# coding: utf-8
# Copyright 2018 IBM All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The IBM® Speech to Text service provides an API that uses IBM's speech-recognition
capabilities to produce transcripts of spoken audio. The service can transcribe speech
from various languages and audio formats. It addition to basic transcription, the service
can produce detailed information about many aspects of the audio. For most languages, the
service supports two sampling rates, broadband and narrowband. It returns all JSON
response content in the UTF-8 character set. For more information about the service, see
the [IBM® Cloud
documentation](https://console.bluemix.net/docs/services/speech-to-text/index.html).
### API usage guidelines
* **Audio formats:** The service accepts audio in many formats (MIME types). See [Audio
formats](https://console.bluemix.net/docs/services/speech-to-text/audio-formats.html).
* **HTTP interfaces:** The service provides three HTTP interfaces for speech recognition.
The sessionless interface includes a single synchronous method. The session-based
interface includes multiple synchronous methods for maintaining a long, multi-turn
exchange with the service. And the asynchronous interface provides multiple methods that
use registered callbacks and polling for non-blocking recognition. See [The HTTP REST
interface](https://console.bluemix.net/docs/services/speech-to-text/http.html) and [The
asynchronous HTTP
interface](https://console.bluemix.net/docs/services/speech-to-text/async.html).
* **WebSocket interface:** The service also offers a WebSocket interface for speech
recognition. The WebSocket interface provides a full-duplex, low-latency communication
channel. Clients send requests and audio to the service and receive results over a single
connection in an asynchronous fashion. See [The WebSocket
interface](https://console.bluemix.net/docs/services/speech-to-text/websockets.html).
* **Customization:** Use language model customization to expand the vocabulary of a base
model with domain-specific terminology. Use acoustic model customization to adapt a base
model for the acoustic characteristics of your audio. Language model customization is
generally available for production use by most supported languages; acoustic model
customization is beta functionality that is available for all supported languages. See
[The customization
interface](https://console.bluemix.net/docs/services/speech-to-text/custom.html).
* **Customization IDs:** Many methods accept a customization ID to identify a custom
language or custom acoustic model. Customization IDs are Globally Unique Identifiers
(GUIDs). They are hexadecimal strings that have the format
`xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx`.
* **`X-Watson-Learning-Opt-Out`:** By default, all Watson services log requests and their
results. Logging is done only to improve the services for future users. The logged data is
not shared or made public. To prevent IBM from accessing your data for general service
improvements, set the `X-Watson-Learning-Opt-Out` request header to `true` for all
requests. You must set the header on each request that you do not want IBM to access for
general service improvements.
Methods of the customization interface do not log corpora, words, and audio resources
that you use to build custom models. Your training data is never used to improve the
service's base models. However, the service does log such data when a custom model is used
with a recognition request. You must set the `X-Watson-Learning-Opt-Out` request header to
`true` to prevent IBM from accessing the data to improve the service.
* **`X-Watson-Metadata`**: This header allows you to associate a customer ID with data
that is passed with a request. If necessary, you can use the **Delete labeled data**
method to delete the data for a customer ID. See [Information
security](https://console.bluemix.net/docs/services/speech-to-text/information-security.html).
"""
from __future__ import absolute_import
import json
from .watson_service import WatsonService, _remove_null_values
from .utils import deprecated
from watson_developer_cloud.websocket import RecognizeCallback, RecognizeListener
import base64
try:
from urllib.parse import urlencode
except ImportError:
from urllib import urlencode
##############################################################################
# Service
##############################################################################
[docs]class SpeechToTextV1(WatsonService):
"""The Speech to Text V1 service."""
default_url = 'https://stream.watsonplatform.net/speech-to-text/api'
def __init__(
self,
url=default_url,
username=None,
password=None,
iam_api_key=None,
iam_access_token=None,
iam_url=None,
):
"""
Construct a new client for the Speech to Text service.
:param str url: The base url to use when contacting the service (e.g.
"https://stream.watsonplatform.net/speech-to-text/api").
The base url may differ between Bluemix regions.
:param str username: The username used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of Bluemix. When running on
Bluemix, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str password: The password used to authenticate with the service.
Username and password credentials are only required to run your
application locally or outside of Bluemix. When running on
Bluemix, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str iam_api_key: An API key that can be used to request IAM tokens. If
this API key is provided, the SDK will manage the token and handle the
refreshing.
:param str iam_access_token: An IAM access token is fully managed by the application.
Responsibility falls on the application to refresh the token, either before
it expires or reactively upon receiving a 401 from the service as any requests
made with an expired token will fail.
:param str iam_url: An optional URL for the IAM service API. Defaults to
'https://iam.ng.bluemix.net/identity/token'.
"""
WatsonService.__init__(
self,
vcap_services_name='speech_to_text',
url=url,
username=username,
password=password,
iam_api_key=iam_api_key,
iam_access_token=iam_access_token,
iam_url=iam_url,
use_vcap_services=True)
#########################
# Models
#########################
[docs] def get_model(self, model_id, **kwargs):
"""
Get a model.
Retrieves information about 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.
:param str model_id: The identifier of the model in the form of its name from the output of the **Get models** method.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `SpeechModel` response.
:rtype: dict
"""
if model_id is None:
raise ValueError('model_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/models/{0}'.format(*self._encode_path_vars(model_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def list_models(self, **kwargs):
"""
Get models.
Retrieves a list of 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.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `SpeechModels` response.
:rtype: dict
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/models'
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
@deprecated('Use list_models instead.')
def models(self):
return self.list_models()
#########################
# Sessionless
#########################
[docs] def recognize(self,
model=None,
customization_id=None,
acoustic_customization_id=None,
customization_weight=None,
version=None,
audio=None,
content_type=None,
inactivity_timeout=None,
keywords=None,
keywords_threshold=None,
max_alternatives=None,
word_alternatives_threshold=None,
word_confidence=None,
timestamps=None,
profanity_filter=None,
smart_formatting=None,
speaker_labels=None,
**kwargs):
"""
Sends audio for speech recognition in sessionless mode.
:param str model: The identifier of the model that is to be used for the recognition request.
:param str customization_id: The GUID of a custom language model that is to be used with the request. The base model of the specified custom language model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom language model is used.
:param str acoustic_customization_id: The GUID of a custom acoustic model that is to be used with the request. The base model of the specified custom acoustic model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom acoustic model is used.
:param float customization_weight: NON-MULTIPART ONLY: If you specify a customization ID with the request, you can use the customization weight to tell the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. 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.
:param str version: The version of the specified base `model` that is to be used for speech recognition. 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. For more information, see [Base model version](https://console.bluemix.net/docs/services/speech-to-text/input.html#version).
:param str audio: NON-MULTIPART ONLY: Audio to transcribe in the format specified by the `Content-Type` header. **Required for a non-multipart request.**.
:param str content_type: The type of the input: audio/basic, audio/flac, audio/l16, audio/mp3, audio/mpeg, audio/mulaw, audio/ogg, audio/ogg;codecs=opus, audio/ogg;codecs=vorbis, audio/wav, audio/webm, audio/webm;codecs=opus, audio/webm;codecs=vorbis, or multipart/form-data.
:param int inactivity_timeout: NON-MULTIPART ONLY: The time in seconds after which, if only silence (no speech) is detected in submitted audio, the connection is closed with a 400 error. Useful for stopping audio submission from a live microphone when a user simply walks away. Use `-1` for infinity.
:param list[str] keywords: NON-MULTIPART ONLY: Array of keyword strings to spot in the audio. Each keyword string can include one or more tokens. Keywords are spotted only in the final hypothesis, not in interim results. If you specify any keywords, you must also specify a keywords threshold. You can spot a maximum of 1000 keywords. Omit the parameter or specify an empty array if you do not need to spot keywords.
:param float keywords_threshold: NON-MULTIPART ONLY: 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 and 1 inclusive. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords.
:param int max_alternatives: NON-MULTIPART ONLY: Maximum number of alternative transcripts to be returned. By default, a single transcription is returned.
:param float word_alternatives_threshold: NON-MULTIPART ONLY: 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 and 1 inclusive. No alternative words are computed if you omit the parameter.
:param bool word_confidence: NON-MULTIPART ONLY: If `true`, confidence measure per word is returned.
:param bool timestamps: NON-MULTIPART ONLY: If `true`, time alignment for each word is returned.
:param bool profanity_filter: NON-MULTIPART ONLY: If `true` (the default), filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to `false` to return results with no censoring. Applies to US English transcription only.
:param bool smart_formatting: NON-MULTIPART ONLY: If `true`, 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. If `false` (the default), no formatting is performed. Applies to US English transcription only.
:param bool speaker_labels: NON-MULTIPART ONLY: Indicates whether labels that identify which words were spoken by which participants in a multi-person exchange are to be included in the response. The default is `false`; no speaker labels are returned. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. To determine whether a language model supports speaker labels, use the **Get models** method and check that the attribute `speaker_labels` is set to `true`. You can also refer to [Speaker labels](https://console.bluemix.net/docs/services/speech-to-text/output.html#speaker_labels).
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `SpeechRecognitionResults` response.
:rtype: dict
"""
headers = {'Content-Type': content_type}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {
'model': model,
'customization_id': customization_id,
'acoustic_customization_id': acoustic_customization_id,
'customization_weight': customization_weight,
'version': version,
'base_model_version': version,
'inactivity_timeout': inactivity_timeout,
'keywords': self._convert_list(keywords),
'keywords_threshold': keywords_threshold,
'max_alternatives': max_alternatives,
'word_alternatives_threshold': word_alternatives_threshold,
'word_confidence': word_confidence,
'timestamps': timestamps,
'profanity_filter': profanity_filter,
'smart_formatting': smart_formatting,
'speaker_labels': speaker_labels
}
data = audio
url = '/v1/recognize'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return response
[docs] def recognize_with_websocket(self,
audio=None,
content_type='audio/l16; rate=44100',
model='en-US_BroadbandModel',
recognize_callback=None,
customization_id=None,
acoustic_customization_id=None,
customization_weight=None,
version=None,
inactivity_timeout=None,
interim_results=True,
keywords=None,
keywords_threshold=None,
max_alternatives=1,
word_alternatives_threshold=None,
word_confidence=False,
timestamps=False,
profanity_filter=None,
smart_formatting=False,
speaker_labels=None,
**kwargs):
"""
Sends audio for speech recognition using web sockets.
:param str audio: Audio to transcribe in the format specified by the `Content-Type` header.
:param str content_type: The type of the input: audio/basic, audio/flac, audio/l16, audio/mp3, audio/mpeg, audio/mulaw, audio/ogg, audio/ogg;codecs=opus, audio/ogg;codecs=vorbis, audio/wav, audio/webm, audio/webm;codecs=opus, audio/webm;codecs=vorbis, or multipart/form-data.
:param str model: The identifier of the model to be used for the recognition request.
:param RecognizeCallback recognize_callback: The instance handling events returned from the service.
:param str customization_id: The GUID of a custom language model that is to be used with the request. The base model of the specified custom language model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom language model is used.
:param str acoustic_customization_id: The GUID of a custom acoustic model that is to be used with the request. The base model of the specified custom acoustic model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom acoustic model is used.
:param float customization_weight: If you specify a `customization_id` with the request, you can use the `customization_weight` parameter to tell 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. 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.
:param str version: The version of the specified base `model` that is to be used for speech recognition. 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. For more information, see [Base model version](https://console.bluemix.net/docs/services/speech-to-text/input.html#version).
:param int inactivity_timeout: The time in seconds after which, if only silence (no speech) is detected in submitted audio, the connection is closed with a 400 error. Useful for stopping audio submission from a live microphone when a user simply walks away. Use `-1` for infinity.
:param bool interim_results: Send back non-final previews of each "sentence" as it is being processed. These results are ignored in text mode.
:param list[str] keywords: Array of keyword strings to spot in the audio. Each keyword string can include one or more tokens. Keywords are spotted only in the final hypothesis, not in interim results. 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.
:param float keywords_threshold: 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 and 1 inclusive. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords.
:param int max_alternatives: Maximum number of alternative transcripts to be returned. By default, a single transcription is returned.
:param float word_alternatives_threshold: 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 and 1 inclusive. No alternative words are computed if you omit the parameter.
:param bool word_confidence: If `true`, confidence measure per word is returned.
:param bool timestamps: If `true`, time alignment for each word is returned.
:param bool profanity_filter: If `true` (the default), filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to `false` to return results with no censoring. Applies to US English transcription only.
:param bool smart_formatting: If `true`, 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. If `false` (the default), no formatting is performed. Applies to US English transcription only.
:param bool speaker_labels: Indicates whether labels that identify which words were spoken by which participants in a multi-person exchange are to be included in the response. The default is `false`; no speaker labels are returned. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. To determine whether a language model supports speaker labels, use the `GET /v1/models` method and check that the attribute `speaker_labels` is set to `true`. You can also refer to [Speaker labels](https://console.bluemix.net/docs/services/speech-to-text/output.html#speaker_labels).
:param dict headers: A `dict` containing the request headers
:return:
"""
if audio is None:
raise ValueError('Audio must be provided')
if recognize_callback is None:
raise ValueError('Recognize callback must be provided')
if not isinstance(recognize_callback, RecognizeCallback):
raise Exception(
'Callback is not a derived class of RecognizeCallback')
headers = {}
if self.default_headers is not None:
headers = self.default_headers.copy()
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
authstring = "{0}:{1}".format(self.username, self.password)
base64_authorization = base64.b64encode(authstring.encode('utf-8')).decode('utf-8')
headers['Authorization'] = 'Basic {0}'.format(base64_authorization)
url = self.url.replace('https:', 'wss:')
params = {
'model': model,
'customization_id': customization_id,
'acoustic_customization_id': acoustic_customization_id,
'customization_weight': customization_weight,
'version': version
}
params = _remove_null_values(params)
url = url + '/v1/recognize?{0}'.format(urlencode(params))
options = {
'content_type': content_type,
'inactivity_timeout': inactivity_timeout,
'interim_results': interim_results,
'keywords': 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
}
options = _remove_null_values(options)
RecognizeListener(audio, options, recognize_callback, url, headers)
#########################
# Asynchronous
#########################
[docs] def check_job(self, id, **kwargs):
"""
Check a job.
Returns information about the specified job. The response always includes the
status of the job and its creation and update times. If the status is `completed`,
the response includes the results of the recognition request. You must submit the
request with the service credentials of the user who created the job. You can
use the method to retrieve the results of any job, regardless of whether it was
submitted with a callback URL and the `recognitions.completed_with_results` event,
and you can retrieve the results multiple times for as long as they remain
available. Use the **Check jobs** method to request information about the most
recent jobs associated with the calling user.
:param str id: The ID of the asynchronous job.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `RecognitionJob` response.
:rtype: dict
"""
if id is None:
raise ValueError('id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/recognitions/{0}'.format(*self._encode_path_vars(id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def check_jobs(self, **kwargs):
"""
Check jobs.
Returns the ID and status of the latest 100 outstanding jobs associated with the
service credentials with which it is called. The method also returns the creation
and update times of each job, and, if a job was created with a callback URL and a
user token, the user token for the job. To obtain the results for a job whose
status is `completed` or not one of the latest 100 outstanding jobs, use the
**Check a job** method. A job and its results remain available until you delete
them with the **Delete a job** method or until the job's time to live expires,
whichever comes first.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `RecognitionJobs` response.
