must use credentials for the instance of the service that owns a model to add or modify a custom word for the model. Adding or modifying a custom word does not affect the custom model until you train the model for the new data by using the [Train a custom language model](#trainlanguagemodel) method. Use the `word_name` parameter to specify the custom word that is to be added or modified. Use the `CustomWord` object to provide one or both of the optional `display_as` or `sounds_like` fields for the word. * The `display_as` field provides a different way of spelling the word in a transcript. Use the parameter when you want the word to appear different from its usual representation or from its spelling in training data. For example, you might indicate that the word `IBM` is to be displayed as `IBM™`. * The `sounds_like` field provides an array of one or more pronunciations for the word. Use the parameter to specify how the word can be pronounced by users. Use the parameter for words that are difficult to pronounce, foreign words, acronyms, and so on. For example, you might specify that the word `IEEE` can sound like `i triple e`. You can specify a maximum of five sounds-like pronunciations for a word. _For custom models that are based on previous-generation models_, if you omit the `sounds_like` field, the service attempts to set the field to its pronunciation of the word. It cannot generate a pronunciation for all words, so you must review the word's definition to ensure that it is complete and valid. If you add a custom word that already exists in the words resource for the custom model, the new definition overwrites the existing data for the word. If the service encounters an error, it does not add the word to the words resource. Use the [Get a custom word](#getword) method to review the word that you add. **See also:** * [Add words to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate#addWords) * [Working with custom words for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#workingWords) * [Working with custom words for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#workingWords-ng) * [Validating a words resource for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#validateModel) * [Validating a words resource for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords-ng#validateModel-ng). :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 credentials 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](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#charEncoding). :param str word: (optional) For the [Add custom words](#addwords) 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](#addword) method. :param List[str] sounds_like: (optional) As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. * _For custom models that are based on previous-generation models_, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. * For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters not including 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. _For custom models that are based on next-generation models_, the service uses the spelling of the word as the display-as value if you omit the field. :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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='add_word') headers.update(sdk_headers) data = { 'word': word, 'sounds_like': sounds_like, 'display_as': display_as } data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'word_name'] path_param_values = self.encode_path_vars(customization_id, word_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/words/{word_name}'.format( **path_param_dict) request = self.prepare_request(method='PUT', url=url, headers=headers, data=data) response = self.send(request, **kwargs) return response def get_word(self, customization_id: str, word_name: str, **kwargs) -> DetailedResponse: """ Get a custom word. Gets information about a custom word from a custom language model. You must use credentials for the instance of the service that owns a model to list information about its words. **See also:** [Listing words from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWords#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 credentials 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](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#charEncoding). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `Word` object """ 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_word') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'word_name'] path_param_values = self.encode_path_vars(customization_id, word_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/words/{word_name}'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_word(self, customization_id: str, word_name: str, **kwargs) -> DetailedResponse: """ Delete a custom word. Deletes a custom word from a custom language model. You can remove any word that you added to the custom model's words resource via any means. However, if the word also exists in the service's base vocabulary, the service removes the word only from the words resource; the word remains in the base vocabulary. Removing a custom word does not affect the custom model until you train the model with the [Train a custom language model](#trainlanguagemodel) method. You must use credentials for the instance of the service that owns a model to delete its words. **See also:** [Deleting a word from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWords#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 credentials 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](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWords#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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_word') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'word_name'] path_param_values = self.encode_path_vars(customization_id, word_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/words/{word_name}'.format( **path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Custom grammars ######################### def list_grammars(self, customization_id: str, **kwargs) -> DetailedResponse: """ List grammars. Lists information about all grammars from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars. **See also:** * [Listing grammars from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials 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 with `dict` result representing a `Grammars` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_grammars') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/grammars'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def add_grammar(self, customization_id: str, grammar_name: str, grammar_file: BinaryIO, content_type: str, *, allow_overwrite: bool = None, **kwargs) -> DetailedResponse: """ Add a grammar. Adds a single grammar file to a custom language model. Submit a plain text file in UTF-8 format that defines the grammar. Use multiple requests to submit multiple grammar files. You must use credentials for the instance of the service that owns a model to add a grammar to it. Adding a grammar does not affect the custom language model until you train the model for the new data by using the [Train a custom language model](#trainlanguagemodel) method. The call returns an HTTP 201 response code if the grammar is valid. The service then asynchronously processes the contents of the grammar and automatically extracts new words that it finds. This operation can take a few seconds or minutes to complete depending on the size and complexity of the grammar, as well as the current load on the service. You cannot submit requests to add additional resources to the custom model or to train the model until the service's analysis of the grammar for the current request completes. Use the [Get a grammar](#getgrammar) method to check the status of the analysis. _For grammars that are based on previous-generation models,_ the service populates the model's words resource with any word that is recognized by the grammar that is not found in the model's base vocabulary. These are referred to as out-of-vocabulary (OOV) words. You can use the [List custom words](#listwords) method to examine the words resource and use other words-related methods to eliminate typos and modify how words are pronounced as needed. _For grammars that are based on next-generation models,_ the service extracts no OOV words from the grammars. To add a grammar that has the same name as an existing grammar, set the `allow_overwrite` parameter to `true`; otherwise, the request fails. Overwriting an existing grammar causes the service to process the grammar file and extract OOV words anew. Before doing so, it removes any OOV words associated with the existing grammar from the model's words resource unless they were also added by another resource or they have been modified in some way with the [Add custom words](#addwords) or [Add a custom word](#addword) method. _For grammars that are based on previous-generation models,_ the service limits the overall amount of data that you can add to a custom model to a maximum of 10 million total words from all sources combined. Also, you can add no more than 90 thousand OOV words to a model. This includes words that the service extracts from corpora and grammars and words that you add directly. **See also:** * [Understanding grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUnderstand#grammarUnderstand) * [Add a grammar to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarAdd#addGrammar) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials for the instance of the service that owns the custom model. :param str grammar_name: The name of the new grammar for the custom language model. Use a localized name that matches the language of the custom model and reflects the contents of the grammar. * Include a maximum of 128 characters in the name. * Do not use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.) * Do not use the name of an existing grammar or corpus that is already defined for the custom model. * Do not use the name `user`, which is reserved by the service to denote custom words that are added or modified by the user. * Do not use the name `base_lm` or `default_lm`. Both names are reserved for future use by the service. :param BinaryIO grammar_file: A plain text file that contains the grammar in the format specified by the `Content-Type` header. Encode the file in UTF-8 (ASCII is a subset of UTF-8). Using any other encoding can lead to issues when compiling the grammar or to unexpected results in decoding. The service ignores an encoding that is specified in the header of the grammar. With the `curl` command, use the `--data-binary` option to upload the file for the request. :param str content_type: The format (MIME type) of the grammar file: * `application/srgs` for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars. * `application/srgs+xml` for XML Form, which uses XML elements to represent the grammar. :param bool allow_overwrite: (optional) If `true`, the specified grammar overwrites an existing grammar with the same name. If `false`, the request fails if a grammar with the same name already exists. The parameter has no effect if a grammar with the same name does not already exist. :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 grammar_name is None: raise ValueError('grammar_name must be provided') if grammar_file is None: raise ValueError('grammar_file must be provided') if content_type is None: raise ValueError('content_type must be provided') headers = {'Content-Type': content_type} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='add_grammar') headers.update(sdk_headers) params = {'allow_overwrite': allow_overwrite} data = grammar_file if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'grammar_name'] path_param_values = self.encode_path_vars(customization_id, grammar_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/grammars/{grammar_name}'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params, data=data) response = self.send(request, **kwargs) return response def get_grammar(self, customization_id: str, grammar_name: str, **kwargs) -> DetailedResponse: """ Get a grammar. Gets information about a grammar from a custom language model. For each grammar, the information includes the name, status, and (for grammars that are based on previous-generation models) the total number of out-of-vocabulary (OOV) words. You must use credentials for the instance of the service that owns a model to list its grammars. **See also:** * [Listing grammars from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials for the instance of the service that owns the custom model. :param str grammar_name: The name of the grammar 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 with `dict` result representing a `Grammar` object """ if customization_id is None: raise ValueError('customization_id must be provided') if grammar_name is None: raise ValueError('grammar_name must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_grammar') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'grammar_name'] path_param_values = self.encode_path_vars(customization_id, grammar_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/grammars/{grammar_name}'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_grammar(self, customization_id: str, grammar_name: str, **kwargs) -> DetailedResponse: """ Delete a grammar. Deletes an existing grammar from a custom language model. _For grammars that are based on previous-generation models,_ the service removes any out-of-vocabulary (OOV) words associated with the grammar from the custom model's words resource unless they were also added by another resource or they were modified in some way with the [Add custom words](#addwords) or [Add a custom word](#addword) method. Removing a grammar does not affect the custom model until you train the model with the [Train a custom language model](#trainlanguagemodel) method. You must use credentials for the instance of the service that owns a model to delete its grammar. **See also:** * [Deleting a grammar from a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageGrammars#deleteGrammar) * [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :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 credentials for the instance of the service that owns the custom model. :param str grammar_name: The name of the grammar 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 grammar_name is None: raise ValueError('grammar_name must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_grammar') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'grammar_name'] path_param_values = self.encode_path_vars(customization_id, grammar_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/customizations/{customization_id}/grammars/{grammar_name}'.format( **path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # Custom acoustic models ######################### def create_acoustic_model(self, name: str, base_model_name: str, *, description: str = None, **kwargs) -> DetailedResponse: """ Create a custom acoustic model. Creates a new custom acoustic model for a specified base model. The custom acoustic model can be used only with the base model for which it is created. The model is owned by the instance of the service whose credentials are used to create it. You can create a maximum of 1024 custom acoustic models per owning credentials. The service returns an error if you attempt to create more than 1024 models. You do not lose any models, but you cannot create any more until your model count is below the limit. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **Important:** Effective 15 March 2022, previous-generation models for all languages other than Arabic and Japanese are deprecated. The deprecated models remain available until 15 September 2022, when they will be removed from the service and the documentation. You must migrate to the equivalent next-generation model by the end of service date. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate). **See also:** [Create a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acoustic#createModel-acoustic). :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. (**Note:** The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.) To determine whether a base model supports acoustic model customization, refer to [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :param str description: (optional) 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 with `dict` result representing a `AcousticModel` object """ 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='create_acoustic_model') headers.update(sdk_headers) data = { 'name': name, 'base_model_name': base_model_name, 'description': description } data = {k: v for (k, v) in data.items() if v is not None} data = json.dumps(data) headers['content-type'] = 'application/json' if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/acoustic_customizations' request = self.prepare_request(method='POST', url=url, headers=headers, data=data) response = self.send(request, **kwargs) return response def list_acoustic_models(self, *, language: str = None, **kwargs) -> DetailedResponse: """ List custom acoustic models. Lists information about all custom acoustic models that are owned by an instance of the service. Use the `language` parameter to see all custom acoustic models for the specified language. Omit the parameter to see all custom acoustic models for all languages. You must use credentials for the instance of the service that owns a model to list information about it. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Listing custom acoustic models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic). :param str language: (optional) The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify `en-US` to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials. (**Note:** The identifier `ar-AR` is deprecated; use `ar-MS` instead.) To determine the languages for which customization is available, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `AcousticModels` object """ headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_acoustic_models') headers.update(sdk_headers) params = {'language': language} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' url = '/v1/acoustic_customizations' request = self.prepare_request(method='GET', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def get_acoustic_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Get a custom acoustic model. Gets information about a specified custom acoustic model. You must use credentials for the instance of the service that owns a model to list information about it. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Listing custom acoustic models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic). :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 credentials 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 with `dict` result representing a `AcousticModel` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_acoustic_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_acoustic_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Delete a custom acoustic model. Deletes an existing custom acoustic model. The custom model cannot be deleted if another request, such as adding an audio resource to the model, is currently being processed. You must use credentials for the instance of the service that owns a model to delete it. