# coding: utf-8
# Copyright 2018 IBM All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The IBM® Speech to Text service provides APIs that use IBM's speech-recognition
capabilities to produce transcripts of spoken audio. The service can transcribe speech
from various languages and audio formats. In addition to basic transcription, the service
can produce detailed information about many different aspects of the audio. For most
languages, the service supports two sampling rates, broadband and narrowband. It returns
all JSON response content in the UTF-8 character set.
For speech recognition, the service supports synchronous and asynchronous HTTP
Representational State Transfer (REST) interfaces. It also supports a WebSocket interface
that provides a full-duplex, low-latency communication channel: Clients send requests and
audio to the service and receive results over a single connection asynchronously.
The service also offers two customization interfaces. Use language model customization to
expand the vocabulary of a base model with domain-specific terminology. Use acoustic model
customization to adapt a base model for the acoustic characteristics of your audio. For
language model customization, the service also supports grammars. A grammar is a formal
language specification that lets you restrict the phrases that the service can recognize.
Language model customization is generally available for production use with most supported
languages. Acoustic model customization is beta functionality that is available for all
supported languages.
"""
from __future__ import absolute_import
import json
from .common import get_sdk_headers
from ibm_cloud_sdk_core import BaseService
##############################################################################
# Service
##############################################################################
[docs]class SpeechToTextV1(BaseService):
"""The Speech to Text V1 service."""
default_url = 'https://stream.watsonplatform.net/speech-to-text/api'
def __init__(
self,
url=default_url,
username=None,
password=None,
iam_apikey=None,
iam_access_token=None,
iam_url=None,
iam_client_id=None,
iam_client_secret=None,
icp4d_access_token=None,
icp4d_url=None,
authentication_type=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/speech-to-text/api").
The base url may differ between IBM Cloud 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 IBM Cloud. When running on
IBM Cloud, 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 IBM Cloud. When running on
IBM Cloud, the credentials will be automatically loaded from the
`VCAP_SERVICES` environment variable.
:param str iam_apikey: An API key that can be used to request IAM tokens. If
this API key is provided, the SDK will manage the token and handle the
refreshing.
:param str iam_access_token: An IAM access token is fully managed by the application.
Responsibility falls on the application to refresh the token, either before
it expires or reactively upon receiving a 401 from the service as any requests
made with an expired token will fail.
:param str iam_url: An optional URL for the IAM service API. Defaults to
'https://iam.cloud.ibm.com/identity/token'.
:param str iam_client_id: An optional client_id value to use when interacting with the IAM service.
:param str iam_client_secret: An optional client_secret value to use when interacting with the IAM service.
:param str icp4d_access_token: A ICP4D(IBM Cloud Pak for Data) 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 icp4d_url: In order to use an SDK-managed token with ICP4D authentication, this
URL must be passed in.
:param str authentication_type: Specifies the authentication pattern to use. Values that it
takes are basic, iam or icp4d.
"""
BaseService.__init__(
self,
vcap_services_name='speech_to_text',
url=url,
username=username,
password=password,
iam_apikey=iam_apikey,
iam_access_token=iam_access_token,
iam_url=iam_url,
iam_client_id=iam_client_id,
iam_client_secret=iam_client_secret,
use_vcap_services=True,
display_name='Speech to Text',
icp4d_access_token=icp4d_access_token,
icp4d_url=icp4d_url,
authentication_type=authentication_type)
#########################
# Models
#########################
[docs] def list_models(self, **kwargs):
"""
List models.
Lists all language models that are available for use with the service. The
information includes the name of the model and its minimum sampling rate in Hertz,
among other things.
**See also:** [Languages and
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'list_models')
headers.update(sdk_headers)
url = '/v1/models'
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def get_model(self, model_id, **kwargs):
"""
Get a model.
Gets information for a single specified language model that is available for use
with the service. The information includes the name of the model and its minimum
sampling rate in Hertz, among other things.