:rtype: dict
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/recognitions'
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def create_job(self,
audio,
content_type,
model=None,
callback_url=None,
events=None,
user_token=None,
results_ttl=None,
customization_id=None,
acoustic_customization_id=None,
customization_weight=None,
version=None,
inactivity_timeout=None,
keywords=None,
keywords_threshold=None,
max_alternatives=None,
word_alternatives_threshold=None,
word_confidence=None,
timestamps=None,
profanity_filter=None,
smart_formatting=None,
speaker_labels=None,
**kwargs):
"""
Create a job.
Creates a job for a new asynchronous recognition request. The job is owned by the
user whose service credentials are used to create it. How you learn the status and
results of a job depends on the parameters you include with the job creation
request: * By callback notification: Include the `callback_url` parameter to
specify a URL to which the service is to send callback notifications when the
status of the job changes. Optionally, you can also include the `events` and
`user_token` parameters to subscribe to specific events and to specify a string
that is to be included with each notification for the job. * By polling the
service: Omit the `callback_url`, `events`, and `user_token` parameters. You must
then use the **Check jobs** or **Check a job** methods to check the status of the
job, using the latter to retrieve the results when the job is complete. The two
approaches are not mutually exclusive. You can poll the service for job status or
obtain results from the service manually even if you include a callback URL. In
both cases, you can include the `results_ttl` parameter to specify how long the
results are to remain available after the job is complete. For detailed usage
information about the two approaches, including callback notifications, see
[Creating a
job](https://console.bluemix.net/docs/services/speech-to-text/async.html#create).
Using the HTTPS **Check a job** method to retrieve results is more secure than
receiving them via callback notification over HTTP because it provides
confidentiality in addition to authentication and data integrity. The method
supports the same basic parameters as other HTTP and WebSocket recognition
requests. The service imposes a data size limit of 100 MB. It automatically
detects the endianness of the incoming audio and, for audio that includes multiple
channels, downmixes the audio to one-channel mono during transcoding. (For the
`audio/l16` format, you can specify the endianness.) ### Audio formats (content
types) Use the `Content-Type` parameter to specify the audio format (MIME type)
of the audio: * `audio/basic` (Use only with narrowband models.) * `audio/flac` *
`audio/l16` (Specify the sampling rate (`rate`) and optionally the number of
channels (`channels`) and endianness (`endianness`) of the audio.) * `audio/mp3` *
`audio/mpeg` * `audio/mulaw` (Specify the sampling rate (`rate`) of the audio.) *
`audio/ogg` (The service automatically detects the codec of the input audio.) *
`audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav` (Provide audio
with a maximum of nine channels.) * `audio/webm` (The service automatically
detects the codec of the input audio.) * `audio/webm;codecs=opus` *
`audio/webm;codecs=vorbis` For information about the supported audio formats,
including specifying the sampling rate, channels, and endianness for the indicated
formats, see [Audio
formats](https://console.bluemix.net/docs/services/speech-to-text/audio-formats.html).
:param str audio: The audio to transcribe in the format specified by the `Content-Type` header.
:param str content_type: The type of the input: audio/basic, audio/flac, audio/l16, audio/mp3, audio/mpeg, audio/mulaw, audio/ogg, audio/ogg;codecs=opus, audio/ogg;codecs=vorbis, audio/wav, audio/webm, audio/webm;codecs=opus, or audio/webm;codecs=vorbis.
:param str model: The identifier of the model that is to be used for the recognition request or, for the **Create a session** method, with the new session.
:param str callback_url: A URL to which callback notifications are to be sent. The URL must already be successfully white-listed by using the **Register a callback** method. Omit the parameter to poll the service for job completion and results. You can include the same callback URL with any number of job creation requests. Use the `user_token` parameter to specify a unique user-specified string with each job to differentiate the callback notifications for the jobs.
:param str events: If the job includes a callback URL, a comma-separated list of notification events to which to subscribe. Valid events are: `recognitions.started` generates a callback notification when the service begins to process the job. `recognitions.completed` generates a callback notification when the job is complete; you must use the **Check a job** method to retrieve the results before they time out or are deleted. `recognitions.completed_with_results` generates a callback notification when the job is complete; the notification includes the results of the request. `recognitions.failed` generates a callback notification if the service experiences an error while processing the job. Omit the parameter to subscribe to the default events: `recognitions.started`, `recognitions.completed`, and `recognitions.failed`. The `recognitions.completed` and `recognitions.completed_with_results` events are incompatible; you can specify only of the two events. If the job does not include a callback URL, omit the parameter.
:param str user_token: If the job includes a callback URL, a user-specified string that the service is to include with each callback notification for the job; the token allows the user to maintain an internal mapping between jobs and notification events. If the job does not include a callback URL, omit the parameter.
:param int results_ttl: The number of minutes for which the results are to be available after the job has finished. If not delivered via a callback, the results must be retrieved within this time. Omit the parameter to use a time to live of one week. The parameter is valid with or without a callback URL.
:param str customization_id: The customization ID (GUID) of a custom language model that is to be used with the recognition request or, for the **Create a session** method, with the new session. The base model of the specified custom language model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom language model is used.
:param str acoustic_customization_id: The customization ID (GUID) of a custom acoustic model that is to be used with the recognition request or, for the **Create a session** method, with the new session. The base model of the specified custom acoustic model must match the model specified with the `model` parameter. You must make the request with service credentials created for the instance of the service that owns the custom model. By default, no custom acoustic model is used.
:param str version: The version of the specified base model that is to be used with recognition request or, for the **Create a session** method, with the new session. 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. For more information, see [Base model version](https://console.bluemix.net/docs/services/speech-to-text/input.html#version).
:param float customization_weight: If you specify the customization ID (GUID) of a custom language model with the recognition request or, for sessions, with the **Create a session** method, 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.
:param int inactivity_timeout: The time in seconds after which, if only silence (no speech) is detected in submitted audio, the connection is closed with a 400 error. Useful for stopping audio submission from a live microphone when a user simply walks away. Use `-1` for infinity.
:param list[str] keywords: An array of keyword strings to spot in the audio. Each keyword string can include one or more tokens. Keywords are spotted only in the final hypothesis, not in interim results. If you specify any keywords, you must also specify a keywords threshold. You can spot a maximum of 1000 keywords. Omit the parameter or specify an empty array if you do not need to spot keywords.
:param float keywords_threshold: A confidence value that is the lower bound for spotting a keyword. A word is considered to match a keyword if its confidence is greater than or equal to the threshold. Specify a probability between 0 and 1 inclusive. No keyword spotting is performed if you omit the parameter. If you specify a threshold, you must also specify one or more keywords.
:param int max_alternatives: The maximum number of alternative transcripts to be returned. By default, a single transcription is returned.
:param float word_alternatives_threshold: A confidence value that is the lower bound for identifying a hypothesis as a possible word alternative (also known as \"Confusion Networks\"). An alternative word is considered if its confidence is greater than or equal to the threshold. Specify a probability between 0 and 1 inclusive. No alternative words are computed if you omit the parameter.
:param bool word_confidence: If `true`, a confidence measure in the range of 0 to 1 is returned for each word. By default, no word confidence measures are returned.
:param bool timestamps: If `true`, time alignment is returned for each word. By default, no timestamps are returned.
:param bool profanity_filter: If `true` (the default), filters profanity from all output except for keyword results by replacing inappropriate words with a series of asterisks. Set the parameter to `false` to return results with no censoring. Applies to US English transcription only.
:param bool smart_formatting: If `true`, 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, also converts certain keyword strings to punctuation symbols. By default, no smart formatting is performed. Applies to US English and Spanish transcription only.
:param bool speaker_labels: If `true`, the response includes labels that identify which words were spoken by which participants in a multi-person exchange. By default, no speaker labels are returned. Setting `speaker_labels` to `true` forces the `timestamps` parameter to be `true`, regardless of whether you specify `false` for the parameter. To determine whether a language model supports speaker labels, use the **Get models** method and check that the attribute `speaker_labels` is set to `true`. You can also refer to [Speaker labels](https://console.bluemix.net/docs/services/speech-to-text/output.html#speaker_labels).
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `RecognitionJob` response.
:rtype: dict
"""
if audio is None:
raise ValueError('audio must be provided')
if content_type is None:
raise ValueError('content_type must be provided')
headers = {'Content-Type': content_type}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {
'model': model,
'callback_url': callback_url,
'events': events,
'user_token': user_token,
'results_ttl': results_ttl,
'customization_id': customization_id,
'acoustic_customization_id': acoustic_customization_id,
'version': version,
'base_model_version': version,
'customization_weight': customization_weight,
'inactivity_timeout': inactivity_timeout,
'keywords': self._convert_list(keywords),
'keywords_threshold': keywords_threshold,
'max_alternatives': max_alternatives,
'word_alternatives_threshold': word_alternatives_threshold,
'word_confidence': word_confidence,
'timestamps': timestamps,
'profanity_filter': profanity_filter,
'smart_formatting': smart_formatting,
'speaker_labels': speaker_labels
}
data = audio
url = '/v1/recognitions'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return response
[docs] def delete_job(self, id, **kwargs):
"""
Delete a job.
Deletes the specified job. You cannot delete a job that the service is actively
processing. Once you delete a job, its results are no longer available. The
service automatically deletes a job and its results when the time to live for the
results expires. You must submit the request with the service credentials of the
user who created the job.
:param str id: The ID of the asynchronous job.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if id is None:
raise ValueError('id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/recognitions/{0}'.format(*self._encode_path_vars(id))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
[docs] def register_callback(self, callback_url, user_secret=None, **kwargs):
"""
Register a callback.
Registers a callback URL with the service for use with subsequent asynchronous
recognition requests. The service attempts to register, or white-list, the
callback URL if it is not already registered by sending a `GET` request to the
callback URL. The service passes a random alphanumeric challenge string via the
`challenge_string` parameter of the request. The request includes an `Accept`
header that specifies `text/plain` as the required response type. To be
registered successfully, the callback URL must respond to the `GET` request from
the service. The response must send status code 200 and must include the challenge
string in its body. Set the `Content-Type` response header to `text/plain`. Upon
receiving this response, the service responds to the original registration request
with response code 201. The service sends only a single `GET` request to the
callback URL. If the service does not receive a reply with a response code of 200
and a body that echoes the challenge string sent by the service within five
seconds, it does not white-list the URL; it instead sends status code 400 in
response to the **Register a callback** request. If the requested callback URL is
already white-listed, the service responds to the initial registration request
with response code 200. If you specify a user secret with the request, the
service uses it as a key to calculate an HMAC-SHA1 signature of the challenge
string in its response to the `POST` request. It sends this signature in the
`X-Callback-Signature` header of its `GET` request to the URL during registration.
It also uses the secret to calculate a signature over the payload of every
callback notification that uses the URL. The signature provides authentication and
data integrity for HTTP communications. After you successfully register a
callback URL, you can use it with an indefinite number of recognition requests.
You can register a maximum of 20 callback URLS in a one-hour span of time. For
more information, see [Registering a callback
URL](https://console.bluemix.net/docs/services/speech-to-text/async.html#register).
:param str callback_url: An HTTP or HTTPS URL to which callback notifications are to be sent. To be white-listed, the URL must successfully echo the challenge string during URL verification. During verification, the client can also check the signature that the service sends in the `X-Callback-Signature` header to verify the origin of the request.
:param str user_secret: A user-specified string that the service uses to generate the HMAC-SHA1 signature that it sends via the `X-Callback-Signature` header. The service includes the header during URL verification and with every notification sent to the callback URL. It calculates the signature over the payload of the notification. If you omit the parameter, the service does not send the header.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `RegisterStatus` response.
:rtype: dict
"""
if callback_url is None:
raise ValueError('callback_url must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'callback_url': callback_url, 'user_secret': user_secret}
url = '/v1/register_callback'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[docs] def unregister_callback(self, callback_url, **kwargs):
"""
Unregister a callback.
Unregisters a callback URL that was previously white-listed with a **Register a
callback** request for use with the asynchronous interface. Once unregistered, the
URL can no longer be used with asynchronous recognition requests.
:param str callback_url: The callback URL that is to be unregistered.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if callback_url is None:
raise ValueError('callback_url must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'callback_url': callback_url}
url = '/v1/unregister_callback'
self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return None
#########################
# Custom language models
#########################
[docs] def create_language_model(self,
name,
base_model_name,
dialect=None,
description=None,
**kwargs):
"""
Create a custom language model.
Creates a new custom language model for a specified base model. The custom
language model can be used only with the base model for which it is created. The
model is owned by the instance of the service whose credentials are used to create
it. You must pass a value of `application/json` with the `Content-Type` header.
: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, request information about the base model and check that the attribute `custom_language_model` is set to `true`, or refer to [Language support for customization](https://console.bluemix.net/docs/services/speech-to-text/custom.html#languageSupport).
:param str dialect: The dialect of the specified language that is to be used with the custom language model. The parameter is meaningful only for Spanish models, for which the service creates a custom language model that is suited for speech in one of the following dialects: * `es-ES` for Castilian Spanish (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish A specified dialect must be valid for the base model. By default, the dialect matches the language of the base model; for example, `en-US` for either of the US English language models.
:param str description: A description of the new custom language model. Use a localized description that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `LanguageModel` response.
:rtype: dict
"""
if name is None:
raise ValueError('name must be provided')
if base_model_name is None:
raise ValueError('base_model_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
data = {
'name': name,
'base_model_name': base_model_name,
'dialect': dialect,
'description': description
}
url = '/v1/customizations'
response = self.request(
method='POST',
url=url,
headers=headers,
json=data,
accept_json=True)
return response
@deprecated('Use create_language_model() instead.')
def create_custom_model(self,
name,
description="",
base_model="en-US_BroadbandModel"):
return self.create_language_model(
name, base_model, description=description)
[docs] def delete_language_model(self, customization_id, **kwargs):
"""
Delete a custom language model.
Deletes an existing custom language model. The custom model cannot be deleted if
another request, such as adding a corpus to the model, is currently being
processed. You must use credentials for the instance of the service that owns a
model to delete it.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}'.format(
*self._encode_path_vars(customization_id))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
@deprecated('Use delete_language_model() instead.')
def delete_custom_model(self, modelid):
return self.delete_language_model(modelid)
[docs] def get_language_model(self, customization_id, **kwargs):
"""
List a custom language model.
Lists 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.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `LanguageModel` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
@deprecated('Use get_language_model() instead.')
def get_custom_model(self, modelid):
return self.get_language_model(modelid)
[docs] def list_language_models(self, language=None, **kwargs):
"""
List custom language models.
Lists information about all custom language models that are owned by an instance
of the service. Use the `language` parameter to see all custom language models for
the specified language; omit the parameter to see all custom language models for
all languages. You must use credentials for the instance of the service that owns
a model to list information about it.
:param str language: The identifier of the language for which custom language or custom acoustic models are to be returned (for example, `en-US`). Omit the parameter to see all custom language or custom acoustic models owned by the requesting service credentials.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `LanguageModels` response.