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Deleting a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAcousticModels#deleteModel-acoustic). :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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_acoustic_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}'.format( **path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response def train_acoustic_model(self, customization_id: str, *, custom_language_model_id: str = None, strict: bool = None, **kwargs) -> DetailedResponse: """ Train a custom acoustic model. Initiates the training of a custom acoustic model with new or changed audio resources. After adding or deleting audio resources for a custom acoustic model, use this method to begin the actual training of the model on the latest audio data. The custom acoustic model does not reflect its changed data until you train it. You must use credentials for the instance of the service that owns a model to train it. The training method is asynchronous. Training time depends on the cumulative amount of audio data that the custom acoustic model contains and the current load on the service. When you train or retrain a model, the service uses all of the model's audio data in the training. Training a custom acoustic model takes approximately as long as the length of its cumulative audio data. For example, it takes approximately 2 hours to train a model that contains a total of 2 hours of audio. The method returns an HTTP 200 response code to indicate that the training process has begun. You can monitor the status of the training by using the [Get a custom acoustic model](#getacousticmodel) method to poll the model's status. Use a loop to check the status once a minute. The method returns an `AcousticModel` object that includes `status` and `progress` fields. A status of `available` indicates that the custom model is trained and ready to use. The service cannot train a model while it is handling another request for the model. The service cannot accept subsequent training requests, or requests to add new audio resources, until the existing training request completes. You can use the optional `custom_language_model_id` parameter to specify the GUID of a separately created custom language model that is to be used during training. Train with a custom language model if you have verbatim transcriptions of the audio files that you have added to the custom model or you have either corpora (text files) or a list of words that are relevant to the contents of the audio files. For training to succeed, both of the custom models must be based on the same version of the same base model, and the custom language model must be fully trained and available. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** * [Train the custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acoustic#trainModel-acoustic) * [Using custom acoustic and custom language models together](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-useBoth#useBoth) ### Training failures Training can fail to start for the following reasons: * The service is currently handling another request for the custom model, such as another training request or a request to add audio resources to the model. * The custom model contains less than 10 minutes or more than 200 hours of audio data. * You passed a custom language model with the `custom_language_model_id` query parameter that is not in the available state. A custom language model must be fully trained and available to be used to train a custom acoustic model. * You passed an incompatible custom language model with the `custom_language_model_id` query parameter. Both custom models must be based on the same version of the same base model. * The custom model contains one or more invalid audio resources. You can correct the invalid audio resources or set the `strict` parameter to `false` to exclude the invalid resources from the training. The model must contain at least one valid resource for training to succeed. :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 credentials for the instance of the service that owns the custom model. :param str custom_language_model_id: (optional) The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources. The custom language model must be based on the same version of the same base model as the custom acoustic model, and the custom language model must be fully trained and available. The credentials specified with the request must own both custom models. :param bool strict: (optional) If `false`, allows training of the custom acoustic model to proceed as long as the model contains at least one valid audio resource. The method returns an array of `TrainingWarning` objects that lists any invalid resources. By default (`true`), training of a custom acoustic model fails (status code 400) if the model contains one or more invalid audio resources. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse with `dict` result representing a `TrainingResponse` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='train_acoustic_model') headers.update(sdk_headers) params = { 'custom_language_model_id': custom_language_model_id, 'strict': strict } if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/train'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response def reset_acoustic_model(self, customization_id: str, **kwargs) -> DetailedResponse: """ Reset a custom acoustic model. Resets a custom acoustic model by removing all audio resources from the model. Resetting a custom acoustic model initializes the model to its state when it was first created. Metadata such as the name and language of the model are preserved, but the model's audio resources are removed and must be re-created. The service cannot reset a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing reset request completes. You must use credentials for the instance of the service that owns a model to reset it. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Resetting a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAcousticModels#resetModel-acoustic). :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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='reset_acoustic_model') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/reset'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers) response = self.send(request, **kwargs) return response def upgrade_acoustic_model(self, customization_id: str, *, custom_language_model_id: str = None, force: bool = None, **kwargs) -> DetailedResponse: """ 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](#getacousticmodel) method to poll the model's status. The method returns an `AcousticModel` object that includes `status` and `progress` fields. Use a loop to check the status once a minute. While it is being upgraded, the custom model has the status `upgrading`. When the upgrade is complete, the model resumes the status that it had prior to upgrade. The service cannot upgrade a model while it is handling another request for the model. The service cannot accept subsequent requests for the model until the existing upgrade request completes. If the custom acoustic model was trained with a separately created custom language model, you must use the `custom_language_model_id` parameter to specify the GUID of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. Omit the parameter if the custom acoustic model was not trained with a custom language model. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Upgrading a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade#custom-upgrade-acoustic). :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 credentials for the instance of the service that owns the custom model. :param str custom_language_model_id: (optional) If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded. The custom language model must be fully trained and available. The credentials specified with the request must own both custom models. :param bool force: (optional) If `true`, forces the upgrade of a custom acoustic model for which no input data has been modified since it was last trained. Use this parameter only to force the upgrade of a custom acoustic model that is trained with a custom language model, and only if you receive a 400 response code and the message `No input data modified since last training`. See [Upgrading a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade#custom-upgrade-acoustic). :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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='upgrade_acoustic_model') headers.update(sdk_headers) params = { 'custom_language_model_id': custom_language_model_id, 'force': force } if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/upgrade_model'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response ######################### # Custom audio resources ######################### def list_audio(self, customization_id: str, **kwargs) -> DetailedResponse: """ List audio resources. Lists information about all audio resources from a custom acoustic model. The information includes the name of the resource and information about its audio data, such as its duration. It also includes the status of the audio resource, which is important for checking the service's analysis of the resource in response to a request to add it to the custom acoustic model. You must use credentials for the instance of the service that owns a model to list its audio resources. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Listing audio resources for a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAudio#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 credentials 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 with `dict` result representing a `AudioResources` object """ if customization_id is None: raise ValueError('customization_id must be provided') headers = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='list_audio') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id'] path_param_values = self.encode_path_vars(customization_id) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/audio'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def add_audio(self, customization_id: str, audio_name: str, audio_resource: BinaryIO, *, content_type: str = None, contained_content_type: str = None, allow_overwrite: bool = None, **kwargs) -> DetailedResponse: """ 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](#trainacousticmodel) method. You can add individual audio files or an archive file that contains multiple audio files. Adding multiple audio files via a single archive file is significantly more efficient than adding each file individually. You can add audio resources in any format that the service supports for speech recognition. You can use this method to add any number of audio resources to a custom model by calling the method once for each audio or archive file. You can add multiple different audio resources at the same time. You must add a minimum of 10 minutes and a maximum of 200 hours of audio that includes speech, not just silence, to a custom acoustic model before you can train it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add an audio resource that has the same name as an existing audio resource, set the `allow_overwrite` parameter to `true`; otherwise, the request fails. The method is asynchronous. It can take several seconds or minutes to complete depending on the duration of the audio and, in the case of an archive file, the total number of audio files being processed. The service returns a 201 response code if the audio is valid. It then asynchronously analyzes the contents of the audio file or files and automatically extracts information about the audio such as its length, sampling rate, and encoding. You cannot submit requests to train or upgrade the model until the service's analysis of all audio resources for current requests completes. To determine the status of the service's analysis of the audio, use the [Get an audio resource](#getaudio) method to poll the status of the audio. The method accepts the customization ID of the custom model and the name of the audio resource, and it returns the status of the resource. Use a loop to check the status of the audio every few seconds until it becomes `ok`. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Add audio to the custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acoustic#addAudio). ### Content types for audio-type resources You can add an individual audio file in any format that the service supports for speech recognition. For an audio-type resource, use the `Content-Type` parameter to specify the audio format (MIME type) of the audio file, including specifying the sampling rate, channels, and endianness where indicated. * `audio/alaw` (Specify the sampling rate (`rate`) of the audio.) * `audio/basic` (Use only with narrowband models.) * `audio/flac` * `audio/g729` (Use only with narrowband models.) * `audio/l16` (Specify the sampling rate (`rate`) and optionally the number of channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` * `audio/mpeg` * `audio/mulaw` (Specify the sampling rate (`rate`) of the audio.) * `audio/ogg` (The service automatically detects the codec of the input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio with a maximum of nine channels.) * `audio/webm` (The service automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` * `audio/webm;codecs=vorbis` The sampling rate of an audio file must match the sampling rate of the base model for the custom model: for broadband models, at least 16 kHz; for narrowband models, at least 8 kHz. If the sampling rate of the audio is higher than the minimum required rate, the service down-samples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as `invalid`. **See also:** [Supported audio formats](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formats). ### Content types for archive-type resources You can add an archive file (**.zip** or **.tar.gz** file) that contains audio files in any format that the service supports for speech recognition. For an archive-type resource, use the `Content-Type` parameter to specify the media type of the archive file: * `application/zip` for a **.zip** file * `application/gzip` for a **.tar.gz** file. When you add an archive-type resource, the `Contained-Content-Type` header is optional depending on the format of the files that you are adding: * For audio files of type `audio/alaw`, `audio/basic`, `audio/l16`, or `audio/mulaw`, you must use the `Contained-Content-Type` header to specify the format of the contained audio files. Include the `rate`, `channels`, and `endianness` parameters where necessary. In this case, all audio files contained in the archive file must have the same audio format. * For audio files of all other types, you can omit the `Contained-Content-Type` header. In this case, the audio files contained in the archive file can have any of the formats not listed in the previous bullet. The audio files do not need to have the same format. Do not use the `Contained-Content-Type` header when adding an audio-type resource. ### Naming restrictions for embedded audio files The name of an audio file that is contained in an archive-type resource can include a maximum of 128 characters. This includes the file extension and all elements of the name (for example, slashes). :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 credentials 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 use characters that need to be URL-encoded. For example, do not use spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs, equals signs, questions marks, and so on in the name. (The service does not prevent the use of these characters. But because they must be URL-encoded wherever used, their use is strongly discouraged.) * Do not use the name of an audio resource that has already been added to the custom model. :param BinaryIO audio_resource: The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file. With the `curl` command, use the `--data-binary` option to upload the file for the request. :param str content_type: (optional) For an audio-type resource, the format (MIME type) of the audio. For more information, see **Content types for audio-type resources** in the method description. For an archive-type resource, the media type of the archive file. For more information, see **Content types for archive-type resources** in the method description. :param str contained_content_type: (optional) _For an archive-type resource_, specify the format of the audio files that are contained in the archive file if they are of type `audio/alaw`, `audio/basic`, `audio/l16`, or `audio/mulaw`. Include the `rate`, `channels`, and `endianness` parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type. For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph. The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see **Content types for audio-type resources** in the method description. _For an audio-type resource_, omit the header. :param bool allow_overwrite: (optional) If `true`, the specified audio resource overwrites an existing audio resource with the same name. If `false`, the request fails if an audio resource with the same name already exists. The parameter has no effect if an audio resource with the same name does not already exist. :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 } sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='add_audio') headers.update(sdk_headers) params = {'allow_overwrite': allow_overwrite} data = audio_resource if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'audio_name'] path_param_values = self.encode_path_vars(customization_id, audio_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/audio/{audio_name}'.format( **path_param_dict) request = self.prepare_request(method='POST', url=url, headers=headers, params=params, data=data) response = self.send(request, **kwargs) return response def get_audio(self, customization_id: str, audio_name: str, **kwargs) -> DetailedResponse: """ Get an audio resource. Gets information about an audio resource from a custom acoustic model. The method returns an `AudioListing` object whose fields depend on the type of audio resource that you specify with the method's `audio_name` parameter: * _For an audio-type resource_, the object's fields match those of an `AudioResource` object: `duration`, `name`, `details`, and `status`. * _For an archive-type resource_, the object includes a `container` field whose fields match those of an `AudioResource` object. It also includes an `audio` field, which contains an array of `AudioResource` objects that provides information about the audio files that are contained in the archive. The information includes the status of the specified audio resource. The status is important for checking the service's analysis of a resource that you add to the custom model. * _For an audio-type resource_, the `status` field is located in the `AudioListing` object. * _For an archive-type resource_, the `status` field is located in the `AudioResource` object that is returned in the `container` field. You must use credentials for the instance of the service that owns a model to list its audio resources. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Listing audio resources for a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAudio#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 credentials 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 with `dict` result representing a `AudioListing` object """ 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='get_audio') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'audio_name'] path_param_values = self.encode_path_vars(customization_id, audio_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/audio/{audio_name}'.format( **path_param_dict) request = self.prepare_request(method='GET', url=url, headers=headers) response = self.send(request, **kwargs) return response def delete_audio(self, customization_id: str, audio_name: str, **kwargs) -> DetailedResponse: """ Delete an audio resource. Deletes an existing audio resource from a custom acoustic model. Deleting an archive-type audio resource removes the entire archive of files. The service does not allow deletion of individual files from an archive resource. Removing an audio resource does not affect the custom model until you train the model on its updated data by using the [Train a custom acoustic model](#trainacousticmodel) method. You can delete an existing audio resource from a model while a different resource is being added to the model. You must use credentials for the instance of the service that owns a model to delete its audio resources. **Note:** Acoustic model customization is supported only for use with previous-generation models. It is not supported for next-generation models. **See also:** [Deleting an audio resource from a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageAudio#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 credentials 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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_audio') headers.update(sdk_headers) if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] headers['Accept'] = 'application/json' path_param_keys = ['customization_id', 'audio_name'] path_param_values = self.encode_path_vars(customization_id, audio_name) path_param_dict = dict(zip(path_param_keys, path_param_values)) url = '/v1/acoustic_customizations/{customization_id}/audio/{audio_name}'.format( **path_param_dict) request = self.prepare_request(method='DELETE', url=url, headers=headers) response = self.send(request, **kwargs) return response ######################### # User data ######################### def delete_user_data(self, customer_id: str, **kwargs) -> DetailedResponse: """ Delete labeled data. Deletes all data that is associated with a specified customer ID. The method deletes all data for the customer ID, regardless of the method by which the information was added. The method has no effect if no data is associated with the customer ID. You must issue the request with credentials for the same instance of the service that was used to associate the customer ID with the data. You associate a customer ID with data by passing the `X-Watson-Metadata` header with a request that passes the data. **Note:** If you delete an instance of the service from the service console, all data associated with that service instance is automatically deleted. This includes all custom language models, corpora, grammars, and words; all custom acoustic models and audio resources; all registered endpoints for the asynchronous HTTP interface; and all data related to speech recognition requests. **See also:** [Information security](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-information-security#information-security). :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 = {} sdk_headers = get_sdk_headers(service_name=self.DEFAULT_SERVICE_NAME, service_version='V1', operation_id='delete_user_data') headers.update(sdk_headers) params = {'customer_id': customer_id} if 'headers' in kwargs: headers.update(kwargs.get('headers')) del kwargs['headers'] url = '/v1/user_data' request = self.prepare_request(method='DELETE', url=url, headers=headers, params=params) response = self.send(request, **kwargs) return response class GetModelEnums: """ Enums for get_model parameters. """ class ModelId(str, Enum): """ The identifier of the model in the form of its name from the output of the [List models](#listmodels) method. (**Note:** The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.). """ AR_AR_BROADBANDMODEL = 'ar-AR_BroadbandModel' AR_MS_BROADBANDMODEL = 'ar-MS_BroadbandModel' AR_MS_TELEPHONY = 'ar-MS_Telephony' CS_CZ_TELEPHONY = 'cs-CZ_Telephony' DE_DE_BROADBANDMODEL = 'de-DE_BroadbandModel' DE_DE_MULTIMEDIA = 'de-DE_Multimedia' DE_DE_NARROWBANDMODEL = 'de-DE_NarrowbandModel' DE_DE_TELEPHONY = 'de-DE_Telephony' EN_AU_BROADBANDMODEL = 'en-AU_BroadbandModel' EN_AU_MULTIMEDIA = 'en-AU_Multimedia' EN_AU_NARROWBANDMODEL = 'en-AU_NarrowbandModel' EN_AU_TELEPHONY = 'en-AU_Telephony' EN_GB_BROADBANDMODEL = 'en-GB_BroadbandModel' EN_GB_MULTIMEDIA = 'en-GB_Multimedia' EN_GB_NARROWBANDMODEL = 'en-GB_NarrowbandModel' EN_GB_TELEPHONY = 'en-GB_Telephony' EN_IN_TELEPHONY = 'en-IN_Telephony' EN_US_BROADBANDMODEL = 'en-US_BroadbandModel' EN_US_MULTIMEDIA = 'en-US_Multimedia' EN_US_NARROWBANDMODEL = 'en-US_NarrowbandModel' EN_US_SHORTFORM_NARROWBANDMODEL = 'en-US_ShortForm_NarrowbandModel' EN_US_TELEPHONY = 'en-US_Telephony' EN_WW_MEDICAL_TELEPHONY = 'en-WW_Medical_Telephony' ES_AR_BROADBANDMODEL = 'es-AR_BroadbandModel' ES_AR_NARROWBANDMODEL = 'es-AR_NarrowbandModel' ES_CL_BROADBANDMODEL = 'es-CL_BroadbandModel' ES_CL_NARROWBANDMODEL = 'es-CL_NarrowbandModel' ES_CO_BROADBANDMODEL = 'es-CO_BroadbandModel' ES_CO_NARROWBANDMODEL = 'es-CO_NarrowbandModel' ES_ES_BROADBANDMODEL = 'es-ES_BroadbandModel' ES_ES_NARROWBANDMODEL = 'es-ES_NarrowbandModel' ES_ES_MULTIMEDIA = 'es-ES_Multimedia' ES_ES_TELEPHONY = 'es-ES_Telephony' ES_LA_TELEPHONY = 'es-LA_Telephony' ES_MX_BROADBANDMODEL = 'es-MX_BroadbandModel' ES_MX_NARROWBANDMODEL = 'es-MX_NarrowbandModel' ES_PE_BROADBANDMODEL = 'es-PE_BroadbandModel' ES_PE_NARROWBANDMODEL = 'es-PE_NarrowbandModel' FR_CA_BROADBANDMODEL = 'fr-CA_BroadbandModel' FR_CA_NARROWBANDMODEL = 'fr-CA_NarrowbandModel' FR_CA_TELEPHONY = 'fr-CA_Telephony' FR_FR_BROADBANDMODEL = 'fr-FR_BroadbandModel' FR_FR_MULTIMEDIA = 'fr-FR_Multimedia' FR_FR_NARROWBANDMODEL = 'fr-FR_NarrowbandModel' FR_FR_TELEPHONY = 'fr-FR_Telephony' HI_IN_TELEPHONY = 'hi-IN_Telephony' IT_IT_BROADBANDMODEL = 'it-IT_BroadbandModel' IT_IT_NARROWBANDMODEL = 'it-IT_NarrowbandModel' IT_IT_MULTIMEDIA = 'it-IT_Multimedia' IT_IT_TELEPHONY = 'it-IT_Telephony' JA_JP_BROADBANDMODEL = 'ja-JP_BroadbandModel' JA_JP_MULTIMEDIA = 'ja-JP_Multimedia' JA_JP_NARROWBANDMODEL = 'ja-JP_NarrowbandModel' KO_KR_BROADBANDMODEL = 'ko-KR_BroadbandModel' KO_KR_MULTIMEDIA = 'ko-KR_Multimedia' KO_KR_NARROWBANDMODEL = 'ko-KR_NarrowbandModel' KO_KR_TELEPHONY = 'ko-KR_Telephony' NL_BE_TELEPHONY = 'nl-BE_Telephony' NL_NL_BROADBANDMODEL = 'nl-NL_BroadbandModel' NL_NL_NARROWBANDMODEL = 'nl-NL_NarrowbandModel' NL_NL_TELEPHONY = 'nl-NL_Telephony' PT_BR_BROADBANDMODEL = 'pt-BR_BroadbandModel' PT_BR_MULTIMEDIA = 'pt-BR_Multimedia' PT_BR_NARROWBANDMODEL = 'pt-BR_NarrowbandModel' PT_BR_TELEPHONY = 'pt-BR_Telephony' ZH_CN_BROADBANDMODEL = 'zh-CN_BroadbandModel' ZH_CN_NARROWBANDMODEL = 'zh-CN_NarrowbandModel' ZH_CN_TELEPHONY = 'zh-CN_Telephony' class RecognizeEnums: """ Enums for recognize parameters. """ class ContentType(str, Enum): """ The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. """ APPLICATION_OCTET_STREAM = 'application/octet-stream' AUDIO_ALAW = 'audio/alaw' AUDIO_BASIC = 'audio/basic' AUDIO_FLAC = 'audio/flac' AUDIO_G729 = 'audio/g729' AUDIO_L16 = 'audio/l16' AUDIO_MP3 = 'audio/mp3' AUDIO_MPEG = 'audio/mpeg' AUDIO_MULAW = 'audio/mulaw' AUDIO_OGG = 'audio/ogg' AUDIO_OGG_CODECS_OPUS = 'audio/ogg;codecs=opus' AUDIO_OGG_CODECS_VORBIS = 'audio/ogg;codecs=vorbis' AUDIO_WAV = 'audio/wav' AUDIO_WEBM = 'audio/webm' AUDIO_WEBM_CODECS_OPUS = 'audio/webm;codecs=opus' AUDIO_WEBM_CODECS_VORBIS = 'audio/webm;codecs=vorbis' class Model(str, Enum): """ The model to use for speech recognition. If you omit the `model` parameter, the service uses the US English `en-US_BroadbandModel` by default. (The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.) _For IBM Cloud Pak for Data,_ if you do not install the `en-US_BroadbandModel`, you must either specify a model with the request or specify a new default model for your installation of the service. **See also:** * [Using a model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use) * [The default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use#models-use-default). """ AR_AR_BROADBANDMODEL = 'ar-AR_BroadbandModel' AR_MS_BROADBANDMODEL = 'ar-MS_BroadbandModel' AR_MS_TELEPHONY = 'ar-MS_Telephony' CS_CZ_TELEPHONY = 'cs-CZ_Telephony' DE_DE_BROADBANDMODEL = 'de-DE_BroadbandModel' DE_DE_MULTIMEDIA = 'de-DE_Multimedia' DE_DE_NARROWBANDMODEL = 'de-DE_NarrowbandModel' DE_DE_TELEPHONY = 'de-DE_Telephony' EN_AU_BROADBANDMODEL = 'en-AU_BroadbandModel' EN_AU_MULTIMEDIA = 'en-AU_Multimedia' EN_AU_NARROWBANDMODEL = 'en-AU_NarrowbandModel' EN_AU_TELEPHONY = 'en-AU_Telephony' EN_IN_TELEPHONY = 'en-IN_Telephony' EN_GB_BROADBANDMODEL = 'en-GB_BroadbandModel' EN_GB_MULTIMEDIA = 'en-GB_Multimedia' EN_GB_NARROWBANDMODEL = 'en-GB_NarrowbandModel' EN_GB_TELEPHONY = 'en-GB_Telephony' EN_US_BROADBANDMODEL = 'en-US_BroadbandModel' EN_US_MULTIMEDIA = 'en-US_Multimedia' EN_US_NARROWBANDMODEL = 'en-US_NarrowbandModel' EN_US_SHORTFORM_NARROWBANDMODEL = 'en-US_ShortForm_NarrowbandModel' EN_US_TELEPHONY = 'en-US_Telephony' EN_WW_MEDICAL_TELEPHONY = 'en-WW_Medical_Telephony' ES_AR_BROADBANDMODEL = 'es-AR_BroadbandModel' ES_AR_NARROWBANDMODEL = 'es-AR_NarrowbandModel' ES_CL_BROADBANDMODEL = 'es-CL_BroadbandModel' ES_CL_NARROWBANDMODEL = 'es-CL_NarrowbandModel' ES_CO_BROADBANDMODEL = 'es-CO_BroadbandModel' ES_CO_NARROWBANDMODEL = 'es-CO_NarrowbandModel' ES_ES_BROADBANDMODEL = 'es-ES_BroadbandModel' ES_ES_NARROWBANDMODEL = 'es-ES_NarrowbandModel' ES_ES_MULTIMEDIA = 'es-ES_Multimedia' ES_ES_TELEPHONY = 'es-ES_Telephony' ES_LA_TELEPHONY = 'es-LA_Telephony' ES_MX_BROADBANDMODEL = 'es-MX_BroadbandModel' ES_MX_NARROWBANDMODEL = 'es-MX_NarrowbandModel' ES_PE_BROADBANDMODEL = 'es-PE_BroadbandModel' ES_PE_NARROWBANDMODEL = 'es-PE_NarrowbandModel' FR_CA_BROADBANDMODEL = 'fr-CA_BroadbandModel' FR_CA_NARROWBANDMODEL = 'fr-CA_NarrowbandModel' FR_CA_TELEPHONY = 'fr-CA_Telephony' FR_FR_BROADBANDMODEL = 'fr-FR_BroadbandModel' FR_FR_MULTIMEDIA = 'fr-FR_Multimedia' FR_FR_NARROWBANDMODEL = 'fr-FR_NarrowbandModel' FR_FR_TELEPHONY = 'fr-FR_Telephony' HI_IN_TELEPHONY = 'hi-IN_Telephony' IT_IT_BROADBANDMODEL = 'it-IT_BroadbandModel' IT_IT_NARROWBANDMODEL = 'it-IT_NarrowbandModel' IT_IT_MULTIMEDIA = 'it-IT_Multimedia' IT_IT_TELEPHONY = 'it-IT_Telephony' JA_JP_BROADBANDMODEL = 'ja-JP_BroadbandModel' JA_JP_MULTIMEDIA = 'ja-JP_Multimedia' JA_JP_NARROWBANDMODEL = 'ja-JP_NarrowbandModel' KO_KR_BROADBANDMODEL = 'ko-KR_BroadbandModel' KO_KR_MULTIMEDIA = 'ko-KR_Multimedia' KO_KR_NARROWBANDMODEL = 'ko-KR_NarrowbandModel' KO_KR_TELEPHONY = 'ko-KR_Telephony' NL_BE_TELEPHONY = 'nl-BE_Telephony' NL_NL_BROADBANDMODEL = 'nl-NL_BroadbandModel' NL_NL_NARROWBANDMODEL = 'nl-NL_NarrowbandModel' NL_NL_TELEPHONY = 'nl-NL_Telephony' PT_BR_BROADBANDMODEL = 'pt-BR_BroadbandModel' PT_BR_MULTIMEDIA = 'pt-BR_Multimedia' PT_BR_NARROWBANDMODEL = 'pt-BR_NarrowbandModel' PT_BR_TELEPHONY = 'pt-BR_Telephony' ZH_CN_BROADBANDMODEL = 'zh-CN_BroadbandModel' ZH_CN_NARROWBANDMODEL = 'zh-CN_NarrowbandModel' ZH_CN_TELEPHONY = 'zh-CN_Telephony' class CreateJobEnums: """ Enums for create_job parameters. """ class ContentType(str, Enum): """ The format (MIME type) of the audio. For more information about specifying an audio format, see **Audio formats (content types)** in the method description. """ APPLICATION_OCTET_STREAM = 'application/octet-stream' AUDIO_ALAW = 'audio/alaw' AUDIO_BASIC = 'audio/basic' AUDIO_FLAC = 'audio/flac' AUDIO_G729 = 'audio/g729' AUDIO_L16 = 'audio/l16' AUDIO_MP3 = 'audio/mp3' AUDIO_MPEG = 'audio/mpeg' AUDIO_MULAW = 'audio/mulaw' AUDIO_OGG = 'audio/ogg' AUDIO_OGG_CODECS_OPUS = 'audio/ogg;codecs=opus' AUDIO_OGG_CODECS_VORBIS = 'audio/ogg;codecs=vorbis' AUDIO_WAV = 'audio/wav' AUDIO_WEBM = 'audio/webm' AUDIO_WEBM_CODECS_OPUS = 'audio/webm;codecs=opus' AUDIO_WEBM_CODECS_VORBIS = 'audio/webm;codecs=vorbis' class Model(str, Enum): """ The model to use for speech recognition. If you omit the `model` parameter, the service uses the US English `en-US_BroadbandModel` by default. (The model `ar-AR_BroadbandModel` is deprecated; use `ar-MS_BroadbandModel` instead.) _For IBM Cloud Pak for Data,_ if you do not install the `en-US_BroadbandModel`, you must either specify a model with the request or specify a new default model for your installation of the service. **See also:** * [Using a model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use) * [The default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-use#models-use-default). """ AR_AR_BROADBANDMODEL = 'ar-AR_BroadbandModel' AR_MS_BROADBANDMODEL = 'ar-MS_BroadbandModel' AR_MS_TELEPHONY = 'ar-MS_Telephony' CS_CZ_TELEPHONY = 'cs-CZ_Telephony' DE_DE_BROADBANDMODEL = 'de-DE_BroadbandModel' DE_DE_MULTIMEDIA = 'de-DE_Multimedia' DE_DE_NARROWBANDMODEL = 'de-DE_NarrowbandModel' DE_DE_TELEPHONY = 'de-DE_Telephony' EN_AU_BROADBANDMODEL = 'en-AU_BroadbandModel' EN_AU_MULTIMEDIA = 'en-AU_Multimedia' EN_AU_NARROWBANDMODEL = 'en-AU_NarrowbandModel' EN_AU_TELEPHONY = 'en-AU_Telephony' EN_IN_TELEPHONY = 'en-IN_Telephony' EN_GB_BROADBANDMODEL = 'en-GB_BroadbandModel' EN_GB_MULTIMEDIA = 'en-GB_Multimedia' EN_GB_NARROWBANDMODEL = 'en-GB_NarrowbandModel' EN_GB_TELEPHONY = 'en-GB_Telephony' EN_US_BROADBANDMODEL = 'en-US_BroadbandModel' EN_US_MULTIMEDIA = 'en-US_Multimedia' EN_US_NARROWBANDMODEL = 'en-US_NarrowbandModel' EN_US_SHORTFORM_NARROWBANDMODEL = 'en-US_ShortForm_NarrowbandModel' EN_US_TELEPHONY = 'en-US_Telephony' EN_WW_MEDICAL_TELEPHONY = 'en-WW_Medical_Telephony' ES_AR_BROADBANDMODEL = 'es-AR_BroadbandModel' ES_AR_NARROWBANDMODEL = 'es-AR_NarrowbandModel' ES_CL_BROADBANDMODEL = 'es-CL_BroadbandModel' ES_CL_NARROWBANDMODEL = 'es-CL_NarrowbandModel' ES_CO_BROADBANDMODEL = 'es-CO_BroadbandModel' ES_CO_NARROWBANDMODEL = 'es-CO_NarrowbandModel' ES_ES_BROADBANDMODEL = 'es-ES_BroadbandModel' ES_ES_NARROWBANDMODEL = 'es-ES_NarrowbandModel' ES_ES_MULTIMEDIA = 'es-ES_Multimedia' ES_ES_TELEPHONY = 'es-ES_Telephony' ES_LA_TELEPHONY = 'es-LA_Telephony' ES_MX_BROADBANDMODEL = 'es-MX_BroadbandModel' ES_MX_NARROWBANDMODEL = 'es-MX_NarrowbandModel' ES_PE_BROADBANDMODEL = 'es-PE_BroadbandModel' ES_PE_NARROWBANDMODEL = 'es-PE_NarrowbandModel' FR_CA_BROADBANDMODEL = 'fr-CA_BroadbandModel' FR_CA_NARROWBANDMODEL = 'fr-CA_NarrowbandModel' FR_CA_TELEPHONY = 'fr-CA_Telephony' FR_FR_BROADBANDMODEL = 'fr-FR_BroadbandModel' FR_FR_MULTIMEDIA = 'fr-FR_Multimedia' FR_FR_NARROWBANDMODEL = 'fr-FR_NarrowbandModel' FR_FR_TELEPHONY = 'fr-FR_Telephony' HI_IN_TELEPHONY = 'hi-IN_Telephony' IT_IT_BROADBANDMODEL = 'it-IT_BroadbandModel' IT_IT_NARROWBANDMODEL = 'it-IT_NarrowbandModel' IT_IT_MULTIMEDIA = 'it-IT_Multimedia' IT_IT_TELEPHONY = 'it-IT_Telephony' JA_JP_BROADBANDMODEL = 'ja-JP_BroadbandModel' JA_JP_MULTIMEDIA = 'ja-JP_Multimedia' JA_JP_NARROWBANDMODEL = 'ja-JP_NarrowbandModel' KO_KR_BROADBANDMODEL = 'ko-KR_BroadbandModel' KO_KR_MULTIMEDIA = 'ko-KR_Multimedia' KO_KR_NARROWBANDMODEL = 'ko-KR_NarrowbandModel' KO_KR_TELEPHONY = 'ko-KR_Telephony' NL_BE_TELEPHONY = 'nl-BE_Telephony' NL_NL_BROADBANDMODEL = 'nl-NL_BroadbandModel' NL_NL_NARROWBANDMODEL = 'nl-NL_NarrowbandModel' NL_NL_TELEPHONY = 'nl-NL_Telephony' PT_BR_BROADBANDMODEL = 'pt-BR_BroadbandModel' PT_BR_MULTIMEDIA = 'pt-BR_Multimedia' PT_BR_NARROWBANDMODEL = 'pt-BR_NarrowbandModel' PT_BR_TELEPHONY = 'pt-BR_Telephony' ZH_CN_BROADBANDMODEL = 'zh-CN_BroadbandModel' ZH_CN_NARROWBANDMODEL = 'zh-CN_NarrowbandModel' ZH_CN_TELEPHONY = 'zh-CN_Telephony' class Events(str, Enum): """ 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](#checkjob) method to retrieve the results before they time out or are deleted. * `recognitions.completed_with_results` generates a callback notification when the job is complete. The notification includes the results of the request. * `recognitions.failed` generates a callback notification if the service experiences an error while processing the job. The `recognitions.completed` and `recognitions.completed_with_results` events are incompatible. You can specify only of the two events. If the job includes a callback URL, omit the parameter to subscribe to the default events: `recognitions.started`, `recognitions.completed`, and `recognitions.failed`. If the job does not include a callback URL, omit the parameter. """ RECOGNITIONS_STARTED = 'recognitions.started' RECOGNITIONS_COMPLETED = 'recognitions.completed' RECOGNITIONS_COMPLETED_WITH_RESULTS = 'recognitions.completed_with_results' RECOGNITIONS_FAILED = 'recognitions.failed' class ListLanguageModelsEnums: """ Enums for list_language_models parameters. """ class Language(str, Enum): """ The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify `en-US` to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials. (**Note:** The identifier `ar-AR` is deprecated; use `ar-MS` instead.) To determine the languages for which customization is available, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). """ AR_AR = 'ar-AR' AR_MS = 'ar-MS' CS_CZ = 'cs-CZ' DE_DE = 'de-DE' EN_AU = 'en-AU' EN_GB = 'en-GB' EN_IN = 'en-IN' EN_US = 'en-US' EN_WW = 'en-WW' ES_AR = 'es-AR' ES_CL = 'es-CL' ES_CO = 'es-CO' ES_ES = 'es-ES' ES_LA = 'es-LA' ES_MX = 'es-MX' ES_PE = 'es-PE' FR_CA = 'fr-CA' FR_FR = 'fr-FR' HI_IN = 'hi-IN' IT_IT = 'it-IT' JA_JP = 'ja-JP' KO_KR = 'ko-KR' NL_BE = 'nl-BE' NL_NL = 'nl-NL' PT_BR = 'pt-BR' ZH_CN = 'zh-CN' class TrainLanguageModelEnums: """ Enums for train_language_model parameters. """ class WordTypeToAdd(str, Enum): """ _For custom models that are based on previous-generation models_, the type of words from the custom language model's words resource on which to train the model: * `all` (the default) trains the model on all new words, regardless of whether they were extracted from corpora or grammars or were added or modified by the user. * `user` trains the model only on custom words that were added or modified by the user directly. The model is not trained on new words extracted from corpora or grammars. _For custom models that are based on next-generation models_, the service ignores the parameter. The words resource contains only custom words that the user adds or modifies directly, so the parameter is unnecessary. """ ALL = 'all' USER = 'user' class ListWordsEnums: """ Enums for list_words parameters. """ class WordType(str, Enum): """ The type of words to be listed from the custom language model's words resource: * `all` (the default) shows all words. * `user` shows only custom words that were added or modified by the user directly. * `corpora` shows only OOV that were extracted from corpora. * `grammars` shows only OOV words that are recognized by grammars. _For a custom model that is based on a next-generation model_, only `all` and `user` apply. Both options return the same results. Words from other sources are not added to custom models that are based on next-generation models. """ ALL = 'all' USER = 'user' CORPORA = 'corpora' GRAMMARS = 'grammars' class Sort(str, Enum): """ 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`. """ ALPHABETICAL = 'alphabetical' COUNT = 'count' class AddGrammarEnums: """ Enums for add_grammar parameters. """ class ContentType(str, Enum): """ The format (MIME type) of the grammar file: * `application/srgs` for Augmented Backus-Naur Form (ABNF), which uses a plain-text representation that is similar to traditional BNF grammars. * `application/srgs+xml` for XML Form, which uses XML elements to represent the grammar. """ APPLICATION_SRGS = 'application/srgs' APPLICATION_SRGS_XML = 'application/srgs+xml' class ListAcousticModelsEnums: """ Enums for list_acoustic_models parameters. """ class Language(str, Enum): """ The identifier of the language for which custom language or custom acoustic models are to be returned. Specify the five-character language identifier; for example, specify `en-US` to see all custom language or custom acoustic models that are based on US English models. Omit the parameter to see all custom