**See also:** [Languages and
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
:param str model_id: The identifier of the model in the form of its name from the
output of the **Get a model** method.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if model_id is None:
raise ValueError('model_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'get_model')
headers.update(sdk_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
#########################
# Synchronous
#########################
[docs] def recognize(self,
audio,
model=None,
language_customization_id=None,
acoustic_customization_id=None,
base_model_version=None,
customization_weight=None,
inactivity_timeout=None,
keywords=None,
keywords_threshold=None,
max_alternatives=None,
word_alternatives_threshold=None,
word_confidence=None,
timestamps=None,
profanity_filter=None,
smart_formatting=None,
speaker_labels=None,
customization_id=None,
grammar_name=None,
redaction=None,
content_type=None,
audio_metrics=None,
**kwargs):
"""
Recognize audio.
Sends audio and returns transcription results for a recognition request. You can
pass a maximum of 100 MB and a minimum of 100 bytes of audio with a request. The
service automatically detects the endianness of the incoming audio and, for audio
that includes multiple channels, downmixes the audio to one-channel mono during
transcoding. The method returns only final results; to enable interim results, use
the WebSocket API. (With the `curl` command, use the `--data-binary` option to
upload the file for the request.)
**See also:** [Making a basic HTTP
request](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-http#HTTP-basic).
### Streaming mode
For requests to transcribe live audio as it becomes available, you must set the
`Transfer-Encoding` header to `chunked` to use streaming mode. In streaming mode,
the service closes the connection (status code 408) if it does not receive at
least 15 seconds of audio (including silence) in any 30-second period. The service
also closes the connection (status code 400) if it detects no speech for
`inactivity_timeout` seconds of streaming audio; use the `inactivity_timeout`
parameter to change the default of 30 seconds.
**See also:**
* [Audio
transmission](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#transmission)
*
[Timeouts](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts)
### Audio formats (content types)
The service accepts audio in the following formats (MIME types).
* For formats that are labeled **Required**, you must use the `Content-Type`
header with the request to specify the format of the audio.
* For all other formats, you can omit the `Content-Type` header or specify
`application/octet-stream` with the header to have the service automatically
detect the format of the audio. (With the `curl` command, you can specify either
`\"Content-Type:\"` or `\"Content-Type: application/octet-stream\"`.)
Where indicated, the format that you specify must include the sampling rate and
can optionally include the number of channels and the endianness of the audio.
* `audio/alaw` (**Required.** Specify the sampling rate (`rate`) of the audio.)
* `audio/basic` (**Required.** Use only with narrowband models.)
* `audio/flac`
* `audio/g729` (Use only with narrowband models.)
* `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the
number of channels (`channels`) and endianness (`endianness`) of the audio.)
* `audio/mp3`
* `audio/mpeg`
* `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.)
* `audio/ogg` (The service automatically detects the codec of the input audio.)
* `audio/ogg;codecs=opus`
* `audio/ogg;codecs=vorbis`
* `audio/wav` (Provide audio with a maximum of nine channels.)
* `audio/webm` (The service automatically detects the codec of the input audio.)
* `audio/webm;codecs=opus`
* `audio/webm;codecs=vorbis`
The sampling rate of the audio must match the sampling rate of the model for the
recognition request: for broadband models, at least 16 kHz; for narrowband models,
at least 8 kHz. If the sampling rate of the audio is higher than the minimum
required rate, the service down-samples the audio to the appropriate rate. If the
sampling rate of the audio is lower than the minimum required rate, the request
fails.
**See also:** [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#audio-formats).
### Multipart speech recognition
**Note:** The Watson SDKs do not support multipart speech recognition.
The HTTP `POST` method of the service also supports multipart speech recognition.
With multipart requests, you pass all audio data as multipart form data. You
specify some parameters as request headers and query parameters, but you pass JSON
metadata as form data to control most aspects of the transcription. You can use
multipart recognition to pass multiple audio files with a single request.
Use the multipart approach with browsers for which JavaScript is disabled or when
the parameters used with the request are greater than the 8 KB limit imposed by
most HTTP servers and proxies. You can encounter this limit, for example, if you
want to spot a very large number of keywords.