:rtype: dict
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'language': language}
url = '/v1/customizations'
response = self.request(
method='GET',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
@deprecated('Use list_language_models() instead.')
def list_custom_models(self):
return self.list_language_models()
[docs] def reset_language_model(self, customization_id, **kwargs):
"""
Reset a custom language model.
Resets a custom language model by removing all corpora and words from the model.
Resetting a custom language model initializes the model to its state when it was
first created. Metadata such as the name and language of the model are preserved,
but the model's words resource is removed and must be re-created. You must use
credentials for the instance of the service that owns a model to reset it.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/reset'.format(
*self._encode_path_vars(customization_id))
self.request(method='POST', url=url, headers=headers, accept_json=True)
return None
[docs] def train_language_model(self,
customization_id,
word_type_to_add=None,
customization_weight=None,
**kwargs):
"""
Train a custom language model.
Initiates the training of a custom language model with new corpora, custom words,
or both. After adding, modifying, or deleting corpora or words for a custom
language model, use this method to begin the actual training of the model on the
latest data. You can specify whether the custom language model is to be trained
with all words from its words resource or only with words that were added or
modified by the user. You must use credentials for the instance of the service
that owns a model to train it. The training method is asynchronous. It can take
on the order of minutes to complete depending on the amount of data on which the
service is being trained and the current load on the service. The method returns
an HTTP 200 response code to indicate that the training process has begun. You
can monitor the status of the training by using the **List a custom language
model** method to poll the model's status. Use a loop to check the status every 10
seconds. The method returns a `Customization` object that includes `status` and
`progress` fields. A status of `available` means that the custom model is trained
and ready to use. The service cannot accept subsequent training requests, or
requests to add new corpora or words, until the existing request completes.
Training can fail to start for the following reasons: * The service is currently
handling another request for the custom model, such as another training request or
a request to add a corpus or words to the model. * No training data (corpora or
words) have been added to the custom model. * One or more words that were added to
the custom model have invalid sounds-like pronunciations that you must fix.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str word_type_to_add: The type of words from the custom language model's words resource on which to train the model: * `all` (the default) trains the model on all new words, regardless of whether they were extracted from corpora or were added or modified by the user. * `user` trains the model only on new words that were added or modified by the user; the model is not trained on new words extracted from corpora.
:param float customization_weight: Specifies a customization weight for the custom language model. The customization weight tells the service how much weight to give to words from the custom language model compared to those from the base model for speech recognition. Specify a value between 0.0 and 1.0; the default is 0.3. The default value yields the best performance in general. Assign a higher value if your audio makes frequent use of OOV words from the custom model. Use caution when setting the weight: a higher value can improve the accuracy of phrases from the custom model's domain, but it can negatively affect performance on non-domain phrases. The value that you assign is used for all recognition requests that use the model. You can override it for any recognition request by specifying a customization weight for that request.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {
'word_type_to_add': word_type_to_add,
'customization_weight': customization_weight
}
url = '/v1/customizations/{0}/train'.format(
*self._encode_path_vars(customization_id))
self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return None
@deprecated('Use train_language_model() instead.')
def train_custom_model(self,
customization_id,
customization_weight=None,
word_type=None):
self.train_language_model(customization_id, word_type,
customization_weight)
[docs] def upgrade_language_model(self, customization_id, **kwargs):
"""
Upgrade a custom language model.
Initiates the upgrade of a custom language model to the latest version of its base
language model. The upgrade method is asynchronous. It can take on the order of
minutes to complete depending on the amount of data in the custom model and the
current load on the service. A custom model must be in the `ready` or `available`
state to be upgraded. You must use credentials for the instance of the service
that owns a model to upgrade it. The method returns an HTTP 200 response code to
indicate that the upgrade process has begun successfully. You can monitor the
status of the upgrade by using the **List a custom language model** method to poll
the model's status. Use a loop to check the status every 10 seconds. While it is
being upgraded, the custom model has the status `upgrading`. When the upgrade is
complete, the model resumes the status that it had prior to upgrade. The service
cannot accept subsequent requests for the model until the upgrade completes. For
more information, see [Upgrading custom
models](https://console.bluemix.net/docs/services/speech-to-text/custom-upgrade.html).
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/upgrade_model'.format(
*self._encode_path_vars(customization_id))
self.request(method='POST', url=url, headers=headers, accept_json=True)
return None
#########################
# Custom corpora
#########################
[docs] def add_corpus(self,
customization_id,
corpus_name,
corpus_file,
allow_overwrite=None,
corpus_file_content_type=None,
corpus_filename=None,
**kwargs):
"""
Add a corpus.
Adds a single corpus text file of new training data to a custom language model.
Use multiple requests to submit multiple corpus text files. You must use
credentials for the instance of the service that owns a model to add a corpus to
it. Adding a corpus does not affect the custom language model until you train the
model for the new data by using the **Train a custom language model** method.
Submit a plain text file that contains sample sentences from the domain of
interest to enable the service to extract words in context. The more sentences you
add that represent the context in which speakers use words from the domain, the
better the service's recognition accuracy. For guidelines about adding a corpus
text file and for information about how the service parses a corpus file, see
[Preparing a corpus text
file](https://console.bluemix.net/docs/services/speech-to-text/language-resource.html#prepareCorpus).
The call returns an HTTP 201 response code if the corpus is valid. The service
then asynchronously processes the contents of the corpus and automatically
extracts new words that it finds. This can take on the order of a minute or two to
complete depending on the total number of words and the number of new words in the
corpus, as well as the current load on the service. You cannot submit requests to
add additional corpora or words to the custom model, or to train the model, until
the service's analysis of the corpus for the current request completes. Use the
**List a corpus** method to check the status of the analysis. The service
auto-populates the model's words resource with any word that is not found in its
base vocabulary; these are referred to as out-of-vocabulary (OOV) words. You can
use the **List custom words** method to examine the words resource, using other
words method to eliminate typos and modify how words are pronounced as needed.
To add a corpus file that has the same name as an existing corpus, set the
`allow_overwrite` parameter to `true`; otherwise, the request fails. Overwriting
an existing corpus causes the service to process the corpus text file and extract
OOV words anew. Before doing so, it removes any OOV words associated with the
existing corpus from the model's words resource unless they were also added by
another corpus or they have been modified in some way with the **Add custom
words** or **Add a custom word** method. The service limits the overall amount
of data that you can add to a custom model to a maximum of 10 million total words
from all corpora combined. Also, you can add no more than 30 thousand custom (OOV)
words to a model; this includes words that the service extracts from corpora and
words that you add directly.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str corpus_name: The name of the corpus for the custom language model. When adding a corpus, do not include spaces in the name; use a localized name that matches the language of the custom model; and do not use the name `user`, which is reserved by the service to denote custom words added or modified by the user.
:param file corpus_file: A plain text file that contains the training data for the corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service assumes UTF-8 encoding if it encounters non-ASCII characters. With cURL, use the `--data-binary` option to upload the file for the request.
:param bool allow_overwrite: If `true`, the specified corpus or audio resource overwrites an existing corpus or audio resource with the same name. If `false` (the default), the request fails if a corpus or audio resource with the same name already exists. The parameter has no effect if a corpus or audio resource with the same name does not already exist.
:param str corpus_file_content_type: The content type of corpus_file.
:param str corpus_filename: The filename for corpus_file.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if corpus_name is None:
raise ValueError('corpus_name must be provided')
if corpus_file is None:
raise ValueError('corpus_file must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'allow_overwrite': allow_overwrite}
if not corpus_filename and hasattr(corpus_file, 'name'):
corpus_filename = corpus_file.name
mime_type = corpus_file_content_type or 'application/octet-stream'
corpus_file_tuple = (corpus_filename, corpus_file, mime_type)
url = '/v1/customizations/{0}/corpora/{1}'.format(
*self._encode_path_vars(customization_id, corpus_name))
self.request(
method='POST',
url=url,
headers=headers,
params=params,
files={'corpus_file': corpus_file_tuple},
accept_json=True)
return None
[docs] def delete_corpus(self, customization_id, corpus_name, **kwargs):
"""
Delete a corpus.
Deletes an existing corpus from a custom language model. The service removes any
out-of-vocabulary (OOV) words associated with the corpus from the custom model's
words resource unless they were also added by another corpus or they have been
modified in some way with the **Add custom words** or **Add a custom word**
method. Removing a corpus does not affect the custom model until you train the
model with the **Train a custom language model** method. You must use credentials
for the instance of the service that owns a model to delete its corpora.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str corpus_name: The name of the corpus for the custom language model. When adding a corpus, do not include spaces in the name; use a localized name that matches the language of the custom model; and do not use the name `user`, which is reserved by the service to denote custom words added or modified by the user.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if corpus_name is None:
raise ValueError('corpus_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/corpora/{1}'.format(
*self._encode_path_vars(customization_id, corpus_name))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
[docs] def get_corpus(self, customization_id, corpus_name, **kwargs):
"""
List a corpus.
Lists information about a corpus from a custom language model. The information
includes the total number of words and out-of-vocabulary (OOV) words, name, and
status of the corpus. You must use credentials for the instance of the service
that owns a model to list its corpora.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str corpus_name: The name of the corpus for the custom language model. When adding a corpus, do not include spaces in the name; use a localized name that matches the language of the custom model; and do not use the name `user`, which is reserved by the service to denote custom words added or modified by the user.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `Corpus` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if corpus_name is None:
raise ValueError('corpus_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/corpora/{1}'.format(
*self._encode_path_vars(customization_id, corpus_name))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def list_corpora(self, customization_id, **kwargs):
"""
List corpora.
Lists information about all corpora from a custom language model. The information
includes the total number of words and out-of-vocabulary (OOV) words, name, and
status of each corpus. You must use credentials for the instance of the service
that owns a model to list its corpora.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `Corpora` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/corpora'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
#########################
# Custom words
#########################
[docs] def add_word(self,
customization_id,
word_name,
sounds_like=None,
display_as=None,
**kwargs):
"""
Add a custom word.
Adds a custom word to a custom language model. The service populates the words
resource for a custom model with out-of-vocabulary (OOV) words found in each
corpus added to the model. You can use this method to add a word or to modify an
existing word in the words resource. The words resource for a model can contain a
maximum of 30 thousand custom (OOV) words, including words that the service
extracts from corpora and words that you add directly. You must use credentials
for the instance of the service that owns a model to add or modify a custom word
for the model. You must pass a value of `application/json` with the `Content-Type`
header. Adding or modifying a custom word does not affect the custom model until
you train the model for the new data by using the **Train a custom language
model** method. Use the `word_name` parameter to specify the custom word that is
to be added or modified. Use the `CustomWord` object to provide one or both of the
optional `sounds_like` and `display_as` fields for the word. * The `sounds_like`
field provides an array of one or more pronunciations for the word. Use the
parameter to specify how the word can be pronounced by users. Use the parameter
for words that are difficult to pronounce, foreign words, acronyms, and so on. For
example, you might specify that the word `IEEE` can sound like `i triple e`. You
can specify a maximum of five sounds-like pronunciations for a word. For
information about pronunciation rules, see [Using the sounds_like
field](https://console.bluemix.net/docs/services/speech-to-text/language-resource.html#soundsLike).
* The `display_as` field provides a different way of spelling the word in a
transcript. Use the parameter when you want the word to appear different from its
usual representation or from its spelling in corpora training data. For example,
you might indicate that the word `IBM(trademark)` is to be displayed as `IBM`. For
more information, see [Using the display_as
field](https://console.bluemix.net/docs/services/speech-to-text/language-resource.html#displayAs).
If you add a custom word that already exists in the words resource for the
custom model, the new definition overwrites the existing data for the word. If the
service encounters an error, it does not add the word to the words resource. Use
the **List a custom word** method to review the word that you add.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str word_name: The custom word for the custom language model. When you add or update a custom word with the **Add a custom word** method, do not include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of compound words.
:param list[str] sounds_like: An array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. For a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations, and a pronunciation can include at most 40 characters not including spaces.
:param str display_as: An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
data = {
'word': word_name,
'sounds_like': sounds_like,
'display_as': display_as
}
url = '/v1/customizations/{0}/words/{1}'.format(
*self._encode_path_vars(customization_id, word_name))
self.request(
method='PUT', url=url, headers=headers, json=data, accept_json=True)
return None
@deprecated('Use add_word instead.')
def add_custom_word(self, customization_id, custom_word):
return self.add_word(customization_id, custom_word)
[docs] def add_words(self, customization_id, words, **kwargs):
"""
Add custom words.
Adds one or more custom words to a custom language model. The service populates
the words resource for a custom model with out-of-vocabulary (OOV) words found in
each corpus added to the model. You can use this method to add additional words or
to modify existing words in the words resource. The words resource for a model can
contain a maximum of 30 thousand custom (OOV) words, including words that the
service extracts from corpora and words that you add directly. You must use
credentials for the instance of the service that owns a model to add or modify
custom words for the model. You must pass a value of `application/json` with the
`Content-Type` header. Adding or modifying custom words does not affect the custom
model until you train the model for the new data by using the **Train a custom
language model** method. You add custom words by providing a `Words` object,
which is an array of `Word` objects, one per word. You must use the object's word
parameter to identify the word that is to be added. You can also provide one or
both of the optional `sounds_like` and `display_as` fields for each word. * The
`sounds_like` field provides an array of one or more pronunciations for the word.
Use the parameter to specify how the word can be pronounced by users. Use the
parameter for words that are difficult to pronounce, foreign words, acronyms, and
so on. For example, you might specify that the word `IEEE` can sound like `i
triple e`. You can specify a maximum of five sounds-like pronunciations for a
word. For information about pronunciation rules, see [Using the sounds_like
field](https://console.bluemix.net/docs/services/speech-to-text/language-resource.html#soundsLike).
* The `display_as` field provides a different way of spelling the word in a
transcript. Use the parameter when you want the word to appear different from its
usual representation or from its spelling in corpora training data. For example,
you might indicate that the word `IBM(trademark)` is to be displayed as `IBM`. For
more information, see [Using the display_as
field](https://console.bluemix.net/docs/services/speech-to-text/language-resource.html#displayAs).
If you add a custom word that already exists in the words resource for the
custom model, the new definition overwrites the existing data for the word. If the
service encounters an error with the input data, it returns a failure code and
does not add any of the words to the words resource. The call returns an HTTP
201 response code if the input data is valid. It then asynchronously processes the
words to add them to the model's words resource. The time that it takes for the
analysis to complete depends on the number of new words that you add but is
generally faster than adding a corpus or training a model. You can monitor the
status of the request by using the **List a custom language model** method to poll
the model's status. Use a loop to check the status every 10 seconds. The method
returns a `Customization` object that includes a `status` field. A status of
`ready` means that the words have been added to the custom model. The service
cannot accept requests to add new corpora or words or to train the model until the
existing request completes. You can use the **List custom words** or **List a
custom word** method to review the words that you add. Words with an invalid
`sounds_like` field include an `error` field that describes the problem. You can
use other words-related methods to correct errors, eliminate typos, and modify how
words are pronounced as needed.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param list[CustomWord] words: An array of objects that provides information about each custom word that is to be added to or updated in the custom language model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if words is None:
raise ValueError('words must be provided')
words = [self._convert_model(x, CustomWord) for x in words]
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
data = {'words': words}
url = '/v1/customizations/{0}/words'.format(
*self._encode_path_vars(customization_id))
self.request(
method='POST',
url=url,
headers=headers,
json=data,
accept_json=True)
return None
@deprecated('Use add_words() instead.')
def add_custom_words(self, customization_id, custom_words):
return self.add_words(customization_id, custom_words)
[docs] def delete_word(self, customization_id, word_name, **kwargs):
"""
Delete a custom word.