language or custom acoustic models that are owned by the requesting credentials. (**Note:** The identifier `ar-AR` is deprecated; use `ar-MS` instead.) To determine the languages for which customization is available, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support). """ AR_AR = 'ar-AR' AR_MS = 'ar-MS' CS_CZ = 'cs-CZ' DE_DE = 'de-DE' EN_AU = 'en-AU' EN_GB = 'en-GB' EN_IN = 'en-IN' EN_US = 'en-US' EN_WW = 'en-WW' ES_AR = 'es-AR' ES_CL = 'es-CL' ES_CO = 'es-CO' ES_ES = 'es-ES' ES_LA = 'es-LA' ES_MX = 'es-MX' ES_PE = 'es-PE' FR_CA = 'fr-CA' FR_FR = 'fr-FR' HI_IN = 'hi-IN' IT_IT = 'it-IT' JA_JP = 'ja-JP' KO_KR = 'ko-KR' NL_BE = 'nl-BE' NL_NL = 'nl-NL' PT_BR = 'pt-BR' ZH_CN = 'zh-CN' class AddAudioEnums: """ Enums for add_audio parameters. """ class ContentType(str, Enum): """ 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. """ APPLICATION_ZIP = 'application/zip' APPLICATION_GZIP = 'application/gzip' AUDIO_ALAW = 'audio/alaw' AUDIO_BASIC = 'audio/basic' AUDIO_FLAC = 'audio/flac' AUDIO_G729 = 'audio/g729' AUDIO_L16 = 'audio/l16' AUDIO_MP3 = 'audio/mp3' AUDIO_MPEG = 'audio/mpeg' AUDIO_MULAW = 'audio/mulaw' AUDIO_OGG = 'audio/ogg' AUDIO_OGG_CODECS_OPUS = 'audio/ogg;codecs=opus' AUDIO_OGG_CODECS_VORBIS = 'audio/ogg;codecs=vorbis' AUDIO_WAV = 'audio/wav' AUDIO_WEBM = 'audio/webm' AUDIO_WEBM_CODECS_OPUS = 'audio/webm;codecs=opus' AUDIO_WEBM_CODECS_VORBIS = 'audio/webm;codecs=vorbis' class ContainedContentType(str, Enum): """ _For an archive-type resource_, specify the format of the audio files that are contained in the archive file if they are of type `audio/alaw`, `audio/basic`, `audio/l16`, or `audio/mulaw`. Include the `rate`, `channels`, and `endianness` parameters where necessary. In this case, all audio files that are contained in the archive file must be of the indicated type. For all other audio formats, you can omit the header. In this case, the audio files can be of multiple types as long as they are not of the types listed in the previous paragraph. The parameter accepts all of the audio formats that are supported for use with speech recognition. For more information, see **Content types for audio-type resources** in the method description. _For an audio-type resource_, omit the header. """ AUDIO_ALAW = 'audio/alaw' AUDIO_BASIC = 'audio/basic' AUDIO_FLAC = 'audio/flac' AUDIO_G729 = 'audio/g729' AUDIO_L16 = 'audio/l16' AUDIO_MP3 = 'audio/mp3' AUDIO_MPEG = 'audio/mpeg' AUDIO_MULAW = 'audio/mulaw' AUDIO_OGG = 'audio/ogg' AUDIO_OGG_CODECS_OPUS = 'audio/ogg;codecs=opus' AUDIO_OGG_CODECS_VORBIS = 'audio/ogg;codecs=vorbis' AUDIO_WAV = 'audio/wav' AUDIO_WEBM = 'audio/webm' AUDIO_WEBM_CODECS_OPUS = 'audio/webm;codecs=opus' AUDIO_WEBM_CODECS_VORBIS = 'audio/webm;codecs=vorbis' ############################################################################## # Models ############################################################################## class AcousticModel(): """ Information about an existing custom acoustic model. :attr str customization_id: The customization ID (GUID) of the custom acoustic model. The [Create a custom acoustic model](#createacousticmodel) method returns only this field of the object; it does not return the other fields. :attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). :attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was last modified. The `created` and `updated` fields are equal when an acoustic model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD). :attr str language: (optional) The language identifier of the custom acoustic model (for example, `en-US`). :attr List[str] versions: (optional) A list of the available versions of the custom acoustic model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded to a new version of its base model. Otherwise, only a single version is shown. :attr str owner: (optional) The GUID of the credentials for the instance of the service that owns the custom acoustic model. :attr str name: (optional) The name of the custom acoustic model. :attr str description: (optional) The description of the custom acoustic model. :attr str base_model_name: (optional) The name of the language model for which the custom acoustic model was created. :attr str status: (optional) The current status of the custom acoustic model: * `pending`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. :attr int progress: (optional) A percentage that indicates the progress of the custom acoustic model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete. :attr str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter. """ def __init__(self, customization_id: str, *, created: str = None, updated: str = None, language: str = None, versions: List[str] = None, owner: str = None, name: str = None, description: str = None, base_model_name: str = None, status: str = None, progress: int = None, warnings: str = None) -> None: """ Initialize a AcousticModel object. :param str customization_id: The customization ID (GUID) of the custom acoustic model. The [Create a custom acoustic model](#createacousticmodel) 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 updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was last modified. The `created` and `updated` fields are equal when an acoustic model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD). :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 to a new version of its base model. Otherwise, only a single version is shown. :param str owner: (optional) The GUID of the 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`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. :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.updated = updated 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: Dict) -> 'AcousticModel': """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 'updated' in _dict: args['updated'] = _dict.get('updated') 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) @classmethod def _from_dict(cls, _dict): """Initialize a AcousticModel object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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, 'updated') and self.updated is not None: _dict['updated'] = self.updated 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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AcousticModel object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AcousticModel') -> bool: """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: 'AcousticModel') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The current status of the custom acoustic model: * `pending`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. """ PENDING = 'pending' READY = 'ready' TRAINING = 'training' AVAILABLE = 'available' UPGRADING = 'upgrading' FAILED = 'failed' class AcousticModels(): """ Information about existing custom acoustic models. :attr List[AcousticModel] customizations: An array of `AcousticModel` objects that provides information about each available custom acoustic model. The array is empty if the requesting credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language. """ def __init__(self, customizations: List['AcousticModel']) -> None: """ Initialize a AcousticModels object. :param List[AcousticModel] customizations: An array of `AcousticModel` objects that provides information about each available custom acoustic model. The array is empty if the requesting credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language. """ self.customizations = customizations @classmethod def from_dict(cls, _dict: Dict) -> 'AcousticModels': """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) @classmethod def _from_dict(cls, _dict): """Initialize a AcousticModels object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AcousticModels object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AcousticModels') -> bool: """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: 'AcousticModels') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioDetails(): """ Information about an audio resource from a custom acoustic model. :attr str type: (optional) The type of the audio resource: * `audio` for an individual audio file * `archive` for an archive (**.zip** or **.tar.gz**) file that contains audio files * `undetermined` for a resource that the service cannot validate (for example, if the user mistakenly passes a file that does not contain audio, such as a JPEG file). :attr str codec: (optional) _For an audio-type resource_, the codec in which the audio is encoded. Omitted for an archive-type resource. :attr int frequency: (optional) _For an audio-type resource_, the sampling rate of the audio in Hertz (samples per second). Omitted for an archive-type resource. :attr str compression: (optional) _For an archive-type resource_, the format of the compressed archive: * `zip` for a **.zip** file * `gzip` for a **.tar.gz** file Omitted for an audio-type resource. """ def __init__(self, *, type: str = None, codec: str = None, frequency: int = None, compression: str = None) -> 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: Dict) -> 'AudioDetails': """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) @classmethod def _from_dict(cls, _dict): """Initialize a AudioDetails object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioDetails object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioDetails') -> bool: """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: 'AudioDetails') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TypeEnum(str, Enum): """ 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). """ AUDIO = 'audio' ARCHIVE = 'archive' UNDETERMINED = 'undetermined' class CompressionEnum(str, Enum): """ _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. """ ZIP = 'zip' GZIP = 'gzip' class AudioListing(): """ Information about an audio resource from a custom acoustic model. :attr int duration: (optional) _For an audio-type resource_, the total seconds of audio in the resource. Omitted for an archive-type resource. :attr str name: (optional) _For an audio-type resource_, the user-specified name of the resource. Omitted for an archive-type resource. :attr AudioDetails details: (optional) _For an audio-type resource_, an `AudioDetails` object that provides detailed information about the resource. The object is empty until the service finishes processing the audio. Omitted for an archive-type resource. :attr str status: (optional) _For an audio-type resource_, the status of the resource: * `ok`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource. :attr AudioResource container: (optional) _For an archive-type resource_, an object of type `AudioResource` that provides information about the resource. Omitted for an audio-type resource. :attr List[AudioResource] audio: (optional) _For an archive-type resource_, an array of `AudioResource` objects that provides information about the audio-type resources that are contained in the resource. Omitted for an audio-type resource. """ def __init__(self, *, duration: int = None, name: str = None, details: 'AudioDetails' = None, status: str = None, container: 'AudioResource' = None, audio: List['AudioResource'] = None) -> None: """ Initialize a AudioListing object. :param int duration: (optional) _For an audio-type resource_, the total seconds of audio in the resource. 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`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource. :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: Dict) -> 'AudioListing': """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) @classmethod def _from_dict(cls, _dict): """Initialize a AudioListing object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioListing object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioListing') -> bool: """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: 'AudioListing') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ _For an audio-type resource_, the status of the resource: * `ok`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource. """ OK = 'ok' BEING_PROCESSED = 'being_processed' INVALID = 'invalid' class AudioMetrics(): """ If audio metrics are requested, information about the signal characteristics of the input audio. :attr float sampling_interval: The interval in seconds (typically 0.1 seconds) at which the service calculated the audio metrics. In other words, how often the service calculated the metrics. A single unit in each histogram (see the `AudioMetricsHistogramBin` object) is calculated based on a `sampling_interval` length of audio. :attr AudioMetricsDetails accumulated: Detailed information about the signal characteristics of the input audio. """ def __init__(self, sampling_interval: float, accumulated: 'AudioMetricsDetails') -> None: """ Initialize a AudioMetrics object. :param float sampling_interval: The interval in seconds (typically 0.1 seconds) at which the service calculated the audio metrics. In other words, how often the service calculated the metrics. A single unit in each histogram (see the `AudioMetricsHistogramBin` object) is calculated based on a `sampling_interval` length of audio. :param AudioMetricsDetails accumulated: Detailed information about the signal characteristics of the input audio. """ self.sampling_interval = sampling_interval self.accumulated = accumulated @classmethod def from_dict(cls, _dict: Dict) -> 'AudioMetrics': """Initialize a AudioMetrics object from a json dictionary.""" args = {} if 'sampling_interval' in _dict: args['sampling_interval'] = _dict.get('sampling_interval') else: raise ValueError( 'Required property \'sampling_interval\' not present in AudioMetrics JSON' ) if 'accumulated' in _dict: args['accumulated'] = AudioMetricsDetails.from_dict( _dict.get('accumulated')) else: raise ValueError( 'Required property \'accumulated\' not present in AudioMetrics JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a AudioMetrics object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'sampling_interval') and self.sampling_interval is not None: _dict['sampling_interval'] = self.sampling_interval if hasattr(self, 'accumulated') and self.accumulated is not None: _dict['accumulated'] = self.accumulated.to_dict() return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioMetrics object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioMetrics') -> bool: """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: 'AudioMetrics') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioMetricsDetails(): """ Detailed information about the signal characteristics of the input audio. :attr bool final: If `true`, indicates the end of the audio stream, meaning that transcription is complete. Currently, the field is always `true`. The service returns metrics just once per audio stream. The results provide aggregated audio metrics that pertain to the complete audio stream. :attr float end_time: The end time in seconds of the block of audio to which the metrics apply. :attr float signal_to_noise_ratio: (optional) The signal-to-noise ratio (SNR) for the audio signal. The value indicates the ratio of speech to noise in the audio. A valid value lies in the range of 0 to 100 decibels (dB). The service omits the field if it cannot compute the SNR for the audio. :attr float speech_ratio: The ratio of speech to non-speech segments in the audio signal. The value lies in the range of 0.0 to 1.0. :attr float high_frequency_loss: The probability that the audio signal is missing the upper half of its frequency content. * A value close to 1.0 typically indicates artificially up-sampled audio, which negatively impacts the accuracy of the transcription results. * A value at or near 0.0 indicates that the audio signal is good and has a full spectrum. * A value around 0.5 means that detection of the frequency content is unreliable or not available. :attr List[AudioMetricsHistogramBin] direct_current_offset: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the cumulative direct current (DC) component of the audio signal. :attr List[AudioMetricsHistogramBin] clipping_rate: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the clipping rate for the audio segments. The clipping rate is defined as the fraction of samples in the segment that reach the maximum or minimum value that is offered by the audio quantization range. The service auto-detects either a 16-bit Pulse-Code Modulation(PCM) audio range (-32768 to +32767) or a unit range (-1.0 to +1.0). The clipping rate is between 0.0 and 1.0, with higher values indicating possible degradation of speech recognition. :attr List[AudioMetricsHistogramBin] speech_level: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the signal level in segments of the audio that contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level). :attr List[AudioMetricsHistogramBin] non_speech_level: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the signal level in segments of the audio that do not contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level). """ def __init__(self, final: bool, end_time: float, speech_ratio: float, high_frequency_loss: float, direct_current_offset: List['AudioMetricsHistogramBin'], clipping_rate: List['AudioMetricsHistogramBin'], speech_level: List['AudioMetricsHistogramBin'], non_speech_level: List['AudioMetricsHistogramBin'], *, signal_to_noise_ratio: float = None) -> None: """ Initialize a AudioMetricsDetails object. :param bool final: If `true`, indicates the end of the audio stream, meaning that transcription is complete. Currently, the field is always `true`. The service returns metrics just once per audio stream. The results provide aggregated audio metrics that pertain to the complete audio stream. :param float end_time: The end time in seconds of the block of audio to which the metrics apply. :param float speech_ratio: The ratio of speech to non-speech segments in the audio signal. The value lies in the range of 0.0 to 1.0. :param float high_frequency_loss: The probability that the audio signal is missing the upper half of its frequency content. * A value close to 1.0 typically indicates artificially up-sampled audio, which negatively impacts the accuracy of the transcription results. * A value at or near 0.0 indicates that the audio signal is good and has a full spectrum. * A value around 0.5 means that detection of the frequency content is unreliable or not available. :param List[AudioMetricsHistogramBin] direct_current_offset: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the cumulative direct current (DC) component of the audio signal. :param List[AudioMetricsHistogramBin] clipping_rate: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the clipping rate for the audio segments. The clipping rate is defined as the fraction of samples in the segment that reach the maximum or minimum value that is offered by the audio quantization range. The service auto-detects either a 16-bit Pulse-Code Modulation(PCM) audio range (-32768 to +32767) or a unit range (-1.0 to +1.0). The clipping rate is between 0.0 and 1.0, with higher values indicating possible degradation of speech recognition. :param List[AudioMetricsHistogramBin] speech_level: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the signal level in segments of the audio that contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level). :param List[AudioMetricsHistogramBin] non_speech_level: An array of `AudioMetricsHistogramBin` objects that defines a histogram of the signal level in segments of the audio that do not contain speech. The signal level is computed as the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0 (minimum level) to 1.0 (maximum level). :param float signal_to_noise_ratio: (optional) The signal-to-noise ratio (SNR) for the audio signal. The value indicates