**See also:** [Making a multipart HTTP
request](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-http#HTTP-multi).
:param file audio: The audio to transcribe.
:param str model: The identifier of the model that is to be used for the
recognition request. See [Languages and
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
:param str language_customization_id: The customization ID (GUID) of a custom
language model that is to be used with the recognition request. The base model of
the specified custom language model must match the model specified with the
`model` parameter. You must make the request with credentials for the instance of
the service that owns the custom model. By default, no custom language model is
used. See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
**Note:** Use this parameter instead of the deprecated `customization_id`
parameter.
:param str acoustic_customization_id: The customization ID (GUID) of a custom
acoustic model that is to be used with the recognition request. The base model of
the specified custom acoustic model must match the model specified with the
`model` parameter. You must make the request with credentials for the instance of
the service that owns the custom model. By default, no custom acoustic model is
used. See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
:param str base_model_version: The version of the specified base model that is to
be used with the recognition request. Multiple versions of a base model can exist
when a model is updated for internal improvements. The parameter is intended
primarily for use with custom models that have been upgraded for a new base model.
The default value depends on whether the parameter is used with or without a
custom model. See [Base model
version](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#version).
:param float customization_weight: If you specify the customization ID (GUID) of a
custom language model with the recognition request, the customization weight tells
the service how much weight to give to words from the custom language model
compared to those from the base model for the current request.
Specify a value between 0.0 and 1.0. Unless a different customization weight was
specified for the custom model when it was trained, the default value is 0.3. A
customization weight that you specify overrides a weight that was specified when
the custom model was trained.
The default value yields the best performance in general. Assign a higher value if
your audio makes frequent use of OOV words from the custom model. Use caution when
setting the weight: a higher value can improve the accuracy of phrases from the
custom model's domain, but it can negatively affect performance on non-domain
phrases.
See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
:param int inactivity_timeout: The time in seconds after which, if only silence
(no speech) is detected in streaming audio, the connection is closed with a 400
error. The parameter is useful for stopping audio submission from a live
microphone when a user simply walks away. Use `-1` for infinity. See [Inactivity
timeout](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts-inactivity).
:param list[str] keywords: An array of keyword strings to spot in the audio. Each
keyword string can include one or more string tokens. Keywords are spotted only in
the final results, not in interim hypotheses. If you specify any keywords, you
must also specify a keywords threshold. You can spot a maximum of 1000 keywords.
Omit the parameter or specify an empty array if you do not need to spot keywords.
See [Keyword
spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
:param float keywords_threshold: A confidence value that is the lower bound for
spotting a keyword. A word is considered to match a keyword if its confidence is
greater than or equal to the threshold. Specify a probability between 0.0 and 1.0.
If you specify a threshold, you must also specify one or more keywords. The
service performs no keyword spotting if you omit either parameter. See [Keyword
spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
:param int max_alternatives: The maximum number of alternative transcripts that
the service is to return. By default, the service returns a single transcript. If
you specify a value of `0`, the service uses the default value, `1`. See [Maximum
alternatives](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#max_alternatives).
:param float word_alternatives_threshold: A confidence value that is the lower
bound for identifying a hypothesis as a possible word alternative (also known as
\"Confusion Networks\"). An alternative word is considered if its confidence is
greater than or equal to the threshold. Specify a probability between 0.0 and 1.0.
By default, the service computes no alternative words. See [Word
alternatives](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_alternatives).
:param bool word_confidence: If `true`, the service returns a confidence measure
in the range of 0.0 to 1.0 for each word. By default, the service returns no word
confidence scores. See [Word
confidence](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_confidence).
:param bool timestamps: If `true`, the service returns time alignment for each
word. By default, no timestamps are returned. See [Word
timestamps](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_timestamps).
:param bool profanity_filter: If `true`, the service filters profanity from all
output except for keyword results by replacing inappropriate words with a series
of asterisks. Set the parameter to `false` to return results with no censoring.
Applies to US English transcription only. See [Profanity
filtering](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#profanity_filter).