Deletes a custom word from a custom language model. You can remove any word that
you added to the custom model's words resource via any means. However, if the word
also exists in the service's base vocabulary, the service removes only the custom
pronunciation for the word; the word remains in the base vocabulary. Removing a
custom word does not affect the custom model until you train the model with the
**Train a custom language model** method. You must use credentials for the
instance of the service that owns a model to delete its words.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str word_name: The custom word for the custom language model. When you add or update a custom word with the **Add a custom word** method, do not include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of compound words.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/words/{1}'.format(
*self._encode_path_vars(customization_id, word_name))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
@deprecated('Use delete_word() instead.')
def delete_custom_word(self, customization_id, custom_word):
return self.delete_word(customization_id, custom_word)
[docs] def get_word(self, customization_id, word_name, **kwargs):
"""
List a custom word.
Lists information about a custom word from a custom language model. You must use
credentials for the instance of the service that owns a model to query information
about its words.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str word_name: The custom word for the custom language model. When you add or update a custom word with the **Add a custom word** method, do not include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of compound words.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `Word` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/customizations/{0}/words/{1}'.format(
*self._encode_path_vars(customization_id, word_name))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
@deprecated('Use get_word() instead.')
def get_custom_word(self, customization_id, custom_word):
return self.get_word(customization_id, custom_word)
[docs] def list_words(self, customization_id, word_type=None, sort=None, **kwargs):
"""
List custom words.
Lists information about custom words from a custom language model. You can list
all words from the custom model's words resource, only custom words that were
added or modified by the user, or only out-of-vocabulary (OOV) words that were
extracted from corpora. You can also indicate the order in which the service is to
return words; by default, words are listed in ascending alphabetical order. You
must use credentials for the instance of the service that owns a model to query
information about its words.
:param str customization_id: The customization ID (GUID) of the custom language model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str word_type: The type of words to be listed from the custom language model's words resource: * `all` (the default) shows all words. * `user` shows only custom words that were added or modified by the user. * `corpora` shows only OOV that were extracted from corpora.
:param str sort: Indicates the order in which the words are to be listed, `alphabetical` or by `count`. You can prepend an optional `+` or `-` to an argument to indicate whether the results are to be sorted in ascending or descending order. By default, words are sorted in ascending alphabetical order. For alphabetical ordering, the lexicographical precedence is numeric values, uppercase letters, and lowercase letters. For count ordering, values with the same count are ordered alphabetically. With cURL, URL encode the `+` symbol as `%2B`.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `Words` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'word_type': word_type, 'sort': sort}
url = '/v1/customizations/{0}/words'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
@deprecated('Use list_words() instead.')
def list_custom_words(self, customization_id, word_type=None, sort=None):
return self.list_words(customization_id, word_type, sort)
#########################
# Custom acoustic models
#########################
[docs] def create_acoustic_model(self,
name,
base_model_name,
description=None,
**kwargs):
"""
Create a custom acoustic model.
Creates a new custom acoustic model for a specified base model. The custom
acoustic model can be used only with the base model for which it is created. The
model is owned by the instance of the service whose credentials are used to create
it. You must pass a value of `application/json` with the `Content-Type` header.
:param str name: A user-defined name for the new custom acoustic model. Use a name that is unique among all custom acoustic models that you own. Use a localized name that matches the language of the custom model. Use a name that describes the acoustic environment of the custom model, such as `Mobile custom model` or `Noisy car custom model`.
:param str base_model_name: The name of the base language model that is to be customized by the new custom acoustic model. The new custom model can be used only with the base model that it customizes. To determine whether a base model supports acoustic model customization, refer to [Language support for customization](https://console.bluemix.net/docs/services/speech-to-text/custom.html#languageSupport).
:param str description: A description of the new custom acoustic model. Use a localized description that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `AcousticModel` response.
:rtype: dict
"""
if name is None:
raise ValueError('name must be provided')
if base_model_name is None:
raise ValueError('base_model_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
data = {
'name': name,
'base_model_name': base_model_name,
'description': description
}
url = '/v1/acoustic_customizations'
response = self.request(
method='POST',
url=url,
headers=headers,
json=data,
accept_json=True)
return response
[docs] def delete_acoustic_model(self, customization_id, **kwargs):
"""
Delete a custom acoustic model.
Deletes an existing custom acoustic model. The custom model cannot be deleted if
another request, such as adding an audio resource to the model, is currently being
processed. You must use credentials for the instance of the service that owns a
model to delete it.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}'.format(
*self._encode_path_vars(customization_id))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
[docs] def get_acoustic_model(self, customization_id, **kwargs):
"""
List a custom acoustic model.
Lists 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.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `AcousticModel` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def list_acoustic_models(self, language=None, **kwargs):
"""
List custom acoustic models.
Lists information about all custom acoustic models that are owned by an instance
of the service. Use the `language` parameter to see all custom acoustic models for
the specified language; omit the parameter to see all custom acoustic models for
all languages. You must use credentials for the instance of the service that owns
a model to list information about it.
:param str language: The identifier of the language for which custom language or custom acoustic models are to be returned (for example, `en-US`). Omit the parameter to see all custom language or custom acoustic models owned by the requesting service credentials.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `AcousticModels` response.
:rtype: dict
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'language': language}
url = '/v1/acoustic_customizations'
response = self.request(
method='GET',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[docs] def reset_acoustic_model(self, customization_id, **kwargs):
"""
Reset a custom acoustic model.
Resets a custom acoustic model by removing all audio resources from the model.
Resetting a custom acoustic model initializes the model to its state when it was
first created. Metadata such as the name and language of the model are preserved,
but the model's audio resources are removed and must be re-created. You must use
credentials for the instance of the service that owns a model to reset it.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}/reset'.format(
*self._encode_path_vars(customization_id))
self.request(method='POST', url=url, headers=headers, accept_json=True)
return None
[docs] def train_acoustic_model(self,
customization_id,
custom_language_model_id=None,
**kwargs):
"""
Train a custom acoustic model.
Initiates the training of a custom acoustic model with new or changed audio
resources. After adding or deleting audio resources for a custom acoustic model,
use this method to begin the actual training of the model on the latest audio
data. The custom acoustic model does not reflect its changed data until you train
it. You must use credentials for the instance of the service that owns a model to
train it. The training method is asynchronous. It can take on the order of
minutes or hours to complete depending on the total amount of audio data on which
the custom acoustic model is being trained and the current load on the service.
Typically, training a custom acoustic model takes approximately two to four times
the length of its audio data. The range of time depends on the model being trained
and the nature of the audio, such as whether the audio is clean or noisy. The
method returns an HTTP 200 response code to indicate that the training process has
begun. You can monitor the status of the training by using the **List a custom
acoustic model** method to poll the model's status. Use a loop to check the status
once a minute. The method returns an `Customization` object that includes `status`
and `progress` fields. A status of `available` indicates that the custom model is
trained and ready to use. The service cannot accept subsequent training requests,
or requests to add new audio resources, until the existing request completes.
You can use the optional `custom_language_model_id` parameter to specify the GUID
of a separately created custom language model that is to be used during training.
Specify a custom language model if you have verbatim transcriptions of the audio
files that you have added to the custom model or you have either corpora (text
files) or a list of words that are relevant to the contents of the audio files.
For information about creating a separate custom language model, see [Creating a
custom language
model](https://console.bluemix.net/docs/services/speech-to-text/language-create.html).
Training can fail to start for the following reasons: * The service is currently
handling another request for the custom model, such as another training request or
a request to add audio resources to the model. * The custom model contains less
than 10 minutes or more than 50 hours of audio data. * One or more of the custom
model's audio resources is invalid.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str custom_language_model_id: The customization ID (GUID) of a custom language model that is to be used during training of the custom acoustic model. Specify a custom language model that has been trained with verbatim transcriptions of the audio resources or that contains words that are relevant to the contents of the audio resources.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'custom_language_model_id': custom_language_model_id}
url = '/v1/acoustic_customizations/{0}/train'.format(
*self._encode_path_vars(customization_id))
self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return None
[docs] def upgrade_acoustic_model(self,
customization_id,
custom_language_model_id=None,
**kwargs):
"""
Upgrade a custom acoustic model.
Initiates the upgrade of a custom acoustic model to the latest version of its base
language model. The upgrade method is asynchronous. It can take on the order of
minutes or hours to complete depending on the amount of data in the custom model
and the current load on the service; typically, upgrade takes approximately twice
the length of the total audio contained in the custom model. A custom model must
be in the `ready` or `available` state to be upgraded. You must use credentials
for the instance of the service that owns a model to upgrade it. The method
returns an HTTP 200 response code to indicate that the upgrade process has begun
successfully. You can monitor the status of the upgrade by using the **List a
custom acoustic model** method to poll the model's status. Use a loop to check the
status once a minute. While it is being upgraded, the custom model has the status
`upgrading`. When the upgrade is complete, the model resumes the status that it
had prior to upgrade. The service cannot accept subsequent requests for the model
until the upgrade completes. If the custom acoustic model was trained with a
separately created custom language model, you must use the
`custom_language_model_id` parameter to specify the GUID of that custom language
model. The custom language model must be upgraded before the custom acoustic model
can be upgraded. Omit the parameter if the custom acoustic model was not trained
with a custom language model. For more information, see [Upgrading custom
models](https://console.bluemix.net/docs/services/speech-to-text/custom-upgrade.html).
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str custom_language_model_id: If the custom acoustic model was trained with a custom language model, the customization ID (GUID) of that custom language model. The custom language model must be upgraded before the custom acoustic model can be upgraded.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'custom_language_model_id': custom_language_model_id}
url = '/v1/acoustic_customizations/{0}/upgrade_model'.format(
*self._encode_path_vars(customization_id))
self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return None
#########################
# Custom audio resources
#########################
[docs] def add_audio(self,
customization_id,
audio_name,
audio_resource,
content_type,
contained_content_type=None,
allow_overwrite=None,
**kwargs):
"""
Add an audio resource.
Adds an audio resource to a custom acoustic model. Add audio content that reflects
the acoustic characteristics of the audio that you plan to transcribe. You must
use credentials for the instance of the service that owns a model to add an audio
resource to it. Adding audio data does not affect the custom acoustic model until
you train the model for the new data by using the **Train a custom acoustic
model** method. You can add individual audio files or an archive file that
contains multiple audio files. Adding multiple audio files via a single archive
file is significantly more efficient than adding each file individually. You can
add audio resources in any format that the service supports for speech
recognition. You can use this method to add any number of audio resources to a
custom model by calling the method once for each audio or archive file. But the
addition of one audio resource must be fully complete before you can add another.
You must add a minimum of 10 minutes and a maximum of 50 hours of audio that
includes speech, not just silence, to a custom acoustic model before you can train
it. No audio resource, audio- or archive-type, can be larger than 100 MB. To add
an audio resource that has the same name as an existing audio resource, set the
`allow_overwrite` parameter to `true`; otherwise, the request fails. The method
is asynchronous. It can take several seconds to complete depending on the duration
of the audio and, in the case of an archive file, the total number of audio files
being processed. The service returns a 201 response code if the audio is valid. It
then asynchronously analyzes the contents of the audio file or files and
automatically extracts information about the audio such as its length, sampling
rate, and encoding. You cannot submit requests to add additional audio resources
to a custom acoustic model, or to train the model, until the service's analysis of
all audio files for the current request completes. To determine the status of
the service's analysis of the audio, use the **List an audio resource** method to
poll the status of the audio. The method accepts the GUID 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`.
### 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: * `audio/basic` (Use only with narrowband models.) *
`audio/flac` * `audio/l16` (Specify the sampling rate (`rate`) and optionally the
number of channels (`channels`) and endianness (`endianness`) of the audio.) *
`audio/mp3` * `audio/mpeg` * `audio/mulaw` (Specify the sampling rate (`rate`) of
the audio.) * `audio/ogg` (The service automatically detects the codec of the
input audio.) * `audio/ogg;codecs=opus` * `audio/ogg;codecs=vorbis` * `audio/wav`
(Provide audio with a maximum of nine channels.) * `audio/webm` (The service
automatically detects the codec of the input audio.) * `audio/webm;codecs=opus` *
`audio/webm;codecs=vorbis` For information about the supported audio formats,
including specifying the sampling rate, channels, and endianness for the indicated
formats, see [Audio
formats](https://console.bluemix.net/docs/services/speech-to-text/audio-formats.html).
**Note:** The sampling rate of an audio file must match the sampling rate of the
base model for the custom model: for broadband models, at least 16 kHz; for
narrowband models, at least 8 kHz. If the sampling rate of the audio is higher
than the minimum required rate, the service down-samples the audio to the
appropriate rate. If the sampling rate of the audio is lower than the minimum
required rate, the service labels the audio file as `invalid`. ### Content types
for archive-type resources You can add an archive file (**.zip** or **.tar.gz**
file) that contains audio files in any format that the service supports for speech
recognition. For an archive-type resource, use the `Content-Type` parameter to
specify the media type of the archive file: * `application/zip` for a **.zip**
file * `application/gzip` for a **.tar.gz** file. All audio files contained in
the archive must have the same audio format. Use the `Contained-Content-Type`
parameter to specify the format of the contained audio files. The parameter
accepts all of the audio formats supported for use with speech recognition and
with the `Content-Type` header, including the `rate`, `channels`, and `endianness`
parameters that are used with some formats. The default contained audio format is
`audio/wav`.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str audio_name: The name of the audio resource for the custom acoustic model. When adding an audio resource, do not include spaces in the name; use a localized name that matches the language of the custom model.
:param list[str] audio_resource: The audio resource that is to be added to the custom acoustic model, an individual audio file or an archive file.
:param str content_type: The type of the input: application/zip, application/gzip, audio/basic, audio/flac, audio/l16, audio/mp3, audio/mpeg, audio/mulaw, audio/ogg, audio/ogg;codecs=opus, audio/ogg;codecs=vorbis, audio/wav, audio/webm, audio/webm;codecs=opus, or audio/webm;codecs=vorbis.
:param str contained_content_type: For an archive-type resource, specifies the format of the audio files contained in the archive file. The parameter accepts all of the audio formats supported for use with speech recognition, including the `rate`, `channels`, and `endianness` parameters that are used with some formats. For a complete list of supported audio formats, see [Audio formats](/docs/services/speech-to-text/input.html#formats).
:param bool allow_overwrite: If `true`, the specified corpus or audio resource overwrites an existing corpus or audio resource with the same name. If `false` (the default), the request fails if a corpus or audio resource with the same name already exists. The parameter has no effect if a corpus or audio resource with the same name does not already exist.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
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')
if content_type is None:
raise ValueError('content_type must be provided')
headers = {
'Content-Type': content_type,
'Contained-Content-Type': contained_content_type
}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'allow_overwrite': allow_overwrite}
data = audio_resource
url = '/v1/acoustic_customizations/{0}/audio/{1}'.format(
*self._encode_path_vars(customization_id, audio_name))
self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return None
[docs] def delete_audio(self, customization_id, audio_name, **kwargs):
"""
Delete an audio resource.