the ratio of speech to noise in the audio. A valid value lies in the range of 0 to 100 decibels (dB). The service omits the field if it cannot compute the SNR for the audio. """ self.final = final self.end_time = end_time self.signal_to_noise_ratio = signal_to_noise_ratio self.speech_ratio = speech_ratio self.high_frequency_loss = high_frequency_loss self.direct_current_offset = direct_current_offset self.clipping_rate = clipping_rate self.speech_level = speech_level self.non_speech_level = non_speech_level @classmethod def from_dict(cls, _dict: Dict) -> 'AudioMetricsDetails': """Initialize a AudioMetricsDetails object from a json dictionary.""" args = {} if 'final' in _dict: args['final'] = _dict.get('final') else: raise ValueError( 'Required property \'final\' not present in AudioMetricsDetails JSON' ) if 'end_time' in _dict: args['end_time'] = _dict.get('end_time') else: raise ValueError( 'Required property \'end_time\' not present in AudioMetricsDetails JSON' ) if 'signal_to_noise_ratio' in _dict: args['signal_to_noise_ratio'] = _dict.get('signal_to_noise_ratio') if 'speech_ratio' in _dict: args['speech_ratio'] = _dict.get('speech_ratio') else: raise ValueError( 'Required property \'speech_ratio\' not present in AudioMetricsDetails JSON' ) if 'high_frequency_loss' in _dict: args['high_frequency_loss'] = _dict.get('high_frequency_loss') else: raise ValueError( 'Required property \'high_frequency_loss\' not present in AudioMetricsDetails JSON' ) if 'direct_current_offset' in _dict: args['direct_current_offset'] = [ AudioMetricsHistogramBin.from_dict(x) for x in _dict.get('direct_current_offset') ] else: raise ValueError( 'Required property \'direct_current_offset\' not present in AudioMetricsDetails JSON' ) if 'clipping_rate' in _dict: args['clipping_rate'] = [ AudioMetricsHistogramBin.from_dict(x) for x in _dict.get('clipping_rate') ] else: raise ValueError( 'Required property \'clipping_rate\' not present in AudioMetricsDetails JSON' ) if 'speech_level' in _dict: args['speech_level'] = [ AudioMetricsHistogramBin.from_dict(x) for x in _dict.get('speech_level') ] else: raise ValueError( 'Required property \'speech_level\' not present in AudioMetricsDetails JSON' ) if 'non_speech_level' in _dict: args['non_speech_level'] = [ AudioMetricsHistogramBin.from_dict(x) for x in _dict.get('non_speech_level') ] else: raise ValueError( 'Required property \'non_speech_level\' not present in AudioMetricsDetails JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a AudioMetricsDetails object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'final') and self.final is not None: _dict['final'] = self.final if hasattr(self, 'end_time') and self.end_time is not None: _dict['end_time'] = self.end_time if hasattr(self, 'signal_to_noise_ratio' ) and self.signal_to_noise_ratio is not None: _dict['signal_to_noise_ratio'] = self.signal_to_noise_ratio if hasattr(self, 'speech_ratio') and self.speech_ratio is not None: _dict['speech_ratio'] = self.speech_ratio if hasattr( self, 'high_frequency_loss') and self.high_frequency_loss is not None: _dict['high_frequency_loss'] = self.high_frequency_loss if hasattr(self, 'direct_current_offset' ) and self.direct_current_offset is not None: _dict['direct_current_offset'] = [ x.to_dict() for x in self.direct_current_offset ] if hasattr(self, 'clipping_rate') and self.clipping_rate is not None: _dict['clipping_rate'] = [x.to_dict() for x in self.clipping_rate] if hasattr(self, 'speech_level') and self.speech_level is not None: _dict['speech_level'] = [x.to_dict() for x in self.speech_level] if hasattr(self, 'non_speech_level') and self.non_speech_level is not None: _dict['non_speech_level'] = [ x.to_dict() for x in self.non_speech_level ] return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioMetricsDetails object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioMetricsDetails') -> bool: """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: 'AudioMetricsDetails') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioMetricsHistogramBin(): """ A bin with defined boundaries that indicates the number of values in a range of signal characteristics for a histogram. The first and last bins of a histogram are the boundary bins. They cover the intervals between negative infinity and the first boundary, and between the last boundary and positive infinity, respectively. :attr float begin: The lower boundary of the bin in the histogram. :attr float end: The upper boundary of the bin in the histogram. :attr int count: The number of values in the bin of the histogram. """ def __init__(self, begin: float, end: float, count: int) -> None: """ Initialize a AudioMetricsHistogramBin object. :param float begin: The lower boundary of the bin in the histogram. :param float end: The upper boundary of the bin in the histogram. :param int count: The number of values in the bin of the histogram. """ self.begin = begin self.end = end self.count = count @classmethod def from_dict(cls, _dict: Dict) -> 'AudioMetricsHistogramBin': """Initialize a AudioMetricsHistogramBin object from a json dictionary.""" args = {} if 'begin' in _dict: args['begin'] = _dict.get('begin') else: raise ValueError( 'Required property \'begin\' not present in AudioMetricsHistogramBin JSON' ) if 'end' in _dict: args['end'] = _dict.get('end') else: raise ValueError( 'Required property \'end\' not present in AudioMetricsHistogramBin JSON' ) if 'count' in _dict: args['count'] = _dict.get('count') else: raise ValueError( 'Required property \'count\' not present in AudioMetricsHistogramBin JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a AudioMetricsHistogramBin object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'begin') and self.begin is not None: _dict['begin'] = self.begin if hasattr(self, 'end') and self.end is not None: _dict['end'] = self.end if hasattr(self, 'count') and self.count is not None: _dict['count'] = self.count return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioMetricsHistogramBin object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioMetricsHistogramBin') -> bool: """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: 'AudioMetricsHistogramBin') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class AudioResource(): """ Information about an audio resource from a custom acoustic model. :attr int duration: The total seconds of audio in the audio resource. :attr str name: _For an archive-type resource_, the user-specified name of the resource. _For an audio-type resource_, the user-specified name of the resource or the name of the audio file that the user added for the resource. The value depends on the method that is called. :attr AudioDetails details: An `AudioDetails` object that provides detailed information about the audio resource. The object is empty until the service finishes processing the audio. :attr str status: The status of the audio resource: * `ok`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid. """ def __init__(self, duration: int, name: str, details: 'AudioDetails', status: str) -> None: """ Initialize a AudioResource object. :param int duration: The total seconds of audio in the audio resource. :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`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid. """ self.duration = duration self.name = name self.details = details self.status = status @classmethod def from_dict(cls, _dict: Dict) -> 'AudioResource': """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) @classmethod def _from_dict(cls, _dict): """Initialize a AudioResource object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioResource object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioResource') -> bool: """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: 'AudioResource') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The status of the audio resource: * `ok`: The service successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed`: The service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid`: The audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid. """ OK = 'ok' BEING_PROCESSED = 'being_processed' INVALID = 'invalid' class AudioResources(): """ Information about the audio resources from a custom acoustic model. :attr float total_minutes_of_audio: The total minutes of accumulated audio summed over all of the valid audio resources for the custom acoustic model. You can use this value to determine whether the custom model has too little or too much audio to begin training. :attr List[AudioResource] audio: An array of `AudioResource` objects that provides information about the audio resources of the custom acoustic model. The array is empty if the custom model has no audio resources. """ def __init__(self, total_minutes_of_audio: float, audio: List['AudioResource']) -> None: """ 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 `AudioResource` objects that provides information about the audio resources of the custom acoustic model. The array is empty if the custom model has no audio resources. """ self.total_minutes_of_audio = total_minutes_of_audio self.audio = audio @classmethod def from_dict(cls, _dict: Dict) -> 'AudioResources': """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) @classmethod def _from_dict(cls, _dict): """Initialize a AudioResources object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this AudioResources object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'AudioResources') -> bool: """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: 'AudioResources') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Corpora(): """ Information about the corpora from a custom language model. :attr List[Corpus] corpora: An array of `Corpus` objects that provides information about the corpora for the custom model. The array is empty if the custom model has no corpora. """ def __init__(self, corpora: List['Corpus']) -> None: """ Initialize a Corpora object. :param List[Corpus] corpora: An array of `Corpus` objects that provides information about the corpora for the custom model. The array is empty if the custom model has no corpora. """ self.corpora = corpora @classmethod def from_dict(cls, _dict: Dict) -> 'Corpora': """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) @classmethod def _from_dict(cls, _dict): """Initialize a Corpora object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Corpora object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Corpora') -> bool: """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: 'Corpora') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Corpus(): """ Information about a corpus from a custom language model. :attr str name: The name of the corpus. :attr int total_words: The total number of words in the corpus. The value is `0` while the corpus is being processed. :attr int out_of_vocabulary_words: _For custom models that are based on previous-generation models_, the number of OOV words extracted from the corpus. The value is `0` while the corpus is being processed. _For custom models that are based on next-generation models_, no OOV words are extracted from corpora, so the value is always `0`. :attr str status: The status of the corpus: * `analyzed`: The service successfully analyzed the corpus. The custom model can be trained with data from the corpus. * `being_processed`: The service is still analyzing the corpus. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the corpus. The `error` field describes the failure. :attr str error: (optional) If the status of the corpus is `undetermined`, the following message: `Analysis of corpus 'name' failed. Please try adding the corpus again by setting the 'allow_overwrite' flag to 'true'`. """ def __init__(self, name: str, total_words: int, out_of_vocabulary_words: int, status: str, *, error: str = None) -> 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: _For custom models that are based on previous-generation models_, the number of OOV words extracted from the corpus. The value is `0` while the corpus is being processed. _For custom models that are based on next-generation models_, no OOV words are extracted from corpora, so the value is always `0`. :param str status: The status of the corpus: * `analyzed`: The service successfully analyzed the corpus. The custom model can be trained with data from the corpus. * `being_processed`: The service is still analyzing the corpus. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the corpus. The `error` field describes the failure. :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: Dict) -> 'Corpus': """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) @classmethod def _from_dict(cls, _dict): """Initialize a Corpus object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Corpus object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Corpus') -> bool: """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: 'Corpus') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The status of the corpus: * `analyzed`: The service successfully analyzed the corpus. The custom model can be trained with data from the corpus. * `being_processed`: The service is still analyzing the corpus. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the corpus. The `error` field describes the failure. """ ANALYZED = 'analyzed' BEING_PROCESSED = 'being_processed' UNDETERMINED = 'undetermined' class CustomWord(): """ Information about a word that is to be added to a custom language model. :attr str word: (optional) For the [Add custom words](#addwords) 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](#addword) method. :attr List[str] sounds_like: (optional) As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. * _For custom models that are based on previous-generation models_, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. * For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters not including 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. _For custom models that are based on next-generation models_, the service uses the spelling of the word as the display-as value if you omit the field. """ def __init__(self, *, word: str = None, sounds_like: List[str] = None, display_as: str = None) -> None: """ Initialize a CustomWord object. :param str word: (optional) For the [Add custom words](#addwords) 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](#addword) method. :param List[str] sounds_like: (optional) As array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. * _For custom models that are based on previous-generation models_, for a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. * For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations. A pronunciation can include at most 40 characters not including 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. _For custom models that are based on next-generation models_, the service uses the spelling of the word as the display-as value if you omit the field. """ self.word = word self.sounds_like = sounds_like self.display_as = display_as @classmethod def from_dict(cls, _dict: Dict) -> 'CustomWord': """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) @classmethod def _from_dict(cls, _dict): """Initialize a CustomWord object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this CustomWord object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'CustomWord') -> bool: """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: 'CustomWord') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Grammar(): """ Information about a grammar from a custom language model. :attr str name: The name of the grammar. :attr int out_of_vocabulary_words: _For custom models that are based on previous-generation models_, the number of OOV words extracted from the grammar. The value is `0` while the grammar is being processed. _For custom models that are based on next-generation models_, no OOV words are extracted from grammars, so the value is always `0`. :attr str status: The status of the grammar: * `analyzed`: The service successfully analyzed the grammar. The custom model can be trained with data from the grammar. * `being_processed`: The service is still analyzing the grammar. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the grammar. The `error` field describes the failure. :attr str error: (optional) If the status of the grammar is `undetermined`, the following message: `Analysis of grammar '{grammar_name}' failed. Please try fixing the error or adding the grammar again by setting the 'allow_overwrite' flag to 'true'.`. """ def __init__(self, name: str, out_of_vocabulary_words: int, status: str, *, error: str = None) -> None: """ Initialize a Grammar object. :param str name: The name of the grammar. :param int out_of_vocabulary_words: _For custom models that are based on previous-generation models_, the number of OOV words extracted from the grammar. The value is `0` while the grammar is being processed. _For custom models that are based on next-generation models_, no OOV words are extracted from grammars, so the value is always `0`. :param str status: The status of the grammar: * `analyzed`: The service successfully analyzed the grammar. The custom model can be trained with data from the grammar. * `being_processed`: The service is still analyzing the grammar. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the grammar. The `error` field describes the failure. :param str error: (optional) If the status of the grammar is `undetermined`, the following message: `Analysis of grammar '{grammar_name}' failed. Please try fixing the error or adding the grammar again by setting the 'allow_overwrite' flag to 'true'.