:param bool smart_formatting: If `true`, the service converts dates, times, series
of digits and numbers, phone numbers, currency values, and internet addresses into
more readable, conventional representations in the final transcript of a
recognition request. For US English, the service also converts certain keyword
strings to punctuation symbols. By default, the service performs no smart
formatting.
**Note:** Applies to US English, Japanese, and Spanish transcription only.
See [Smart
formatting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#smart_formatting).
: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, the service returns no speaker labels. Setting `speaker_labels` to `true`
forces the `timestamps` parameter to be `true`, regardless of whether you specify
`false` for the parameter.
**Note:** Applies to US English, Japanese, and Spanish transcription only. To
determine whether a language model supports speaker labels, you can also use the
**Get a model** method and check that the attribute `speaker_labels` is set to
`true`.
See [Speaker
labels](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#speaker_labels).
:param str customization_id: **Deprecated.** Use the `language_customization_id`
parameter to specify the customization ID (GUID) of a custom language model that
is to be used with the recognition request. Do not specify both parameters with a
request.
:param str grammar_name: The name of a grammar that is to be used with the
recognition request. If you specify a grammar, you must also use the
`language_customization_id` parameter to specify the name of the custom language
model for which the grammar is defined. The service recognizes only strings that
are recognized by the specified grammar; it does not recognize other custom words
from the model's words resource. See
[Grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#grammars-input).
:param bool redaction: If `true`, the service redacts, or masks, numeric data from
final transcripts. The feature redacts any number that has three or more
consecutive digits by replacing each digit with an `X` character. It is intended
to redact sensitive numeric data, such as credit card numbers. By default, the
service performs no redaction.
When you enable redaction, the service automatically enables smart formatting,
regardless of whether you explicitly disable that feature. To ensure maximum
security, the service also disables keyword spotting (ignores the `keywords` and
`keywords_threshold` parameters) and returns only a single final transcript
(forces the `max_alternatives` parameter to be `1`).
**Note:** Applies to US English, Japanese, and Korean transcription only.
See [Numeric
redaction](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#redaction).
:param bool audio_metrics: If `true`, requests detailed information about the
signal characteristics of the input audio. The service returns audio metrics with
the final transcription results. By default, the service returns no audio metrics.
:param str content_type: The format (MIME type) of the audio. For more information
about specifying an audio format, see **Audio formats (content types)** in the
method description.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if audio is None:
raise ValueError('audio must be provided')
headers = {'Content-Type': content_type}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'recognize')
headers.update(sdk_headers)
params = {
'model': model,
'language_customization_id': language_customization_id,
'acoustic_customization_id': acoustic_customization_id,
'base_model_version': base_model_version,
'customization_weight': customization_weight,
'inactivity_timeout': inactivity_timeout,
'keywords': self._convert_list(keywords),
'keywords_threshold': keywords_threshold,
'max_alternatives': max_alternatives,
'word_alternatives_threshold': word_alternatives_threshold,
'word_confidence': word_confidence,
'timestamps': timestamps,
'profanity_filter': profanity_filter,
'smart_formatting': smart_formatting,
'speaker_labels': speaker_labels,
'customization_id': customization_id,
'grammar_name': grammar_name,
'redaction': redaction,
'audio_metrics': audio_metrics
}
data = audio
url = '/v1/recognize'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return response
#########################
# Asynchronous
#########################
[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.
**See also:** [Registering a callback
URL](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#register).
:param str callback_url: An HTTP or HTTPS URL to which callback notifications are
to be sent. To be white-listed, the URL must successfully echo the challenge
string during URL verification. During verification, the client can also check the
signature that the service sends in the `X-Callback-Signature` header to verify
the origin of the request.
:param str user_secret: A user-specified string that the service uses to generate
the HMAC-SHA1 signature that it sends via the `X-Callback-Signature` header. The
service includes the header during URL verification and with every notification
sent to the callback URL. It calculates the signature over the payload of the
notification. If you omit the parameter, the service does not send the header.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if callback_url is None:
raise ValueError('callback_url must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'register_callback')
headers.update(sdk_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.