Deletes an existing audio resource from a custom acoustic model. Deleting an
archive-type audio resource removes the entire archive of files; the current
interface does not allow deletion of individual files from an archive resource.
Removing an audio resource does not affect the custom model until you train the
model on its updated data by using the **Train a custom acoustic model** method.
You must use credentials for the instance of the service that owns a model to
delete its audio resources.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str audio_name: The name of the audio resource for the custom acoustic model. When adding an audio resource, do not include spaces in the name; use a localized name that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if audio_name is None:
raise ValueError('audio_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}/audio/{1}'.format(
*self._encode_path_vars(customization_id, audio_name))
self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return None
[docs] def get_audio(self, customization_id, audio_name, **kwargs):
"""
List an audio resource.
Lists 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
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, which is important for checking the service's analysis
of the resource in response to a request to add it to the custom model. You must
use credentials for the instance of the service that owns a model to list its
audio resources.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param str audio_name: The name of the audio resource for the custom acoustic model. When adding an audio resource, do not include spaces in the name; use a localized name that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `AudioListing` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if audio_name is None:
raise ValueError('audio_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}/audio/{1}'.format(
*self._encode_path_vars(customization_id, audio_name))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def list_audio(self, customization_id, **kwargs):
"""
List audio resources.
Lists information about all audio resources from a custom acoustic model. The
information includes the name of the resource and information about its audio
data, such as its duration. It also includes the status of the audio resource,
which is important for checking the service's analysis of the resource in response
to a request to add it to the custom acoustic model. You must use credentials for
the instance of the service that owns a model to list its audio resources.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. You must make the request with service credentials created for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `dict` containing the `AudioResources` response.
:rtype: dict
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
url = '/v1/acoustic_customizations/{0}/audio'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
#########################
# User data
#########################
[docs] def delete_user_data(self, customer_id, **kwargs):
"""
Delete labeled data.
Deletes all data that is associated with a specified customer ID. The method
deletes all data for the customer ID, regardless of the method by which the
information was added. The method has no effect if no data is associated with the
customer ID. You must issue the request with credentials for the same instance of
the service that was used to associate the customer ID with the data. You
associate a customer ID with data by passing the `X-Watson-Metadata` header with a
request that passes the data. For more information about customer IDs and about
using this method, see [Information
security](https://console.bluemix.net/docs/services/speech-to-text/information-security.html).
:param str customer_id: The customer ID for which all data is to be deleted.
:param dict headers: A `dict` containing the request headers
:rtype: None
"""
if customer_id is None:
raise ValueError('customer_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
params = {'customer_id': customer_id}
url = '/v1/user_data'
self.request(
method='DELETE',
url=url,
headers=headers,
params=params,
accept_json=True)
return None
##############################################################################
# Models
##############################################################################
[docs]class AcousticModel(object):
"""
AcousticModel.
:attr str customization_id: The customization ID (GUID) of the custom acoustic model. The **Create a custom acoustic model** method returns only this field of the object; it does not return the other fields.
:attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str language: (optional) The language identifier of the custom acoustic model (for example, `en-US`).
:attr list[str] versions: (optional) A list of the available versions of the custom acoustic model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
:attr str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom acoustic model.
:attr str name: (optional) The name of the custom acoustic model.
:attr str description: (optional) The description of the custom acoustic model.
:attr str base_model_name: (optional) The name of the language model for which the custom acoustic model was created.
:attr str status: (optional) The current status of the custom acoustic model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed.
:attr int progress: (optional) A percentage that indicates the progress of the custom acoustic model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete.
:attr str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter.
"""
def __init__(self,
customization_id,
created=None,
language=None,
versions=None,
owner=None,
name=None,
description=None,
base_model_name=None,
status=None,
progress=None,
warnings=None):
"""
Initialize a AcousticModel object.
:param str customization_id: The customization ID (GUID) of the custom acoustic model. The **Create a custom acoustic model** method returns only this field of the object; it does not return the other fields.
:param str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom acoustic model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:param str language: (optional) The language identifier of the custom acoustic model (for example, `en-US`).
:param list[str] versions: (optional) A list of the available versions of the custom acoustic model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
:param str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom acoustic model.
:param str name: (optional) The name of the custom acoustic model.
:param str description: (optional) The description of the custom acoustic model.
:param str base_model_name: (optional) The name of the language model for which the custom acoustic model was created.
:param str status: (optional) The current status of the custom acoustic model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed.
:param int progress: (optional) A percentage that indicates the progress of the custom acoustic model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete.
:param str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter.
"""
self.customization_id = customization_id
self.created = created
self.language = language
self.versions = versions
self.owner = owner
self.name = name
self.description = description
self.base_model_name = base_model_name
self.status = status
self.progress = progress
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AcousticModel object from a json dictionary."""
args = {}
if 'customization_id' in _dict:
args['customization_id'] = _dict.get('customization_id')
else:
raise ValueError(
'Required property \'customization_id\' not present in AcousticModel JSON'
)
if 'created' in _dict:
args['created'] = _dict.get('created')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'versions' in _dict:
args['versions'] = _dict.get('versions')
if 'owner' in _dict:
args['owner'] = _dict.get('owner')
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'base_model_name' in _dict:
args['base_model_name'] = _dict.get('base_model_name')
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'progress' in _dict:
args['progress'] = _dict.get('progress')
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'customization_id') and self.customization_id is not None:
_dict['customization_id'] = self.customization_id
if hasattr(self, 'created') and self.created is not None:
_dict['created'] = self.created
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'versions') and self.versions is not None:
_dict['versions'] = self.versions
if hasattr(self, 'owner') and self.owner is not None:
_dict['owner'] = self.owner
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self,
'base_model_name') and self.base_model_name is not None:
_dict['base_model_name'] = self.base_model_name
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'progress') and self.progress is not None:
_dict['progress'] = self.progress
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __str__(self):
"""Return a `str` version of this AcousticModel object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AcousticModels(object):
"""
AcousticModels.
:attr list[AcousticModel] customizations: An array of objects that provides information about each available custom acoustic model. The array is empty if the requesting service credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language.
"""
def __init__(self, customizations):
"""
Initialize a AcousticModels object.
:param list[AcousticModel] customizations: An array of objects that provides information about each available custom acoustic model. The array is empty if the requesting service credentials own no custom acoustic models (if no language is specified) or own no custom acoustic models for the specified language.
"""
self.customizations = customizations
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AcousticModels object from a json dictionary."""
args = {}
if 'customizations' in _dict:
args['customizations'] = [
AcousticModel._from_dict(x)
for x in (_dict.get('customizations'))
]
else:
raise ValueError(
'Required property \'customizations\' not present in AcousticModels JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'customizations') and self.customizations is not None:
_dict['customizations'] = [
x._to_dict() for x in self.customizations
]
return _dict
def __str__(self):
"""Return a `str` version of this AcousticModels object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AudioDetails(object):
"""
AudioDetails.
:attr str type: (optional) The type of the audio resource: * `audio` for an individual audio file * `archive` for an archive (**.zip** or **.tar.gz**) file that contains audio files.
:attr str codec: (optional) **For an audio-type resource,** the codec in which the audio is encoded. Omitted for an archive-type resource.
:attr int frequency: (optional) **For an audio-type resource,** the sampling rate of the audio in Hertz (samples per second). Omitted for an archive-type resource.
:attr str compression: (optional) **For an archive-type resource,** the format of the compressed archive: * `zip` for a **.zip** file * `gzip` for a **.tar.gz** file Omitted for an audio-type resource.
"""
def __init__(self, type=None, codec=None, frequency=None, compression=None):
"""
Initialize a AudioDetails object.
:param str type: (optional) The type of the audio resource: * `audio` for an individual audio file * `archive` for an archive (**.zip** or **.tar.gz**) file that contains audio files.
:param str codec: (optional) **For an audio-type resource,** the codec in which the audio is encoded. Omitted for an archive-type resource.
:param int frequency: (optional) **For an audio-type resource,** the sampling rate of the audio in Hertz (samples per second). Omitted for an archive-type resource.
:param str compression: (optional) **For an archive-type resource,** the format of the compressed archive: * `zip` for a **.zip** file * `gzip` for a **.tar.gz** file Omitted for an audio-type resource.
"""
self.type = type
self.codec = codec
self.frequency = frequency
self.compression = compression
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioDetails object from a json dictionary."""
args = {}
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'codec' in _dict:
args['codec'] = _dict.get('codec')
if 'frequency' in _dict:
args['frequency'] = _dict.get('frequency')
if 'compression' in _dict:
args['compression'] = _dict.get('compression')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'codec') and self.codec is not None:
_dict['codec'] = self.codec
if hasattr(self, 'frequency') and self.frequency is not None:
_dict['frequency'] = self.frequency
if hasattr(self, 'compression') and self.compression is not None:
_dict['compression'] = self.compression
return _dict
def __str__(self):
"""Return a `str` version of this AudioDetails object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AudioListing(object):
"""
AudioListing.
:attr float 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 name of the resource. Omitted for an archive-type resource.
:attr AudioDetails details: (optional) **For an audio-type resource,** an `AudioDetails` object that provides detailed information about the resource. The object is empty until the service finishes processing the audio. Omitted for an archive-type resource.
:attr str status: (optional) **For an audio-type resource,** the status of the resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that the service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid` indicates that the audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource.
:attr AudioResource container: (optional) **For an archive-type resource,** an object of type `AudioResource` that provides information about the resource. Omitted for an audio-type resource.
:attr list[AudioResource] audio: (optional) **For an archive-type resource,** an array of `AudioResource` objects that provides information about the audio-type resources that are contained in the resource. Omitted for an audio-type resource.
"""
def __init__(self,
duration=None,
name=None,
details=None,
status=None,
container=None,
audio=None):
"""
Initialize a AudioListing object.
:param float duration: (optional) **For an audio-type resource,** the total seconds of audio in the resource. Omitted for an archive-type resource.
:param str name: (optional) **For an audio-type resource,** the name of the resource. Omitted for an archive-type resource.
:param AudioDetails details: (optional) **For an audio-type resource,** an `AudioDetails` object that provides detailed information about the resource. The object is empty until the service finishes processing the audio. Omitted for an archive-type resource.
:param str status: (optional) **For an audio-type resource,** the status of the resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that the service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid` indicates that the audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). Omitted for an archive-type resource.
:param AudioResource container: (optional) **For an archive-type resource,** an object of type `AudioResource` that provides information about the resource. Omitted for an audio-type resource.
:param list[AudioResource] audio: (optional) **For an archive-type resource,** an array of `AudioResource` objects that provides information about the audio-type resources that are contained in the resource. Omitted for an audio-type resource.
"""
self.duration = duration
self.name = name
self.details = details
self.status = status
self.container = container
self.audio = audio
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioListing object from a json dictionary."""
args = {}
if 'duration' in _dict:
args['duration'] = _dict.get('duration')
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'details' in _dict:
args['details'] = AudioDetails._from_dict(_dict.get('details'))
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'container' in _dict:
args['container'] = AudioResource._from_dict(_dict.get('container'))
if 'audio' in _dict:
args['audio'] = [
AudioResource._from_dict(x) for x in (_dict.get('audio'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'duration') and self.duration is not None:
_dict['duration'] = self.duration
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'details') and self.details is not None:
_dict['details'] = self.details._to_dict()
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'container') and self.container is not None:
_dict['container'] = self.container._to_dict()
if hasattr(self, 'audio') and self.audio is not None:
_dict['audio'] = [x._to_dict() for x in self.audio]
return _dict
def __str__(self):
"""Return a `str` version of this AudioListing object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AudioResource(object):
"""
AudioResource.
:attr float duration: The total seconds of audio in the audio resource.
:attr str name: The name of the audio resource.
:attr AudioDetails details: An `AudioDetails` object that provides detailed information about the audio resource. The object is empty until the service finishes processing the audio.
:attr str status: The status of the audio resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that the service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid` indicates that the audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid.
"""
def __init__(self, duration, name, details, status):
"""
Initialize a AudioResource object.
:param float duration: The total seconds of audio in the audio resource.
:param str name: The name of the audio resource.
:param AudioDetails details: An `AudioDetails` object that provides detailed information about the audio resource. The object is empty until the service finishes processing the audio.
:param str status: The status of the audio resource: * `ok` indicates that the service has successfully analyzed the audio data. The data can be used to train the custom model. * `being_processed` indicates that the service is still analyzing the audio data. The service cannot accept requests to add new audio resources or to train the custom model until its analysis is complete. * `invalid` indicates that the audio data is not valid for training the custom model (possibly because it has the wrong format or sampling rate, or because it is corrupted). For an archive file, the entire archive is invalid if any of its audio files are invalid.
"""
self.duration = duration
self.name = name
self.details = details
self.status = status
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioResource object from a json dictionary."""
args = {}
if 'duration' in _dict:
args['duration'] = _dict.get('duration')
else:
raise ValueError(
'Required property \'duration\' not present in AudioResource JSON'
)
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in AudioResource JSON')
if 'details' in _dict:
args['details'] = AudioDetails._from_dict(_dict.get('details'))
else:
raise ValueError(
'Required property \'details\' not present in AudioResource JSON'
)
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError(
'Required property \'status\' not present in AudioResource JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'duration') and self.duration is not None:
_dict['duration'] = self.duration
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'details') and self.details is not None:
_dict['details'] = self.details._to_dict()
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
return _dict
def __str__(self):
"""Return a `str` version of this AudioResource object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class AudioResources(object):
"""
AudioResources.
:attr float total_minutes_of_audio: The total minutes of accumulated audio summed over all of the valid audio resources for the custom acoustic model. You can use this value to determine whether the custom model has too little or too much audio to begin training.
:attr list[AudioResource] audio: An array of `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, audio):
"""
Initialize a AudioResources object.
:param float total_minutes_of_audio: The total minutes of accumulated audio summed over all of the valid audio resources for the custom acoustic model. You can use this value to determine whether the custom model has too little or too much audio to begin training.
:param list[AudioResource] audio: An array of `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):
"""Initialize a AudioResources object from a json dictionary."""
args = {}
if 'total_minutes_of_audio' in _dict:
args['total_minutes_of_audio'] = _dict.get('total_minutes_of_audio')
else:
raise ValueError(
'Required property \'total_minutes_of_audio\' not present in AudioResources JSON'
)
if 'audio' in _dict:
args['audio'] = [
AudioResource._from_dict(x) for x in (_dict.get('audio'))
]
else:
raise ValueError(
'Required property \'audio\' not present in AudioResources JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'total_minutes_of_audio'
) and self.total_minutes_of_audio is not None:
_dict['total_minutes_of_audio'] = self.total_minutes_of_audio
if hasattr(self, 'audio') and self.audio is not None:
_dict['audio'] = [x._to_dict() for x in self.audio]
return _dict
def __str__(self):
"""Return a `str` version of this AudioResources object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Corpora(object):
"""
Corpora.
:attr list[Corpus] corpora: Information about corpora of the custom model. The array is empty if the custom model has no corpora.
"""
def __init__(self, corpora):
"""
Initialize a Corpora object.