`. """ self.name = name self.out_of_vocabulary_words = out_of_vocabulary_words self.status = status self.error = error @classmethod def from_dict(cls, _dict: Dict) -> 'Grammar': """Initialize a Grammar object from a json dictionary.""" args = {} if 'name' in _dict: args['name'] = _dict.get('name') else: raise ValueError( 'Required property \'name\' not present in Grammar 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 Grammar JSON' ) if 'status' in _dict: args['status'] = _dict.get('status') else: raise ValueError( 'Required property \'status\' not present in Grammar JSON') if 'error' in _dict: args['error'] = _dict.get('error') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a Grammar object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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, '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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Grammar object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Grammar') -> bool: """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: 'Grammar') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The status of the grammar: * `analyzed`: The service successfully analyzed the grammar. The custom model can be trained with data from the grammar. * `being_processed`: The service is still analyzing the grammar. The service cannot accept requests to add new resources or to train the custom model. * `undetermined`: The service encountered an error while processing the grammar. The `error` field describes the failure. """ ANALYZED = 'analyzed' BEING_PROCESSED = 'being_processed' UNDETERMINED = 'undetermined' class Grammars(): """ Information about the grammars from a custom language model. :attr List[Grammar] grammars: An array of `Grammar` objects that provides information about the grammars for the custom model. The array is empty if the custom model has no grammars. """ def __init__(self, grammars: List['Grammar']) -> None: """ Initialize a Grammars object. :param List[Grammar] grammars: An array of `Grammar` objects that provides information about the grammars for the custom model. The array is empty if the custom model has no grammars. """ self.grammars = grammars @classmethod def from_dict(cls, _dict: Dict) -> 'Grammars': """Initialize a Grammars object from a json dictionary.""" args = {} if 'grammars' in _dict: args['grammars'] = [ Grammar.from_dict(x) for x in _dict.get('grammars') ] else: raise ValueError( 'Required property \'grammars\' not present in Grammars JSON') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a Grammars object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'grammars') and self.grammars is not None: _dict['grammars'] = [x.to_dict() for x in self.grammars] return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Grammars object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Grammars') -> bool: """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: 'Grammars') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class KeywordResult(): """ Information about a match for a keyword from speech recognition results. :attr str normalized_text: A specified keyword normalized to the spoken phrase that matched in the audio input. :attr float start_time: The start time in seconds of the keyword match. :attr float end_time: The end time in seconds of the keyword match. :attr float confidence: A confidence score for the keyword match in the range of 0.0 to 1.0. """ def __init__(self, normalized_text: str, start_time: float, end_time: float, confidence: float) -> None: """ 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: Dict) -> 'KeywordResult': """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) @classmethod def _from_dict(cls, _dict): """Initialize a KeywordResult object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this KeywordResult object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'KeywordResult') -> bool: """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: 'KeywordResult') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class LanguageModel(): """ Information about an existing custom language model. :attr str customization_id: The customization ID (GUID) of the custom language model. The [Create a custom language model](#createlanguagemodel) method returns only this field of the object; it does not return the other fields. :attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). :attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was last modified. The `created` and `updated` fields are equal when a language model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD). :attr str language: (optional) The language identifier of the custom language model (for example, `en-US`). The value matches the five-character language identifier from the name of the base model for the custom model. This value might be different from the value of the `dialect` field. :attr str dialect: (optional) The dialect of the language for the custom language model. _For custom models that are based on non-Spanish previous-generation models and on next-generation models,_ the field matches the language of the base model; for example, `en-US` for one of the US English models. _For custom models that are based on Spanish previous-generation models,_ the field indicates the dialect with which the model was created. The value can match the name of the base model or, if it was specified by the user, can be one of the following: * `es-ES` for Castilian Spanish (`es-ES` models) * `es-LA` for Latin American Spanish (`es-AR`, `es-CL`, `es-CO`, and `es-PE` models) * `es-US` for Mexican (North American) Spanish (`es-MX` models) Dialect values are case-insensitive. :attr List[str] versions: (optional) A list of the available versions of the custom language model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded to a new version of its base model. Otherwise, only a single version is shown. :attr str owner: (optional) The GUID of the credentials for the instance of the service that owns the custom language model. :attr str name: (optional) The name of the custom language model. :attr str description: (optional) The description of the custom language model. :attr str base_model_name: (optional) The name of the language model for which the custom language model was created. :attr str status: (optional) The current status of the custom language model: * `pending`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. :attr int progress: (optional) A percentage that indicates the progress of the custom language model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete. :attr str error: (optional) If an error occurred while adding a grammar file to the custom language model, a message that describes an `Internal Server Error` and includes the string `Cannot compile grammar`. The status of the custom model is not affected by the error, but the grammar cannot be used with the model. :attr str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter. """ def __init__(self, customization_id: str, *, created: str = None, updated: str = None, language: str = None, dialect: str = None, versions: List[str] = None, owner: str = None, name: str = None, description: str = None, base_model_name: str = None, status: str = None, progress: int = None, error: str = None, warnings: str = None) -> None: """ Initialize a LanguageModel object. :param str customization_id: The customization ID (GUID) of the custom language model. The [Create a custom language model](#createlanguagemodel) 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 updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was last modified. The `created` and `updated` fields are equal when a language model is first added but has yet to be updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD). :param str language: (optional) The language identifier of the custom language model (for example, `en-US`). The value matches the five-character language identifier from the name of the base model for the custom model. This value might be different from the value of the `dialect` field. :param str dialect: (optional) The dialect of the language for the custom language model. _For custom models that are based on non-Spanish previous-generation models and on next-generation models,_ the field matches the language of the base model; for example, `en-US` for one of the US English models. _For custom models that are based on Spanish previous-generation models,_ the field indicates the dialect with which the model was created. The value can match the name of the base model or, if it was specified by the user, can be one of the following: * `es-ES` for Castilian Spanish (`es-ES` models) * `es-LA` for Latin American Spanish (`es-AR`, `es-CL`, `es-CO`, and `es-PE` models) * `es-US` for Mexican (North American) Spanish (`es-MX` models) Dialect values are case-insensitive. :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 to a new version of its base model. Otherwise, only a single version is shown. :param str owner: (optional) The GUID of the 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`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. :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 error: (optional) If an error occurred while adding a grammar file to the custom language model, a message that describes an `Internal Server Error` and includes the string `Cannot compile grammar`. The status of the custom model is not affected by the error, but the grammar cannot be used with the model. :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.updated = updated 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.error = error self.warnings = warnings @classmethod def from_dict(cls, _dict: Dict) -> 'LanguageModel': """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 'updated' in _dict: args['updated'] = _dict.get('updated') 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 'error' in _dict: args['error'] = _dict.get('error') if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a LanguageModel object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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, 'updated') and self.updated is not None: _dict['updated'] = self.updated 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, 'error') and self.error is not None: _dict['error'] = self.error if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this LanguageModel object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'LanguageModel') -> bool: """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: 'LanguageModel') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The current status of the custom language model: * `pending`: The model was created but is waiting either for valid training data to be added or for the service to finish analyzing added data. * `ready`: The model contains valid data and is ready to be trained. If the model contains a mix of valid and invalid resources, you need to set the `strict` parameter to `false` for the training to proceed. * `training`: The model is currently being trained. * `available`: The model is trained and ready to use. * `upgrading`: The model is currently being upgraded. * `failed`: Training of the model failed. """ PENDING = 'pending' READY = 'ready' TRAINING = 'training' AVAILABLE = 'available' UPGRADING = 'upgrading' FAILED = 'failed' class LanguageModels(): """ Information about existing custom language models. :attr List[LanguageModel] customizations: An array of `LanguageModel` objects that provides information about each available custom language model. The array is empty if the requesting credentials own no custom language models (if no language is specified) or own no custom language models for the specified language. """ def __init__(self, customizations: List['LanguageModel']) -> None: """ Initialize a LanguageModels object. :param List[LanguageModel] customizations: An array of `LanguageModel` objects that provides information about each available custom language model. The array is empty if the requesting credentials own no custom language models (if no language is specified) or own no custom language models for the specified language. """ self.customizations = customizations @classmethod def from_dict(cls, _dict: Dict) -> 'LanguageModels': """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) @classmethod def _from_dict(cls, _dict): """Initialize a LanguageModels object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this LanguageModels object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'LanguageModels') -> bool: """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: 'LanguageModels') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class ProcessedAudio(): """ Detailed timing information about the service's processing of the input audio. :attr float received: The seconds of audio that the service has received as of this response. The value of the field is greater than the values of the `transcription` and `speaker_labels` fields during speech recognition processing, since the service first has to receive the audio before it can begin to process it. The final value can also be greater than the value of the `transcription` and `speaker_labels` fields by a fractional number of seconds. :attr float seen_by_engine: The seconds of audio that the service has passed to its speech-processing engine as of this response. The value of the field is greater than the values of the `transcription` and `speaker_labels` fields during speech recognition processing. The `received` and `seen_by_engine` fields have identical values when the service has finished processing all audio. This final value can be greater than the value of the `transcription` and `speaker_labels` fields by a fractional number of seconds. :attr float transcription: The seconds of audio that the service has processed for speech recognition as of this response. :attr float speaker_labels: (optional) If speaker labels are requested, the seconds of audio that the service has processed to determine speaker labels as of this response. This value often trails the value of the `transcription` field during speech recognition processing. The `transcription` and `speaker_labels` fields have identical values when the service has finished processing all audio. """ def __init__(self, received: float, seen_by_engine: float, transcription: float, *, speaker_labels: float = None) -> None: """ Initialize a ProcessedAudio object. :param float received: The seconds of audio that the service has received as of this response. The value of the field is greater than the values of the `transcription` and `speaker_labels` fields during speech recognition processing, since the service first has to receive the audio before it can begin to process it. The final value can also be greater than the value of the `transcription` and `speaker_labels` fields by a fractional number of seconds. :param float seen_by_engine: The seconds of audio that the service has passed to its speech-processing engine as of this response. The value of the field is greater than the values of the `transcription` and `speaker_labels` fields during speech recognition processing. The `received` and `seen_by_engine` fields have identical values when the service has finished processing all audio. This final value can be greater than the value of the `transcription` and `speaker_labels` fields by a fractional number of seconds. :param float transcription: The seconds of audio that the service has processed for speech recognition as of this response. :param float speaker_labels: (optional) If speaker labels are requested, the seconds of audio that the service has processed to determine speaker labels as of this response. This value often trails the value of the `transcription` field during speech recognition processing. The `transcription` and `speaker_labels` fields have identical values when the service has finished processing all audio. """ self.received = received self.seen_by_engine = seen_by_engine self.transcription = transcription self.speaker_labels = speaker_labels @classmethod def from_dict(cls, _dict: Dict) -> 'ProcessedAudio': """Initialize a ProcessedAudio object from a json dictionary.""" args = {} if 'received' in _dict: args['received'] = _dict.get('received') else: raise ValueError( 'Required property \'received\' not present in ProcessedAudio JSON' ) if 'seen_by_engine' in _dict: args['seen_by_engine'] = _dict.get('seen_by_engine') else: raise ValueError( 'Required property \'seen_by_engine\' not present in ProcessedAudio JSON' ) if 'transcription' in _dict: args['transcription'] = _dict.get('transcription') else: raise ValueError( 'Required property \'transcription\' not present in ProcessedAudio JSON' ) if 'speaker_labels' in _dict: args['speaker_labels'] = _dict.get('speaker_labels') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a ProcessedAudio object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'received') and self.received is not None: _dict['received'] = self.received if hasattr(self, 'seen_by_engine') and self.seen_by_engine is not None: _dict['seen_by_engine'] = self.seen_by_engine if hasattr(self, 'transcription') and self.transcription is not None: _dict['transcription'] = self.transcription if hasattr(self, 'speaker_labels') and self.speaker_labels is not None: _dict['speaker_labels'] = self.speaker_labels return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this ProcessedAudio object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'ProcessedAudio') -> bool: """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: 'ProcessedAudio') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class ProcessingMetrics(): """ If processing metrics are requested, information about the service's processing of the input audio. Processing metrics are not available with the synchronous [Recognize audio](#recognize) method. :attr ProcessedAudio processed_audio: Detailed timing information about the service's processing of the input audio. :attr float wall_clock_since_first_byte_received: The amount of real time in seconds that has passed since the service received the first byte of input audio. Values in this field are generally multiples of the specified metrics interval, with two differences: * Values might not reflect exact intervals (for instance, 0.25, 0.5, and so on). Actual values might be 0.27, 0.52, and so on, depending on when the service receives and processes audio. * The service also returns values for transcription events if you set the `interim_results` parameter to `true`. The service returns both processing metrics and transcription results when such events occur. :attr bool periodic: An indication of whether the metrics apply to a periodic interval or a transcription event: * `true` means that the response was triggered by a specified processing interval. The information contains processing metrics only. * `false` means that the response was triggered by a transcription event. The information contains processing metrics plus transcription results. Use the field to identify why the service generated the response and to filter different results if necessary. """ def __init__(self, processed_audio: 'ProcessedAudio', wall_clock_since_first_byte_received: float, periodic: bool) -> None: """ Initialize a ProcessingMetrics object. :param ProcessedAudio processed_audio: Detailed timing information about the service's processing of the input audio. :param float wall_clock_since_first_byte_received: The amount of real time in seconds that has passed since the service received the first byte of input audio. Values in this field are generally multiples of the specified metrics interval, with two differences: * Values might not reflect exact intervals (for instance, 0.25, 0.5, and so on). Actual values might be 0.27, 0.52, and so on, depending on when the service receives and processes audio. * The service also returns values for transcription events if you set the `interim_results` parameter to `true`. The service returns both processing metrics and transcription results when such events occur. :param bool periodic: An indication of whether the metrics apply to a periodic interval or a transcription event: * `true` means that the response was triggered by a specified processing interval. The information contains processing metrics only. * `false` means that the response was triggered by a transcription event. The information contains processing metrics plus transcription results. Use the field to identify why the service generated the response and to filter different results if necessary. """ self.processed_audio = processed_audio self.wall_clock_since_first_byte_received = wall_clock_since_first_byte_received self.periodic = periodic @classmethod def from_dict(cls, _dict: Dict) -> 'ProcessingMetrics': """Initialize a ProcessingMetrics object from a json dictionary.""" args = {} if 'processed_audio' in _dict: args['processed_audio'] = ProcessedAudio.from_dict( _dict.get('processed_audio')) else: raise ValueError( 'Required property \'processed_audio\' not present in ProcessingMetrics JSON' ) if 'wall_clock_since_first_byte_received' in _dict: args['wall_clock_since_first_byte_received'] = _dict.get( 'wall_clock_since_first_byte_received') else: raise ValueError( 'Required property \'wall_clock_since_first_byte_received\' not present in ProcessingMetrics JSON' ) if 'periodic' in _dict: args['periodic'] = _dict.get('periodic') else: raise ValueError( 'Required property \'periodic\' not present in ProcessingMetrics JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a ProcessingMetrics object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'processed_audio') and self.processed_audio is not None: _dict['processed_audio'] = self.processed_audio.to_dict() if hasattr(self, 'wall_clock_since_first_byte_received' ) and self.wall_clock_since_first_byte_received is not None: _dict[ 'wall_clock_since_first_byte_received'] = self.wall_clock_since_first_byte_received if hasattr(self, 'periodic') and self.periodic is not None: _dict['periodic'] = self.periodic return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this ProcessingMetrics object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'ProcessingMetrics') -> bool: """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: 'ProcessingMetrics') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RecognitionJob(): """ Information about a current asynchronous speech recognition job. :attr str id: The ID of the asynchronous job. :attr str status: The current status of the job: * `waiting`: The service is preparing the job for processing. The service returns this status when the job is initially created or when it is waiting for capacity to process the job. The job remains in this state until the service has the capacity to begin processing it. * `processing`: The service is actively processing the job. * `completed`: The service has finished processing the job. If the job specified a callback URL and the event `recognitions.completed_with_results`, the service sent the results with the callback notification. Otherwise, you must retrieve the results by checking the individual job. * `failed`: The job failed. :attr str created: The date and time in Coordinated Universal Time (UTC) at which the job was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). :attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the job was last updated by the service. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). This field is returned only by the [Check jobs](#checkjobs) and [Check a job[(#checkjob) methods. :attr str url: (optional) The URL to use to request information about the job with the [Check a job](#checkjob) method. This field is returned only by the [Create a job](#createjob) 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](#checkjobs) 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](#checkjob) 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](#createjob) method. (If you use the `character_insertion_bias` parameter with a previous-generation model, the warning message refers to the parameter as `lambdaBias`.). """ def __init__(self, id: str, status: str, created: str, *, updated: str = None, url: str = None, user_token: str = None, results: List['SpeechRecognitionResults'] = None, warnings: List[str] = None) -> 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](#checkjobs) and [Check a job[(#checkjob) methods. :param str url: (optional) The URL to use to request information about the job with the [Check a job](#checkjob) method. This field is returned only by the [Create a job](#createjob) 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](#checkjobs) 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](#checkjob) 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](#createjob) method. (If you use the `character_insertion_bias` parameter with a previous-generation model, the warning message refers to the parameter as `lambdaBias`.). """ 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: Dict) -> 'RecognitionJob': """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) @classmethod def _from_dict(cls, _dict): """Initialize a RecognitionJob object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RecognitionJob object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RecognitionJob') -> bool: """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: 'RecognitionJob') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ 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. """ WAITING = 'waiting' PROCESSING = 'processing' COMPLETED = 'completed' FAILED = 'failed' class RecognitionJobs(): """ Information about current asynchronous speech recognition jobs. :attr List[RecognitionJob] recognitions: An array of `RecognitionJob` objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs. """ def __init__(self, recognitions: List['RecognitionJob']) -> None: """ Initialize a RecognitionJobs object. :param List[RecognitionJob] recognitions: An array of `RecognitionJob` objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs. """ self.recognitions = recognitions @classmethod def from_dict(cls, _dict: Dict) -> 'RecognitionJobs': """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) @classmethod def _from_dict(cls, _dict): """Initialize a RecognitionJobs object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RecognitionJobs object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RecognitionJobs') -> bool: """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: 'RecognitionJobs') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class RegisterStatus(): """ Information about a request to register a callback for asynchronous speech recognition. :attr str status: The current status of the job: * `created`: The service successfully allowlisted the callback URL as a result of the call. * `already created`: The URL was already allowlisted. :attr str url: The callback URL that is successfully registered. """ def __init__(self, status: str, url: str) -> None: """ Initialize a RegisterStatus object. :param str status: The current status of the job: * `created`: The service successfully allowlisted the callback URL as a result of the call. * `already created`: The URL was already allowlisted. :param str url: The callback URL that is successfully registered. """ self.status = status self.url = url @classmethod def from_dict(cls, _dict: Dict) -> 'RegisterStatus': """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) @classmethod def _from_dict(cls, _dict): """Initialize a RegisterStatus object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this RegisterStatus object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'RegisterStatus') -> bool: """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: 'RegisterStatus') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class StatusEnum(str, Enum): """ The current status of the job: * `created`: The service successfully allowlisted the callback URL as a result of the call. * `already created`: The URL was already allowlisted. """ CREATED = 'created' ALREADY_CREATED = 'already created' class SpeakerLabelsResult(): """ Information about the speakers from speech recognition results. :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: 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_: float, to: float, speaker: int, confidence: float, final: bool) -> None: """ 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: 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 = final @classmethod def from_dict(cls, _dict: Dict) -> 'SpeakerLabelsResult': """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: args['final'] = _dict.get('final') else: raise ValueError( 'Required property \'final\' not present in SpeakerLabelsResult JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a SpeakerLabelsResult object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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') and self.final is not None: _dict['final'] = self.final return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeakerLabelsResult object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeakerLabelsResult') -> bool: """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: 'SpeakerLabelsResult') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechModel(): """ Information about an available language model. :attr str name: The name of the model for use as an identifier in calls to the service (for example, `en-US_BroadbandModel`). :attr str language: The language identifier of the model (for example, `en-US`). :attr int rate: The sampling rate (minimum acceptable rate for audio) used by the model in Hertz. :attr str url: The URI for the model. :attr SupportedFeatures supported_features: Indicates whether select service features are supported with the model. :attr str description: A brief description of the model. """ def __init__(self, name: str, language: str, rate: int, url: str, supported_features: 'SupportedFeatures', description: str) -> None: """ 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: Indicates whether select service features 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: Dict) -> 'SpeechModel': """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) @classmethod def _from_dict(cls, _dict): """Initialize a SpeechModel object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeechModel object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeechModel') -> bool: """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: 'SpeechModel') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechModels(): """ Information about the available language models. :attr List[SpeechModel] models: An array of `SpeechModel` objects that provides information about each available model. """ def __init__(self, models: List['SpeechModel']) -> None: """ Initialize a SpeechModels object. :param List[SpeechModel] models: An array of `SpeechModel` objects that provides information about each available model. """ self.models = models @classmethod def from_dict(cls, _dict: Dict) -> 'SpeechModels': """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) @classmethod def _from_dict(cls, _dict): """Initialize a SpeechModels object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeechModels object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeechModels') -> bool: """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: 'SpeechModels') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechRecognitionAlternative(): """ An alternative transcript from speech recognition results. :attr str transcript: A transcription of the audio. :attr float confidence: (optional) A score that indicates the service's confidence in the transcript in the range of 0.0 to 1.0. The service returns a confidence score 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",0.86]]`. Confidence scores are returned only for the best alternative and only with results marked as final. """ def __init__(self, transcript: str, *, confidence: float = None, timestamps: List[str] = None, word_confidence: List[str] = None) -> 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. The service returns a confidence score 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",0.86]]`. 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: Dict) -> 'SpeechRecognitionAlternative': """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) @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionAlternative object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeechRecognitionAlternative object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeechRecognitionAlternative') -> bool: """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: 'SpeechRecognitionAlternative') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SpeechRecognitionResult(): """ Component results for a speech recognition request. :attr bool final: An indication of whether the transcription results are final: * If `true`, the results for this utterance are final. They are guaranteed not to be updated further. * If `false`, the results are interim. They can be updated with further interim results until final results are eventually sent. **Note:** Because `final` is a reserved word in Java and Swift, the field is renamed `xFinal` in Java and is escaped with back quotes in Swift. :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. :attr str end_of_utterance: (optional) If the `split_transcript_at_phrase_end` parameter is `true`, describes the reason for the split: * `end_of_data` - The end of the input audio stream. * `full_stop` - A full semantic stop, such as for the conclusion of a grammatical sentence. The insertion of splits is influenced by the base language model and biased by custom language models and grammars. * `reset` - The amount of audio that is currently being processed exceeds the two-minute maximum. The service splits the transcript to avoid excessive memory use. * `silence` - A pause or silence that is at least as long as the pause interval. """ def __init__(self, final: bool, alternatives: List['SpeechRecognitionAlternative'], *, keywords_result: dict = None, word_alternatives: List['WordAlternativeResults'] = None, end_of_utterance: str = None) -> None: """ Initialize a SpeechRecognitionResult object. :param bool final: An indication of whether the transcription results are final: * If `true`, the results for this utterance are final. They are guaranteed not to be updated further. * If `false`, the results are interim. They can be updated with further interim results until final results are eventually sent. **Note:** Because `final` is a reserved word in Java and Swift, the field is renamed `xFinal` in Java and is escaped with back quotes in Swift. :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. :param str end_of_utterance: (optional) If the `split_transcript_at_phrase_end` parameter is `true`, describes the reason for the split: * `end_of_data` - The end of the input audio stream. * `full_stop` - A full semantic stop, such as for the conclusion of a grammatical sentence. The insertion of splits is influenced by the base language model and biased by custom language models and grammars. * `reset` - The amount of audio that is currently being processed exceeds the two-minute maximum. The service splits the transcript to avoid excessive memory use. * `silence` - A pause or silence that is at least as long as the pause interval. """ self.final = final self.alternatives = alternatives self.keywords_result = keywords_result self.word_alternatives = word_alternatives self.end_of_utterance = end_of_utterance @classmethod def from_dict(cls, _dict: Dict) -> 'SpeechRecognitionResult': """Initialize a SpeechRecognitionResult object from a json dictionary.""" args = {} if 'final' in _dict: args['final'] = _dict.get('final') 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') ] if 'end_of_utterance' in _dict: args['end_of_utterance'] = _dict.get('end_of_utterance') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionResult object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'final') and self.final is not None: _dict['final'] = self.final 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 ] if hasattr(self, 'end_of_utterance') and self.end_of_utterance is not None: _dict['end_of_utterance'] = self.end_of_utterance return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeechRecognitionResult object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeechRecognitionResult') -> bool: """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: 'SpeechRecognitionResult') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class EndOfUtteranceEnum(str, Enum): """ If the `split_transcript_at_phrase_end` parameter is `true`, describes the reason for the split: * `end_of_data` - The end of the input audio stream. * `full_stop` - A full semantic stop, such as for the conclusion of a grammatical sentence. The insertion of splits is influenced by the base language model and biased by custom language models and grammars. * `reset` - The amount of audio that is currently being processed exceeds the two-minute maximum. The service splits the transcript to avoid excessive memory use. * `silence` - A pause or silence that is at least as long as the pause interval. """ END_OF_DATA = 'end_of_data' FULL_STOP = 'full_stop' RESET = 'reset' SILENCE = 'silence' class SpeechRecognitionResults(): """ The complete results for a speech recognition request. :attr List[SpeechRecognitionResult] results: (optional) An array of `SpeechRecognitionResult` objects that can include interim and final results (interim results are returned only if supported by the method). Final results are guaranteed not to change; interim results might be replaced by further interim results and eventually final results. For the HTTP interfaces, all results arrive at the same time. For the WebSocket interface, results can be sent as multiple separate responses. The service periodically sends updates to the results list. The `result_index` is incremented to the lowest index in the array that has changed for new results. For more information, see [Understanding speech recognition results](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-basic-response). :attr int result_index: (optional) An index that indicates a change point in the `results` array. The service increments the index for additional results that it sends for new audio for the same request. All results with the same index are delivered at the same time. The same index can include multiple final results that are delivered with the same response. :attr List[SpeakerLabelsResult] speaker_labels: (optional) An array of `SpeakerLabelsResult` objects that identifies which words were spoken by which speakers in a multi-person exchange. The array is returned only if the `speaker_labels` parameter is `true`. When interim results are also requested for methods that support them, it is possible for a `SpeechRecognitionResults` object to include only the `speaker_labels` field. :attr ProcessingMetrics processing_metrics: (optional) If processing metrics are requested, information about the service's processing of the input audio. Processing metrics are not available with the synchronous [Recognize audio](#recognize) method. :attr AudioMetrics audio_metrics: (optional) If audio metrics are requested, information about the signal characteristics of the input audio. :attr List[str] warnings: (optional) An array of warning messages associated with the request: * Warnings for invalid parameters or fields can include a descriptive message and a list of invalid argument strings, for example, `"Unknown arguments:"` or `"Unknown url query arguments:"` followed by a list of the form `"{invalid_arg_1}, {invalid_arg_2}."` (If you use the `character_insertion_bias` parameter with a previous-generation model, the warning message refers to the parameter as `lambdaBias`.) * 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: List['SpeechRecognitionResult'] = None, result_index: int = None, speaker_labels: List['SpeakerLabelsResult'] = None, processing_metrics: 'ProcessingMetrics' = None, audio_metrics: 'AudioMetrics' = None, warnings: List[str] = None) -> 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 eventually final results. For the HTTP interfaces, all results arrive at the same time. For the WebSocket interface, results can be sent as multiple separate responses. The service periodically sends updates to the results list. The `result_index` is incremented to the lowest index in the array that has changed for new results. For more information, see [Understanding speech recognition results](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-basic-response). :param int result_index: (optional) An index that indicates a change point in the `results` array. The service increments the index for additional results that it sends for new audio for the same request. All results with the same index are delivered at the same time. The same index can include multiple final results that are delivered with the same response. :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 ProcessingMetrics processing_metrics: (optional) If processing metrics are requested, information about the service's processing of the input audio. Processing metrics are not available with the synchronous [Recognize audio](#recognize) method. :param AudioMetrics audio_metrics: (optional) If audio metrics are requested, information about the signal characteristics of the input audio. :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}."