:param list[Corpus] corpora: Information about corpora of the custom model. The array is empty if the custom model has no corpora.
"""
self.corpora = corpora
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Corpora object from a json dictionary."""
args = {}
if 'corpora' in _dict:
args['corpora'] = [
Corpus._from_dict(x) for x in (_dict.get('corpora'))
]
else:
raise ValueError(
'Required property \'corpora\' not present in Corpora JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'corpora') and self.corpora is not None:
_dict['corpora'] = [x._to_dict() for x in self.corpora]
return _dict
def __str__(self):
"""Return a `str` version of this Corpora object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Corpus(object):
"""
Corpus.
:attr str name: The name of the corpus.
:attr int total_words: The total number of words in the corpus. The value is `0` while the corpus is being processed.
:attr int out_of_vocabulary_words: The number of OOV words in the corpus. The value is `0` while the corpus is being processed.
:attr str status: The status of the corpus: * `analyzed` indicates that the service has successfully analyzed the corpus; the custom model can be trained with data from the corpus. * `being_processed` indicates that the service is still analyzing the corpus; the service cannot accept requests to add new corpora or words, or to train the custom model. * `undetermined` indicates that the service encountered an error while processing the corpus.
:attr str error: (optional) If the status of the corpus is `undetermined`, the following message: `Analysis of corpus 'name' failed. Please try adding the corpus again by setting the 'allow_overwrite' flag to 'true'`.
"""
def __init__(self,
name,
total_words,
out_of_vocabulary_words,
status,
error=None):
"""
Initialize a Corpus object.
:param str name: The name of the corpus.
:param int total_words: The total number of words in the corpus. The value is `0` while the corpus is being processed.
:param int out_of_vocabulary_words: The number of OOV words in the corpus. The value is `0` while the corpus is being processed.
:param str status: The status of the corpus: * `analyzed` indicates that the service has successfully analyzed the corpus; the custom model can be trained with data from the corpus. * `being_processed` indicates that the service is still analyzing the corpus; the service cannot accept requests to add new corpora or words, or to train the custom model. * `undetermined` indicates that the service encountered an error while processing the corpus.
:param str error: (optional) If the status of the corpus is `undetermined`, the following message: `Analysis of corpus 'name' failed. Please try adding the corpus again by setting the 'allow_overwrite' flag to 'true'`.
"""
self.name = name
self.total_words = total_words
self.out_of_vocabulary_words = out_of_vocabulary_words
self.status = status
self.error = error
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Corpus object from a json dictionary."""
args = {}
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in Corpus JSON')
if 'total_words' in _dict:
args['total_words'] = _dict.get('total_words')
else:
raise ValueError(
'Required property \'total_words\' not present in Corpus JSON')
if 'out_of_vocabulary_words' in _dict:
args['out_of_vocabulary_words'] = _dict.get(
'out_of_vocabulary_words')
else:
raise ValueError(
'Required property \'out_of_vocabulary_words\' not present in Corpus JSON'
)
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError(
'Required property \'status\' not present in Corpus JSON')
if 'error' in _dict:
args['error'] = _dict.get('error')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'total_words') and self.total_words is not None:
_dict['total_words'] = self.total_words
if hasattr(self, 'out_of_vocabulary_words'
) and self.out_of_vocabulary_words is not None:
_dict['out_of_vocabulary_words'] = self.out_of_vocabulary_words
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'error') and self.error is not None:
_dict['error'] = self.error
return _dict
def __str__(self):
"""Return a `str` version of this Corpus object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class CustomWord(object):
"""
CustomWord.
:attr str word: (optional) For the **Add custom words** method, you must specify the custom word that is to be added to or updated in the custom model. Do not include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of compound words. Omit this field for the **Add a custom word** method.
:attr list[str] sounds_like: (optional) An array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. For a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations, and a pronunciation can include at most 40 characters not including spaces.
:attr str display_as: (optional) An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.
"""
def __init__(self, word=None, sounds_like=None, display_as=None):
"""
Initialize a CustomWord object.
:param str word: (optional) For the **Add custom words** method, you must specify the custom word that is to be added to or updated in the custom model. Do not include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of compound words. Omit this field for the **Add a custom word** method.
:param list[str] sounds_like: (optional) An array of sounds-like pronunciations for the custom word. Specify how words that are difficult to pronounce, foreign words, acronyms, and so on can be pronounced by users. For a word that is not in the service's base vocabulary, omit the parameter to have the service automatically generate a sounds-like pronunciation for the word. For a word that is in the service's base vocabulary, use the parameter to specify additional pronunciations for the word. You cannot override the default pronunciation of a word; pronunciations you add augment the pronunciation from the base vocabulary. A word can have at most five sounds-like pronunciations, and a pronunciation can include at most 40 characters not including spaces.
:param str display_as: (optional) An alternative spelling for the custom word when it appears in a transcript. Use the parameter when you want the word to have a spelling that is different from its usual representation or from its spelling in corpora training data.
"""
self.word = word
self.sounds_like = sounds_like
self.display_as = display_as
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CustomWord object from a json dictionary."""
args = {}
if 'word' in _dict:
args['word'] = _dict.get('word')
if 'sounds_like' in _dict:
args['sounds_like'] = _dict.get('sounds_like')
if 'display_as' in _dict:
args['display_as'] = _dict.get('display_as')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'word') and self.word is not None:
_dict['word'] = self.word
if hasattr(self, 'sounds_like') and self.sounds_like is not None:
_dict['sounds_like'] = self.sounds_like
if hasattr(self, 'display_as') and self.display_as is not None:
_dict['display_as'] = self.display_as
return _dict
def __str__(self):
"""Return a `str` version of this CustomWord object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class KeywordResult(object):
"""
KeywordResult.
:attr str normalized_text: A specified keyword normalized to the spoken phrase that matched in the audio input.
:attr float start_time: The start time in seconds of the keyword match.
:attr float end_time: The end time in seconds of the keyword match.
:attr float confidence: A confidence score for the keyword match in the range of 0 to 1.
"""
def __init__(self, normalized_text, start_time, end_time, confidence):
"""
Initialize a KeywordResult object.
:param str normalized_text: A specified keyword normalized to the spoken phrase that matched in the audio input.
:param float start_time: The start time in seconds of the keyword match.
:param float end_time: The end time in seconds of the keyword match.
:param float confidence: A confidence score for the keyword match in the range of 0 to 1.
"""
self.normalized_text = normalized_text
self.start_time = start_time
self.end_time = end_time
self.confidence = confidence
@classmethod
def _from_dict(cls, _dict):
"""Initialize a KeywordResult object from a json dictionary."""
args = {}
if 'normalized_text' in _dict:
args['normalized_text'] = _dict.get('normalized_text')
else:
raise ValueError(
'Required property \'normalized_text\' not present in KeywordResult JSON'
)
if 'start_time' in _dict:
args['start_time'] = _dict.get('start_time')
else:
raise ValueError(
'Required property \'start_time\' not present in KeywordResult JSON'
)
if 'end_time' in _dict:
args['end_time'] = _dict.get('end_time')
else:
raise ValueError(
'Required property \'end_time\' not present in KeywordResult JSON'
)
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
else:
raise ValueError(
'Required property \'confidence\' not present in KeywordResult JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'normalized_text') and self.normalized_text is not None:
_dict['normalized_text'] = self.normalized_text
if hasattr(self, 'start_time') and self.start_time is not None:
_dict['start_time'] = self.start_time
if hasattr(self, 'end_time') and self.end_time is not None:
_dict['end_time'] = self.end_time
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
return _dict
def __str__(self):
"""Return a `str` version of this KeywordResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class LanguageModel(object):
"""
LanguageModel.
:attr str customization_id: The customization ID (GUID) of the custom language model. The **Create a custom language model** method returns only this field of the object; it does not return the other fields.
:attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str language: (optional) The language identifier of the custom language model (for example, `en-US`).
:attr str dialect: (optional) The dialect of the language for the custom language model. By default, the dialect matches the language of the base model; for example, `en-US` for either of the US English language models. For Spanish models, the field indicates the dialect for which the model was created: * `es-ES` for Castilian Spanish (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish.
:attr list[str] versions: (optional) A list of the available versions of the custom language model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
:attr str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom language model.
:attr str name: (optional) The name of the custom language model.
:attr str description: (optional) The description of the custom language model.
:attr str base_model_name: (optional) The name of the language model for which the custom language model was created.
:attr str status: (optional) The current status of the custom language model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed.
:attr int progress: (optional) A percentage that indicates the progress of the custom language model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete.
:attr str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter.
"""
def __init__(self,
customization_id,
created=None,
language=None,
dialect=None,
versions=None,
owner=None,
name=None,
description=None,
base_model_name=None,
status=None,
progress=None,
warnings=None):
"""
Initialize a LanguageModel object.
:param str customization_id: The customization ID (GUID) of the custom language model. The **Create a custom language model** method returns only this field of the object; it does not return the other fields.
:param str created: (optional) The date and time in Coordinated Universal Time (UTC) at which the custom language model was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:param str language: (optional) The language identifier of the custom language model (for example, `en-US`).
:param str dialect: (optional) The dialect of the language for the custom language model. By default, the dialect matches the language of the base model; for example, `en-US` for either of the US English language models. For Spanish models, the field indicates the dialect for which the model was created: * `es-ES` for Castilian Spanish (the default) * `es-LA` for Latin American Spanish * `es-US` for North American (Mexican) Spanish.
:param list[str] versions: (optional) A list of the available versions of the custom language model. Each element of the array indicates a version of the base model with which the custom model can be used. Multiple versions exist only if the custom model has been upgraded; otherwise, only a single version is shown.
:param str owner: (optional) The GUID of the service credentials for the instance of the service that owns the custom language model.
:param str name: (optional) The name of the custom language model.
:param str description: (optional) The description of the custom language model.
:param str base_model_name: (optional) The name of the language model for which the custom language model was created.
:param str status: (optional) The current status of the custom language model: * `pending` indicates that the model was created but is waiting either for training data to be added or for the service to finish analyzing added data. * `ready` indicates that the model contains data and is ready to be trained. * `training` indicates that the model is currently being trained. * `available` indicates that the model is trained and ready to use. * `upgrading` indicates that the model is currently being upgraded. * `failed` indicates that training of the model failed.
:param int progress: (optional) A percentage that indicates the progress of the custom language model's current training. A value of `100` means that the model is fully trained. **Note:** The `progress` field does not currently reflect the progress of the training. The field changes from `0` to `100` when training is complete.
:param str warnings: (optional) If the request included unknown parameters, the following message: `Unexpected query parameter(s) ['parameters'] detected`, where `parameters` is a list that includes a quoted string for each unknown parameter.
"""
self.customization_id = customization_id
self.created = created
self.language = language
self.dialect = dialect
self.versions = versions
self.owner = owner
self.name = name
self.description = description
self.base_model_name = base_model_name
self.status = status
self.progress = progress
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a LanguageModel object from a json dictionary."""
args = {}
if 'customization_id' in _dict:
args['customization_id'] = _dict.get('customization_id')
else:
raise ValueError(
'Required property \'customization_id\' not present in LanguageModel JSON'
)
if 'created' in _dict:
args['created'] = _dict.get('created')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'dialect' in _dict:
args['dialect'] = _dict.get('dialect')
if 'versions' in _dict:
args['versions'] = _dict.get('versions')
if 'owner' in _dict:
args['owner'] = _dict.get('owner')
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'base_model_name' in _dict:
args['base_model_name'] = _dict.get('base_model_name')
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'progress' in _dict:
args['progress'] = _dict.get('progress')
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'customization_id') and self.customization_id is not None:
_dict['customization_id'] = self.customization_id
if hasattr(self, 'created') and self.created is not None:
_dict['created'] = self.created
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'dialect') and self.dialect is not None:
_dict['dialect'] = self.dialect
if hasattr(self, 'versions') and self.versions is not None:
_dict['versions'] = self.versions
if hasattr(self, 'owner') and self.owner is not None:
_dict['owner'] = self.owner
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self,
'base_model_name') and self.base_model_name is not None:
_dict['base_model_name'] = self.base_model_name
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'progress') and self.progress is not None:
_dict['progress'] = self.progress
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __str__(self):
"""Return a `str` version of this LanguageModel object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class LanguageModels(object):
"""
LanguageModels.
:attr list[LanguageModel] customizations: An array of objects that provides information about each available custom language model. The array is empty if the requesting service credentials own no custom language models (if no language is specified) or own no custom language models for the specified language.
"""
def __init__(self, customizations):
"""
Initialize a LanguageModels object.
:param list[LanguageModel] customizations: An array of objects that provides information about each available custom language model. The array is empty if the requesting service credentials own no custom language models (if no language is specified) or own no custom language models for the specified language.
"""
self.customizations = customizations
@classmethod
def _from_dict(cls, _dict):
"""Initialize a LanguageModels object from a json dictionary."""
args = {}
if 'customizations' in _dict:
args['customizations'] = [
LanguageModel._from_dict(x)
for x in (_dict.get('customizations'))
]
else:
raise ValueError(
'Required property \'customizations\' not present in LanguageModels JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'customizations') and self.customizations is not None:
_dict['customizations'] = [
x._to_dict() for x in self.customizations
]
return _dict
def __str__(self):
"""Return a `str` version of this LanguageModels object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RecognitionJob(object):
"""
RecognitionJob.
:attr str id: The ID of the asynchronous job.
:attr str status: The current status of the job: * `waiting`: The service is preparing the job for processing. The service returns this status when the job is initially created or when it is waiting for capacity to process the job. The job remains in this state until the service has the capacity to begin processing it. * `processing`: The service is actively processing the job. * `completed`: The service has finished processing the job. If the job specified a callback URL and the event `recognitions.completed_with_results`, the service sent the results with the callback notification; otherwise, you must retrieve the results by checking the individual job. * `failed`: The job failed.
:attr str created: The date and time in Coordinated Universal Time (UTC) at which the job was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the job was last updated by the service. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). This field is returned only by the **Check jobs** and **Check a job** methods.
:attr str url: (optional) The URL to use to request information about the job with the **Check a job** method. This field is returned only by the **Create a job** method.
:attr str user_token: (optional) The user token associated with a job that was created with a callback URL and a user token. This field can be returned only by the **Check jobs** method.
:attr list[SpeechRecognitionResults] results: (optional) If the status is `completed`, the results of the recognition request as an array that includes a single instance of a `SpeechRecognitionResults` object. This field is returned only by the **Check a job** method.
:attr list[str] warnings: (optional) An array of warning messages about invalid parameters included with the request. Each warning includes a descriptive message and a list of invalid argument strings, for example, `"unexpected query parameter 'user_token', query parameter 'callback_url' was not specified"`. The request succeeds despite the warnings. This field can be returned only by the **Create a job** method.
"""
def __init__(self,
id,
status,
created,
updated=None,
url=None,
user_token=None,
results=None,
warnings=None):
"""
Initialize a RecognitionJob object.
:param str id: The ID of the asynchronous job.
:param str status: The current status of the job: * `waiting`: The service is preparing the job for processing. The service returns this status when the job is initially created or when it is waiting for capacity to process the job. The job remains in this state until the service has the capacity to begin processing it. * `processing`: The service is actively processing the job. * `completed`: The service has finished processing the job. If the job specified a callback URL and the event `recognitions.completed_with_results`, the service sent the results with the callback notification; otherwise, you must retrieve the results by checking the individual job. * `failed`: The job failed.