` (If you use the `character_insertion_bias` parameter with a previous-generation model, the warning message refers to the parameter as `lambdaBias`.) * 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.processing_metrics = processing_metrics self.audio_metrics = audio_metrics self.warnings = warnings @classmethod def from_dict(cls, _dict: Dict) -> 'SpeechRecognitionResults': """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 'processing_metrics' in _dict: args['processing_metrics'] = ProcessingMetrics.from_dict( _dict.get('processing_metrics')) if 'audio_metrics' in _dict: args['audio_metrics'] = AudioMetrics.from_dict( _dict.get('audio_metrics')) if 'warnings' in _dict: args['warnings'] = _dict.get('warnings') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a SpeechRecognitionResults object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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, 'processing_metrics') and self.processing_metrics is not None: _dict['processing_metrics'] = self.processing_metrics.to_dict() if hasattr(self, 'audio_metrics') and self.audio_metrics is not None: _dict['audio_metrics'] = self.audio_metrics.to_dict() if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = self.warnings return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SpeechRecognitionResults object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SpeechRecognitionResults') -> bool: """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: 'SpeechRecognitionResults') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class SupportedFeatures(): """ Indicates whether select service features 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 custom_acoustic_model: Indicates whether the customization interface can be used to create a custom acoustic model based on the language model. :attr bool speaker_labels: Indicates whether the `speaker_labels` parameter can be used with the language model. **Note:** The field returns `true` for all models. However, speaker labels are supported for use only with the following languages and models: * _For previous-generation models,_ the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. * _For next-generation models,_ the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only. Speaker labels are not supported for use with any other languages or models. :attr bool low_latency: (optional) Indicates whether the `low_latency` parameter can be used with a next-generation language model. The field is returned only for next-generation models. Previous-generation models do not support the `low_latency` parameter. """ def __init__(self, custom_language_model: bool, custom_acoustic_model: bool, speaker_labels: bool, *, low_latency: bool = None) -> None: """ 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 custom_acoustic_model: Indicates whether the customization interface can be used to create a custom acoustic model based on the language model. :param bool speaker_labels: Indicates whether the `speaker_labels` parameter can be used with the language model. **Note:** The field returns `true` for all models. However, speaker labels are supported for use only with the following languages and models: * _For previous-generation models,_ the parameter can be used with Australian English, US English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model) transcription only. * _For next-generation models,_ the parameter can be used with Czech, English (Australian, Indian, UK, and US), German, Japanese, Korean, and Spanish transcription only. Speaker labels are not supported for use with any other languages or models. :param bool low_latency: (optional) Indicates whether the `low_latency` parameter can be used with a next-generation language model. The field is returned only for next-generation models. Previous-generation models do not support the `low_latency` parameter. """ self.custom_language_model = custom_language_model self.custom_acoustic_model = custom_acoustic_model self.speaker_labels = speaker_labels self.low_latency = low_latency @classmethod def from_dict(cls, _dict: Dict) -> 'SupportedFeatures': """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 'custom_acoustic_model' in _dict: args['custom_acoustic_model'] = _dict.get('custom_acoustic_model') else: raise ValueError( 'Required property \'custom_acoustic_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' ) if 'low_latency' in _dict: args['low_latency'] = _dict.get('low_latency') return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a SupportedFeatures object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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, 'custom_acoustic_model' ) and self.custom_acoustic_model is not None: _dict['custom_acoustic_model'] = self.custom_acoustic_model if hasattr(self, 'speaker_labels') and self.speaker_labels is not None: _dict['speaker_labels'] = self.speaker_labels if hasattr(self, 'low_latency') and self.low_latency is not None: _dict['low_latency'] = self.low_latency return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this SupportedFeatures object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'SupportedFeatures') -> bool: """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: 'SupportedFeatures') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TrainingResponse(): """ The response from training of a custom language or custom acoustic model. :attr List[TrainingWarning] warnings: (optional) An array of `TrainingWarning` objects that lists any invalid resources contained in the custom model. For custom language models, invalid resources are grouped and identified by type of resource. The method can return warnings only if the `strict` parameter is set to `false`. """ def __init__(self, *, warnings: List['TrainingWarning'] = None) -> None: """ Initialize a TrainingResponse object. :param List[TrainingWarning] warnings: (optional) An array of `TrainingWarning` objects that lists any invalid resources contained in the custom model. For custom language models, invalid resources are grouped and identified by type of resource. The method can return warnings only if the `strict` parameter is set to `false`. """ self.warnings = warnings @classmethod def from_dict(cls, _dict: Dict) -> 'TrainingResponse': """Initialize a TrainingResponse object from a json dictionary.""" args = {} if 'warnings' in _dict: args['warnings'] = [ TrainingWarning.from_dict(x) for x in _dict.get('warnings') ] return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a TrainingResponse object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'warnings') and self.warnings is not None: _dict['warnings'] = [x.to_dict() for x in self.warnings] return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this TrainingResponse object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'TrainingResponse') -> bool: """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: 'TrainingResponse') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class TrainingWarning(): """ A warning from training of a custom language or custom acoustic model. :attr str code: An identifier for the type of invalid resources listed in the `description` field. :attr str message: A warning message that lists the invalid resources that are excluded from the custom model's training. The message has the following format: `Analysis of the following {resource_type} has not completed successfully: [{resource_names}]. They will be excluded from custom {model_type} model training.`. """ def __init__(self, code: str, message: str) -> None: """ Initialize a TrainingWarning object. :param str code: An identifier for the type of invalid resources listed in the `description` field. :param str message: A warning message that lists the invalid resources that are excluded from the custom model's training. The message has the following format: `Analysis of the following {resource_type} has not completed successfully: [{resource_names}]. They will be excluded from custom {model_type} model training.`. """ self.code = code self.message = message @classmethod def from_dict(cls, _dict: Dict) -> 'TrainingWarning': """Initialize a TrainingWarning object from a json dictionary.""" args = {} if 'code' in _dict: args['code'] = _dict.get('code') else: raise ValueError( 'Required property \'code\' not present in TrainingWarning JSON' ) if 'message' in _dict: args['message'] = _dict.get('message') else: raise ValueError( 'Required property \'message\' not present in TrainingWarning JSON' ) return cls(**args) @classmethod def _from_dict(cls, _dict): """Initialize a TrainingWarning object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """Return a json dictionary representing this model.""" _dict = {} if hasattr(self, 'code') and self.code is not None: _dict['code'] = self.code if hasattr(self, 'message') and self.message is not None: _dict['message'] = self.message return _dict def _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this TrainingWarning object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'TrainingWarning') -> bool: """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: 'TrainingWarning') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class CodeEnum(str, Enum): """ An identifier for the type of invalid resources listed in the `description` field. """ INVALID_AUDIO_FILES = 'invalid_audio_files' INVALID_CORPUS_FILES = 'invalid_corpus_files' INVALID_GRAMMAR_FILES = 'invalid_grammar_files' INVALID_WORDS = 'invalid_words' class Word(): """ Information about a word from a custom language model. :attr str word: A word from the custom model's words resource. The spelling of the word is used to train the model. :attr List[str] sounds_like: An array of as many as five pronunciations for the word. * _For a custom model that is based on a previous-generation model_, in addition to sounds-like pronunciations that were added by a user, the array can include a sounds-like pronunciation that is automatically generated by the service if none is provided when the word is added to the custom model. * _For a custom model that is based on a next-generation model_, the array can include only sounds-like pronunciations that were added by a user. :attr str display_as: The spelling of the word that the service uses to display the word in a transcript. * _For a custom model that is based on a previous-generation model_, the field can contain an empty string if no display-as value is provided for a word that exists in the service's base vocabulary. In this case, the word is displayed as it is spelled. * _For a custom model that is based on a next-generation model_, the service uses the spelling of the word as the value of the display-as field when the word is added to the model. :attr int count: _For a custom model that is based on a previous-generation model_, a sum of the number of times the word is found across all corpora and grammars. 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 or grammars, the count begins at `1`; if the word is added from a corpus or grammar first and later modified, the count reflects only the number of times it is found in corpora and grammars. _For a custom model that is based on a next-generation model_, the `count` field for any word is always `1`. :attr List[str] source: An array of sources that describes how the word was added to the custom model's words resource. * _For a custom model that is based on previous-generation model,_ the field includes the name of each corpus and grammar from which the service extracted the word. For OOV that are added by multiple corpora or grammars, the names of all corpora and grammars are listed. If you modified or added the word directly, the field includes the string `user`. * _For a custom model that is based on a next-generation model,_ this field shows only `user` for custom words that were added directly to the custom model. Words from corpora and grammars are not added to the words resource for custom models that are based on next-generation models. :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: str, sounds_like: List[str], display_as: str, count: int, source: List[str], *, error: List['WordError'] = None) -> 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 as many as five pronunciations for the word. * _For a custom model that is based on a previous-generation model_, in addition to sounds-like pronunciations that were added by a user, the array can include a sounds-like pronunciation that is automatically generated by the service if none is provided when the word is added to the custom model. * _For a custom model that is based on a next-generation model_, the array can include only sounds-like pronunciations that were added by a user. :param str display_as: The spelling of the word that the service uses to display the word in a transcript. * _For a custom model that is based on a previous-generation model_, the field can contain an empty string if no display-as value is provided for a word that exists in the service's base vocabulary. In this case, the word is displayed as it is spelled. * _For a custom model that is based on a next-generation model_, the service uses the spelling of the word as the value of the display-as field when the word is added to the model. :param int count: _For a custom model that is based on a previous-generation model_, a sum of the number of times the word is found across all corpora and grammars. 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 or grammars, the count begins at `1`; if the word is added from a corpus or grammar first and later modified, the count reflects only the number of times it is found in corpora and grammars. _For a custom model that is based on a next-generation model_, the `count` field for any word is always `1`. :param List[str] source: An array of sources that describes how the word was added to the custom model's words resource. * _For a custom model that is based on previous-generation model,_ the field includes the name of each corpus and grammar from which the service extracted the word. For OOV that are added by multiple corpora or grammars, the names of all corpora and grammars are listed. If you modified or added the word directly, the field includes the string `user`. * _For a custom model that is based on a next-generation model,_ this field shows only `user` for custom words that were added directly to the custom model. Words from corpora and grammars are not added to the words resource for custom models that are based on next-generation models. :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: Dict) -> 'Word': """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) @classmethod def _from_dict(cls, _dict): """Initialize a Word object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Word object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Word') -> bool: """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: 'Word') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordAlternativeResult(): """ An alternative hypothesis for a word from speech recognition results. :attr float confidence: A confidence score for the word alternative hypothesis in the range of 0.0 to 1.0. :attr str word: An alternative hypothesis for a word from the input audio. """ def __init__(self, confidence: float, word: str) -> None: """ 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: Dict) -> 'WordAlternativeResult': """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) @classmethod def _from_dict(cls, _dict): """Initialize a WordAlternativeResult object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this WordAlternativeResult object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'WordAlternativeResult') -> bool: """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: 'WordAlternativeResult') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordAlternativeResults(): """ Information about alternative hypotheses for words from speech recognition results. :attr float start_time: The start time in seconds of the word from the input audio that corresponds to the word alternatives. :attr float end_time: The end time in seconds of the word from the input audio that corresponds to the word alternatives. :attr List[WordAlternativeResult] alternatives: An array of alternative hypotheses for a word from the input audio. """ def __init__(self, start_time: float, end_time: float, alternatives: List['WordAlternativeResult']) -> None: """ 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: Dict) -> 'WordAlternativeResults': """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) @classmethod def _from_dict(cls, _dict): """Initialize a WordAlternativeResults object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this WordAlternativeResults object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'WordAlternativeResults') -> bool: """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: 'WordAlternativeResults') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class WordError(): """ An error associated with a word from a custom language model. :attr str element: A key-value pair that describes an error associated with the definition of a word in the words resource. The pair has the format `"element": "message"`, where `element` is the aspect of the definition that caused the problem and `message` describes the problem. The following example describes a problem with one of the word's sounds-like definitions: `"{sounds_like_string}": "Numbers are not allowed in sounds-like. You can try for example '{suggested_string}'."`. """ def __init__(self, element: str) -> None: """ 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. The pair has the format `"element": "message"`, where `element` is the aspect of the definition that caused the problem and `message` describes the problem. The following example describes a problem with one of the word's sounds-like definitions: `"{sounds_like_string}": "Numbers are not allowed in sounds-like. You can try for example '{suggested_string}'."`. """ self.element = element @classmethod def from_dict(cls, _dict: Dict) -> 'WordError': """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) @classmethod def _from_dict(cls, _dict): """Initialize a WordError object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this WordError object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'WordError') -> bool: """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: 'WordError') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other class Words(): """ Information about the words from a custom language model. :attr List[Word] words: An array of `Word` 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: List['Word']) -> None: """ Initialize a Words object. :param List[Word] words: An array of `Word` 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: Dict) -> 'Words': """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) @classmethod def _from_dict(cls, _dict): """Initialize a Words object from a json dictionary.""" return cls.from_dict(_dict) def to_dict(self) -> Dict: """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 _to_dict(self): """Return a json dictionary representing this model.""" return self.to_dict() def __str__(self) -> str: """Return a `str` version of this Words object.""" return json.dumps(self.to_dict(), indent=2) def __eq__(self, other: 'Words') -> bool: """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: 'Words') -> bool: """Return `true` when self and other are not equal, false otherwise.""" return not self == other