:param str created: The date and time in Coordinated Universal Time (UTC) at which the job was created. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:param str updated: (optional) The date and time in Coordinated Universal Time (UTC) at which the job was last updated by the service. The value is provided in full ISO 8601 format (`YYYY-MM-DDThh:mm:ss.sTZD`). This field is returned only by the **Check jobs** and **Check a job** methods.
:param str url: (optional) The URL to use to request information about the job with the **Check a job** method. This field is returned only by the **Create a job** method.
:param str user_token: (optional) The user token associated with a job that was created with a callback URL and a user token. This field can be returned only by the **Check jobs** method.
:param list[SpeechRecognitionResults] results: (optional) If the status is `completed`, the results of the recognition request as an array that includes a single instance of a `SpeechRecognitionResults` object. This field is returned only by the **Check a job** method.
:param list[str] warnings: (optional) An array of warning messages about invalid parameters included with the request. Each warning includes a descriptive message and a list of invalid argument strings, for example, `"unexpected query parameter 'user_token', query parameter 'callback_url' was not specified"`. The request succeeds despite the warnings. This field can be returned only by the **Create a job** method.
"""
self.id = id
self.status = status
self.created = created
self.updated = updated
self.url = url
self.user_token = user_token
self.results = results
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RecognitionJob object from a json dictionary."""
args = {}
if 'id' in _dict:
args['id'] = _dict.get('id')
else:
raise ValueError(
'Required property \'id\' not present in RecognitionJob JSON')
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError(
'Required property \'status\' not present in RecognitionJob JSON'
)
if 'created' in _dict:
args['created'] = _dict.get('created')
else:
raise ValueError(
'Required property \'created\' not present in RecognitionJob JSON'
)
if 'updated' in _dict:
args['updated'] = _dict.get('updated')
if 'url' in _dict:
args['url'] = _dict.get('url')
if 'user_token' in _dict:
args['user_token'] = _dict.get('user_token')
if 'results' in _dict:
args['results'] = [
SpeechRecognitionResults._from_dict(x)
for x in (_dict.get('results'))
]
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'id') and self.id is not None:
_dict['id'] = self.id
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'created') and self.created is not None:
_dict['created'] = self.created
if hasattr(self, 'updated') and self.updated is not None:
_dict['updated'] = self.updated
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
if hasattr(self, 'user_token') and self.user_token is not None:
_dict['user_token'] = self.user_token
if hasattr(self, 'results') and self.results is not None:
_dict['results'] = [x._to_dict() for x in self.results]
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __str__(self):
"""Return a `str` version of this RecognitionJob object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RecognitionJobs(object):
"""
RecognitionJobs.
:attr list[RecognitionJob] recognitions: An array of objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs.
"""
def __init__(self, recognitions):
"""
Initialize a RecognitionJobs object.
:param list[RecognitionJob] recognitions: An array of objects that provides the status for each of the user's current jobs. The array is empty if the user has no current jobs.
"""
self.recognitions = recognitions
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RecognitionJobs object from a json dictionary."""
args = {}
if 'recognitions' in _dict:
args['recognitions'] = [
RecognitionJob._from_dict(x)
for x in (_dict.get('recognitions'))
]
else:
raise ValueError(
'Required property \'recognitions\' not present in RecognitionJobs JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'recognitions') and self.recognitions is not None:
_dict['recognitions'] = [x._to_dict() for x in self.recognitions]
return _dict
def __str__(self):
"""Return a `str` version of this RecognitionJobs object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class RegisterStatus(object):
"""
RegisterStatus.
:attr str status: The current status of the job: * `created` if the callback URL was successfully white-listed as a result of the call. * `already created` if the URL was already white-listed.
:attr str url: The callback URL that is successfully registered.
"""
def __init__(self, status, url):
"""
Initialize a RegisterStatus object.
:param str status: The current status of the job: * `created` if the callback URL was successfully white-listed as a result of the call. * `already created` if the URL was already white-listed.
:param str url: The callback URL that is successfully registered.
"""
self.status = status
self.url = url
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RegisterStatus object from a json dictionary."""
args = {}
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError(
'Required property \'status\' not present in RegisterStatus JSON'
)
if 'url' in _dict:
args['url'] = _dict.get('url')
else:
raise ValueError(
'Required property \'url\' not present in RegisterStatus JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
return _dict
def __str__(self):
"""Return a `str` version of this RegisterStatus object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeakerLabelsResult(object):
"""
SpeakerLabelsResult.
:attr float _from: The start time of a word from the transcript. The value matches the start time of a word from the `timestamps` array.
:attr float to: The end time of a word from the transcript. The value matches the end time of a word from the `timestamps` array.
:attr int speaker: The numeric identifier that the service assigns to a speaker from the audio. Speaker IDs begin at `0` initially but can evolve and change across interim results (if supported by the method) and between interim and final results as the service processes the audio. They are not guaranteed to be sequential, contiguous, or ordered.
:attr float confidence: A score that indicates the service's confidence in its identification of the speaker in the range of 0 to 1.
:attr bool final_results: An indication of whether the service might further change word and speaker-label results. A value of `true` means that the service guarantees not to send any further updates for the current or any preceding results; `false` means that the service might send further updates to the results.
"""
def __init__(self, _from, to, speaker, confidence, final_results):
"""
Initialize a SpeakerLabelsResult object.
:param float _from: The start time of a word from the transcript. The value matches the start time of a word from the `timestamps` array.
:param float to: The end time of a word from the transcript. The value matches the end time of a word from the `timestamps` array.
:param int speaker: The numeric identifier that the service assigns to a speaker from the audio. Speaker IDs begin at `0` initially but can evolve and change across interim results (if supported by the method) and between interim and final results as the service processes the audio. They are not guaranteed to be sequential, contiguous, or ordered.
:param float confidence: A score that indicates the service's confidence in its identification of the speaker in the range of 0 to 1.
:param bool final_results: An indication of whether the service might further change word and speaker-label results. A value of `true` means that the service guarantees not to send any further updates for the current or any preceding results; `false` means that the service might send further updates to the results.
"""
self._from = _from
self.to = to
self.speaker = speaker
self.confidence = confidence
self.final_results = final_results
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeakerLabelsResult object from a json dictionary."""
args = {}
if 'from' in _dict:
args['_from'] = _dict.get('from')
else:
raise ValueError(
'Required property \'from\' not present in SpeakerLabelsResult JSON'
)
if 'to' in _dict:
args['to'] = _dict.get('to')
else:
raise ValueError(
'Required property \'to\' not present in SpeakerLabelsResult JSON'
)
if 'speaker' in _dict:
args['speaker'] = _dict.get('speaker')
else:
raise ValueError(
'Required property \'speaker\' not present in SpeakerLabelsResult JSON'
)
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
else:
raise ValueError(
'Required property \'confidence\' not present in SpeakerLabelsResult JSON'
)
if 'final' in _dict or 'final_results' in _dict:
args['final_results'] = _dict.get('final') or _dict.get('final_results')
else:
raise ValueError(
'Required property \'final\' not present in SpeakerLabelsResult JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, '_from') and self._from is not None:
_dict['from'] = self._from
if hasattr(self, 'to') and self.to is not None:
_dict['to'] = self.to
if hasattr(self, 'speaker') and self.speaker is not None:
_dict['speaker'] = self.speaker
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
if hasattr(self, 'final_results') and self.final_results is not None:
_dict['final'] = self.final_results
return _dict
def __str__(self):
"""Return a `str` version of this SpeakerLabelsResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechModel(object):
"""
SpeechModel.
:attr str name: The name of the model for use as an identifier in calls to the service (for example, `en-US_BroadbandModel`).
:attr str language: The language identifier of the model (for example, `en-US`).
:attr int rate: The sampling rate (minimum acceptable rate for audio) used by the model in Hertz.
:attr str url: The URI for the model.
:attr SupportedFeatures supported_features: Describes the additional service features supported with the model.
:attr str description: Brief description of the model.
:attr str sessions: (optional) The URI for the model for use with the **Create a session** method. This field is returned only by the **Get a model** method.
"""
def __init__(self,
name,
language,
rate,
url,
supported_features,
description,
sessions=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: Describes the additional service features supported with the model.
:param str description: Brief description of the model.
:param str sessions: (optional) The URI for the model for use with the **Create a session** method. This field is returned only by the **Get a model** method.
"""
self.name = name
self.language = language
self.rate = rate
self.url = url
self.supported_features = supported_features
self.description = description
self.sessions = sessions
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechModel object from a json dictionary."""
args = {}
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in SpeechModel JSON')
if 'language' in _dict:
args['language'] = _dict.get('language')
else:
raise ValueError(
'Required property \'language\' not present in SpeechModel JSON'
)
if 'rate' in _dict:
args['rate'] = _dict.get('rate')
else:
raise ValueError(
'Required property \'rate\' not present in SpeechModel JSON')
if 'url' in _dict:
args['url'] = _dict.get('url')
else:
raise ValueError(
'Required property \'url\' not present in SpeechModel JSON')
if 'supported_features' in _dict:
args['supported_features'] = SupportedFeatures._from_dict(
_dict.get('supported_features'))
else:
raise ValueError(
'Required property \'supported_features\' not present in SpeechModel JSON'
)
if 'description' in _dict:
args['description'] = _dict.get('description')
else:
raise ValueError(
'Required property \'description\' not present in SpeechModel JSON'
)
if 'sessions' in _dict:
args['sessions'] = _dict.get('sessions')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'rate') and self.rate is not None:
_dict['rate'] = self.rate
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
if hasattr(
self,
'supported_features') and self.supported_features is not None:
_dict['supported_features'] = self.supported_features._to_dict()
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self, 'sessions') and self.sessions is not None:
_dict['sessions'] = self.sessions
return _dict
def __str__(self):
"""Return a `str` version of this SpeechModel object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechModels(object):
"""
SpeechModels.
:attr list[SpeechModel] models: Information about each available model.
"""
def __init__(self, models):
"""
Initialize a SpeechModels object.
:param list[SpeechModel] models: Information about each available model.
"""
self.models = models
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechModels object from a json dictionary."""
args = {}
if 'models' in _dict:
args['models'] = [
SpeechModel._from_dict(x) for x in (_dict.get('models'))
]
else:
raise ValueError(
'Required property \'models\' not present in SpeechModels JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'models') and self.models is not None:
_dict['models'] = [x._to_dict() for x in self.models]
return _dict
def __str__(self):
"""Return a `str` version of this SpeechModels object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechRecognitionAlternative(object):
"""
SpeechRecognitionAlternative.
:attr str transcript: A transcription of the audio.
:attr float confidence: (optional) A score that indicates the service's confidence in the transcript in the range of 0 to 1. Returned only for the best alternative and only with results marked as final.
:attr list[str] timestamps: (optional) Time alignments for each word from the transcript as a list of lists. Each inner list consists of three elements: the word followed by its start and end time in seconds. Example: `[["hello",0.0,1.2],["world",1.2,2.5]]`. 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 to 1. Example: `[["hello",0.95],["world",0.866]]`. Returned only for the best alternative and only with results marked as final.
"""
def __init__(self,
transcript,
confidence=None,
timestamps=None,
word_confidence=None):
"""
Initialize a SpeechRecognitionAlternative object.
:param str transcript: A transcription of the audio.
:param float confidence: (optional) A score that indicates the service's confidence in the transcript in the range of 0 to 1. Returned only for the best alternative and only with results marked as final.
:param list[str] timestamps: (optional) Time alignments for each word from the transcript as a list of lists. Each inner list consists of three elements: the word followed by its start and end time in seconds. Example: `[["hello",0.0,1.2],["world",1.2,2.5]]`. 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 to 1. Example: `[["hello",0.95],["world",0.866]]`. Returned only for the best alternative and only with results marked as final.
"""
self.transcript = transcript
self.confidence = confidence
self.timestamps = timestamps
self.word_confidence = word_confidence
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionAlternative object from a json dictionary."""
args = {}
if 'transcript' in _dict:
args['transcript'] = _dict.get('transcript')
else:
raise ValueError(
'Required property \'transcript\' not present in SpeechRecognitionAlternative JSON'
)
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
if 'timestamps' in _dict:
args['timestamps'] = _dict.get('timestamps')
if 'word_confidence' in _dict:
args['word_confidence'] = _dict.get('word_confidence')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'transcript') and self.transcript is not None:
_dict['transcript'] = self.transcript
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
if hasattr(self, 'timestamps') and self.timestamps is not None:
_dict['timestamps'] = self.timestamps
if hasattr(self,
'word_confidence') and self.word_confidence is not None:
_dict['word_confidence'] = self.word_confidence
return _dict
def __str__(self):
"""Return a `str` version of this SpeechRecognitionAlternative object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechRecognitionResult(object):
"""
SpeechRecognitionResult.
:attr bool final_results: An indication of whether the transcription results are final. If `true`, the results for this utterance are not updated further; no additional results are sent for a `result_index` once its results are indicated as final.
:attr list[SpeechRecognitionAlternative] alternatives: An array of alternative transcripts. The `alternatives` array can include additional requested output such as word confidence or timestamps.
:attr dict keywords_result: (optional) A dictionary (or associative array) whose keys are the strings specified for `keywords` if both that parameter and `keywords_threshold` are specified. A keyword for which no matches are found is omitted from the array. The array is omitted if no matches are found for any keywords.
:attr list[WordAlternativeResults] word_alternatives: (optional) An array of alternative hypotheses found for words of the input audio if a `word_alternatives_threshold` is specified.
"""
def __init__(self,
final_results,
alternatives,
keywords_result=None,
word_alternatives=None):
"""
Initialize a SpeechRecognitionResult object.
:param bool final_results: An indication of whether the transcription results are final. If `true`, the results for this utterance are not updated further; no additional results are sent for a `result_index` once its results are indicated as final.
:param list[SpeechRecognitionAlternative] alternatives: An array of alternative transcripts. The `alternatives` array can include additional requested output such as word confidence or timestamps.
:param dict keywords_result: (optional) A dictionary (or associative array) whose keys are the strings specified for `keywords` if both that parameter and `keywords_threshold` are specified. A keyword for which no matches are found is omitted from the array. The array is omitted if no matches are found for any keywords.
:param list[WordAlternativeResults] word_alternatives: (optional) An array of alternative hypotheses found for words of the input audio if a `word_alternatives_threshold` is specified.
"""
self.final_results = final_results
self.alternatives = alternatives
self.keywords_result = keywords_result
self.word_alternatives = word_alternatives
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionResult object from a json dictionary."""
args = {}
if 'final' in _dict or 'final_results' in _dict:
args['final_results'] = _dict.get('final') or _dict.get(
'final_results')
else:
raise ValueError(
'Required property \'final\' not present in SpeechRecognitionResult JSON'
)
if 'alternatives' in _dict:
args['alternatives'] = [
SpeechRecognitionAlternative._from_dict(x)
for x in (_dict.get('alternatives'))
]
else:
raise ValueError(
'Required property \'alternatives\' not present in SpeechRecognitionResult JSON'
)
if 'keywords_result' in _dict:
args['keywords_result'] = _dict.get('keywords_result')
if 'word_alternatives' in _dict:
args['word_alternatives'] = [
WordAlternativeResults._from_dict(x)
for x in (_dict.get('word_alternatives'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'final_results') and self.final_results is not None:
_dict['final'] = self.final_results
if hasattr(self, 'alternatives') and self.alternatives is not None:
_dict['alternatives'] = [x._to_dict() for x in self.alternatives]
if hasattr(self,
'keywords_result') and self.keywords_result is not None:
_dict['keywords_result'] = self.keywords_result
if hasattr(self,
'word_alternatives') and self.word_alternatives is not None:
_dict['word_alternatives'] = [
x._to_dict() for x in self.word_alternatives
]
return _dict
def __str__(self):
"""Return a `str` version of this SpeechRecognitionResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechRecognitionResults(object):
"""
SpeechRecognitionResults.
:attr list[SpeechRecognitionResult] results: (optional) An array that can include interim and final results (interim results are returned only if supported by the method). Final results are guaranteed not to change; interim results might be replaced by further interim results and final results. The service periodically sends updates to the results list; the `result_index` is set to the lowest index in the array that has changed; it is incremented for new results.
:attr int result_index: (optional) An index that indicates a change point in the `results` array. The service increments the index only for additional results that it sends for new audio for the same request.
:attr list[SpeakerLabelsResult] speaker_labels: (optional) An array that identifies which words were spoken by which speakers in a multi-person exchange. Returned in the response only if `speaker_labels` is `true`. When interim results are also requested for methods that support them, it is possible for a `SpeechRecognitionResults` object to include only the `speaker_labels` field.
:attr list[str] warnings: (optional) An array of warning messages associated with the request: * Warnings for invalid parameters or fields can include a descriptive message and a list of invalid argument strings, for example, `"Unknown arguments:"` or `"Unknown url query arguments:"` followed by a list of the form `"invalid_arg_1, invalid_arg_2."` * The following warning is returned if the request passes a custom model that is based on an older version of a base model for which an updated version is available: `"Using previous version of base model, because your custom model has been built with it. Please note that this version will be supported only for a limited time. Consider updating your custom model to the new base model. If you do not do that you will be automatically switched to base model when you used the non-updated custom model."` In both cases, the request succeeds despite the warnings.
"""
def __init__(self,
results=None,
result_index=None,
speaker_labels=None,
warnings=None):
"""
Initialize a SpeechRecognitionResults object.
:param list[SpeechRecognitionResult] results: (optional) An array that can include interim and final results (interim results are returned only if supported by the method). Final results are guaranteed not to change; interim results might be replaced by further interim results and final results. The service periodically sends updates to the results list; the `result_index` is set to the lowest index in the array that has changed; it is incremented for new results.
:param int result_index: (optional) An index that indicates a change point in the `results` array. The service increments the index only for additional results that it sends for new audio for the same request.
:param list[SpeakerLabelsResult] speaker_labels: (optional) An array that identifies which words were spoken by which speakers in a multi-person exchange. Returned in the response only if `speaker_labels` is `true`. When interim results are also requested for methods that support them, it is possible for a `SpeechRecognitionResults` object to include only the `speaker_labels` field.
:param list[str] warnings: (optional) An array of warning messages associated with the request: * Warnings for invalid parameters or fields can include a descriptive message and a list of invalid argument strings, for example, `"Unknown arguments:"` or `"Unknown url query arguments:"` followed by a list of the form `"invalid_arg_1, invalid_arg_2."` * The following warning is returned if the request passes a custom model that is based on an older version of a base model for which an updated version is available: `"Using previous version of base model, because your custom model has been built with it. Please note that this version will be supported only for a limited time. Consider updating your custom model to the new base model. If you do not do that you will be automatically switched to base model when you used the non-updated custom model."` In both cases, the request succeeds despite the warnings.
"""
self.results = results
self.result_index = result_index
self.speaker_labels = speaker_labels
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionResults object from a json dictionary."""
args = {}
if 'results' in _dict:
args['results'] = [
SpeechRecognitionResult._from_dict(x)
for x in (_dict.get('results'))
]
if 'result_index' in _dict:
args['result_index'] = _dict.get('result_index')
if 'speaker_labels' in _dict:
args['speaker_labels'] = [
SpeakerLabelsResult._from_dict(x)
for x in (_dict.get('speaker_labels'))
]
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'results') and self.results is not None:
_dict['results'] = [x._to_dict() for x in self.results]
if hasattr(self, 'result_index') and self.result_index is not None:
_dict['result_index'] = self.result_index
if hasattr(self, 'speaker_labels') and self.speaker_labels is not None:
_dict['speaker_labels'] = [
x._to_dict() for x in self.speaker_labels
]
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __str__(self):
"""Return a `str` version of this SpeechRecognitionResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SupportedFeatures(object):
"""
SupportedFeatures.
:attr bool custom_language_model: Indicates whether the customization interface can be used to create a custom language model based on the language model.
:attr bool speaker_labels: Indicates whether the `speaker_labels` parameter can be used with the language model.
"""
def __init__(self, custom_language_model, speaker_labels):
"""
Initialize a SupportedFeatures object.
:param bool custom_language_model: Indicates whether the customization interface can be used to create a custom language model based on the language model.
:param bool speaker_labels: Indicates whether the `speaker_labels` parameter can be used with the language model.
"""
self.custom_language_model = custom_language_model
self.speaker_labels = speaker_labels
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SupportedFeatures object from a json dictionary."""
args = {}
if 'custom_language_model' in _dict:
args['custom_language_model'] = _dict.get('custom_language_model')
else:
raise ValueError(
'Required property \'custom_language_model\' not present in SupportedFeatures JSON'
)
if 'speaker_labels' in _dict:
args['speaker_labels'] = _dict.get('speaker_labels')
else:
raise ValueError(
'Required property \'speaker_labels\' not present in SupportedFeatures JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'custom_language_model'
) and self.custom_language_model is not None:
_dict['custom_language_model'] = self.custom_language_model
if hasattr(self, 'speaker_labels') and self.speaker_labels is not None:
_dict['speaker_labels'] = self.speaker_labels
return _dict
def __str__(self):
"""Return a `str` version of this SupportedFeatures object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Word(object):
"""
Word.
:attr str word: A word from the custom model's words resource. The spelling of the word is used to train the model.
:attr list[str] sounds_like: An array of pronunciations for the word. The array can include the sounds-like pronunciation automatically generated by the service if none is provided for the word; the service adds this pronunciation when it finishes processing the word.
:attr str display_as: The spelling of the word that the service uses to display the word in a transcript. The field contains an empty string if no display-as value is provided for the word, in which case the word is displayed as it is spelled.
:attr int count: A sum of the number of times the word is found across all corpora. For example, if the word occurs five times in one corpus and seven times in another, its count is `12`. If you add a custom word to a model before it is added by any corpora, the count begins at `1`; if the word is added from a corpus first and later modified, the count reflects only the number of times it is found in corpora.
:attr list[str] source: An array of sources that describes how the word was added to the custom model's words resource. For OOV words added from a corpus, includes the name of the corpus; if the word was added by multiple corpora, the names of all corpora are listed. If the word was modified or added by the user directly, the field includes the string `user`.
:attr list[WordError] error: (optional) If the service discovered one or more problems that you need to correct for the word's definition, an array that describes each of the errors.
"""
def __init__(self, word, sounds_like, display_as, count, source,
error=None):
"""
Initialize a Word object.
:param str word: A word from the custom model's words resource. The spelling of the word is used to train the model.
:param list[str] sounds_like: An array of pronunciations for the word. The array can include the sounds-like pronunciation automatically generated by the service if none is provided for the word; the service adds this pronunciation when it finishes processing the word.
:param str display_as: The spelling of the word that the service uses to display the word in a transcript. The field contains an empty string if no display-as value is provided for the word, in which case the word is displayed as it is spelled.
:param int count: A sum of the number of times the word is found across all corpora. For example, if the word occurs five times in one corpus and seven times in another, its count is `12`. If you add a custom word to a model before it is added by any corpora, the count begins at `1`; if the word is added from a corpus first and later modified, the count reflects only the number of times it is found in corpora.
:param list[str] source: An array of sources that describes how the word was added to the custom model's words resource. For OOV words added from a corpus, includes the name of the corpus; if the word was added by multiple corpora, the names of all corpora are listed. If the word was modified or added by the user directly, the field includes the string `user`.
:param list[WordError] error: (optional) If the service discovered one or more problems that you need to correct for the word's definition, an array that describes each of the errors.
"""
self.word = word
self.sounds_like = sounds_like
self.display_as = display_as
self.count = count
self.source = source
self.error = error
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Word object from a json dictionary."""
args = {}
if 'word' in _dict:
args['word'] = _dict.get('word')
else:
raise ValueError(
'Required property \'word\' not present in Word JSON')
if 'sounds_like' in _dict:
args['sounds_like'] = _dict.get('sounds_like')
else:
raise ValueError(
'Required property \'sounds_like\' not present in Word JSON')
if 'display_as' in _dict:
args['display_as'] = _dict.get('display_as')
else:
raise ValueError(
'Required property \'display_as\' not present in Word JSON')
if 'count' in _dict:
args['count'] = _dict.get('count')
else:
raise ValueError(
'Required property \'count\' not present in Word JSON')
if 'source' in _dict:
args['source'] = _dict.get('source')
else:
raise ValueError(
'Required property \'source\' not present in Word JSON')
if 'error' in _dict:
args['error'] = [
WordError._from_dict(x) for x in (_dict.get('error'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'word') and self.word is not None:
_dict['word'] = self.word
if hasattr(self, 'sounds_like') and self.sounds_like is not None:
_dict['sounds_like'] = self.sounds_like
if hasattr(self, 'display_as') and self.display_as is not None:
_dict['display_as'] = self.display_as
if hasattr(self, 'count') and self.count is not None:
_dict['count'] = self.count
if hasattr(self, 'source') and self.source is not None:
_dict['source'] = self.source
if hasattr(self, 'error') and self.error is not None:
_dict['error'] = [x._to_dict() for x in self.error]
return _dict
def __str__(self):
"""Return a `str` version of this Word object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class WordAlternativeResult(object):
"""
WordAlternativeResult.
:attr float confidence: A confidence score for the word alternative hypothesis in the range of 0 to 1.
:attr str word: An alternative hypothesis for a word from the input audio.
"""
def __init__(self, confidence, word):
"""
Initialize a WordAlternativeResult object.
:param float confidence: A confidence score for the word alternative hypothesis in the range of 0 to 1.
:param str word: An alternative hypothesis for a word from the input audio.
"""
self.confidence = confidence
self.word = word
@classmethod
def _from_dict(cls, _dict):
"""Initialize a WordAlternativeResult object from a json dictionary."""
args = {}
if 'confidence' in _dict:
args['confidence'] = _dict.get('confidence')
else:
raise ValueError(
'Required property \'confidence\' not present in WordAlternativeResult JSON'
)
if 'word' in _dict:
args['word'] = _dict.get('word')
else:
raise ValueError(
'Required property \'word\' not present in WordAlternativeResult JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'confidence') and self.confidence is not None:
_dict['confidence'] = self.confidence
if hasattr(self, 'word') and self.word is not None:
_dict['word'] = self.word
return _dict
def __str__(self):
"""Return a `str` version of this WordAlternativeResult object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class WordAlternativeResults(object):
"""
WordAlternativeResults.
:attr float start_time: The start time in seconds of the word from the input audio that corresponds to the word alternatives.
:attr float end_time: The end time in seconds of the word from the input audio that corresponds to the word alternatives.
:attr list[WordAlternativeResult] alternatives: An array of alternative hypotheses for a word from the input audio.
"""
def __init__(self, start_time, end_time, alternatives):
"""
Initialize a WordAlternativeResults object.
:param float start_time: The start time in seconds of the word from the input audio that corresponds to the word alternatives.
:param float end_time: The end time in seconds of the word from the input audio that corresponds to the word alternatives.
:param list[WordAlternativeResult] alternatives: An array of alternative hypotheses for a word from the input audio.
"""
self.start_time = start_time
self.end_time = end_time
self.alternatives = alternatives
@classmethod
def _from_dict(cls, _dict):
"""Initialize a WordAlternativeResults object from a json dictionary."""
args = {}
if 'start_time' in _dict:
args['start_time'] = _dict.get('start_time')
else:
raise ValueError(
'Required property \'start_time\' not present in WordAlternativeResults JSON'
)
if 'end_time' in _dict:
args['end_time'] = _dict.get('end_time')
else:
raise ValueError(
'Required property \'end_time\' not present in WordAlternativeResults JSON'
)
if 'alternatives' in _dict:
args['alternatives'] = [
WordAlternativeResult._from_dict(x)
for x in (_dict.get('alternatives'))
]
else:
raise ValueError(
'Required property \'alternatives\' not present in WordAlternativeResults JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'start_time') and self.start_time is not None:
_dict['start_time'] = self.start_time
if hasattr(self, 'end_time') and self.end_time is not None:
_dict['end_time'] = self.end_time
if hasattr(self, 'alternatives') and self.alternatives is not None:
_dict['alternatives'] = [x._to_dict() for x in self.alternatives]
return _dict
def __str__(self):
"""Return a `str` version of this WordAlternativeResults object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class WordError(object):
"""
WordError.
:attr str element: A key-value pair that describes an error associated with the definition of a word in the words resource. Each pair has the format `"element": "message"`, where `element` is the aspect of the definition that caused the problem and `message` describes the problem. The following example describes a problem with one of the word's sounds-like definitions: `"{sounds_like_string}": "Numbers are not allowed in sounds-like. You can try for example '{suggested_string}'."` You must correct the error before you can train the model.
"""
def __init__(self, element):
"""
Initialize a WordError object.
:param str element: A key-value pair that describes an error associated with the definition of a word in the words resource. Each pair has the format `"element": "message"`, where `element` is the aspect of the definition that caused the problem and `message` describes the problem. The following example describes a problem with one of the word's sounds-like definitions: `"{sounds_like_string}": "Numbers are not allowed in sounds-like. You can try for example '{suggested_string}'."` You must correct the error before you can train the model.
"""
self.element = element
@classmethod
def _from_dict(cls, _dict):
"""Initialize a WordError object from a json dictionary."""
args = {}
if 'element' in _dict:
args['element'] = _dict.get('element')
else:
raise ValueError(
'Required property \'element\' not present in WordError JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'element') and self.element is not None:
_dict['element'] = self.element
return _dict
def __str__(self):
"""Return a `str` version of this WordError object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class Words(object):
"""
Words.
:attr list[Word] words: Information about each word in the custom model's words resource. The array is empty if the custom model has no words.
"""
def __init__(self, words):
"""
Initialize a Words object.
:param list[Word] words: Information about each word in the custom model's words resource. The array is empty if the custom model has no words.
"""
self.words = words
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Words object from a json dictionary."""
args = {}
if 'words' in _dict:
args['words'] = [Word._from_dict(x) for x in _dict.get('words')]
else:
raise ValueError(
'Required property \'words\' not present in Words JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'words') and self.words is not None:
_dict['words'] = [x._to_dict() for x in self.words]
return _dict
def __str__(self):
"""Return a `str` version of this Words object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other