**See also:** [Unregistering a callback
URL](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#unregister).
:param str callback_url: The callback URL that is to be unregistered.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if callback_url is None:
raise ValueError('callback_url must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'unregister_callback')
headers.update(sdk_headers)
params = {'callback_url': callback_url}
url = '/v1/unregister_callback'
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=False)
return response
[docs] def create_job(self,
audio,
model=None,
callback_url=None,
events=None,
user_token=None,
results_ttl=None,
language_customization_id=None,
acoustic_customization_id=None,
base_model_version=None,
customization_weight=None,
inactivity_timeout=None,
keywords=None,
keywords_threshold=None,
max_alternatives=None,
word_alternatives_threshold=None,
word_confidence=None,
timestamps=None,
profanity_filter=None,
smart_formatting=None,
speaker_labels=None,
customization_id=None,
grammar_name=None,
redaction=None,
content_type=None,
processing_metrics=None,
processing_metrics_interval=None,
audio_metrics=None,
**kwargs):
"""
Create a job.
Creates a job for a new asynchronous recognition request. The job is owned by the
instance of the service whose credentials are used to create it. How you learn the
status and results of a job depends on the parameters you include with the job
creation request:
* By callback notification: Include the `callback_url` parameter to specify a URL
to which the service is to send callback notifications when the status of the job
changes. Optionally, you can also include the `events` and `user_token` parameters
to subscribe to specific events and to specify a string that is to be included
with each notification for the job.
* By polling the service: Omit the `callback_url`, `events`, and `user_token`
parameters. You must then use the **Check jobs** or **Check a job** methods to
check the status of the job, using the latter to retrieve the results when the job
is complete.
The two approaches are not mutually exclusive. You can poll the service for job
status or obtain results from the service manually even if you include a callback
URL. In both cases, you can include the `results_ttl` parameter to specify how
long the results are to remain available after the job is complete. Using the
HTTPS **Check a job** method to retrieve results is more secure than receiving
them via callback notification over HTTP because it provides confidentiality in
addition to authentication and data integrity.
The method supports the same basic parameters as other HTTP and WebSocket
recognition requests. It also supports the following parameters specific to the
asynchronous interface:
* `callback_url`
* `events`
* `user_token`
* `results_ttl`
You can pass a maximum of 1 GB and a minimum of 100 bytes of audio with a request.
The service automatically detects the endianness of the incoming audio and, for
audio that includes multiple channels, downmixes the audio to one-channel mono
during transcoding. The method returns only final results; to enable interim
results, use the WebSocket API. (With the `curl` command, use the `--data-binary`
option to upload the file for the request.)
**See also:** [Creating a
job](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#create).
### Streaming mode
For requests to transcribe live audio as it becomes available, you must set the
`Transfer-Encoding` header to `chunked` to use streaming mode. In streaming mode,
the service closes the connection (status code 408) if it does not receive at
least 15 seconds of audio (including silence) in any 30-second period. The service
also closes the connection (status code 400) if it detects no speech for
`inactivity_timeout` seconds of streaming audio; use the `inactivity_timeout`
parameter to change the default of 30 seconds.
**See also:**
* [Audio
transmission](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#transmission)
*
[Timeouts](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts)
### Audio formats (content types)
The service accepts audio in the following formats (MIME types).
* For formats that are labeled **Required**, you must use the `Content-Type`
header with the request to specify the format of the audio.
* For all other formats, you can omit the `Content-Type` header or specify
`application/octet-stream` with the header to have the service automatically
detect the format of the audio. (With the `curl` command, you can specify either
`\"Content-Type:\"` or `\"Content-Type: application/octet-stream\"`.)
Where indicated, the format that you specify must include the sampling rate and
can optionally include the number of channels and the endianness of the audio.
* `audio/alaw` (**Required.** Specify the sampling rate (`rate`) of the audio.)
* `audio/basic` (**Required.** Use only with narrowband models.)
* `audio/flac`
* `audio/g729` (Use only with narrowband models.)
* `audio/l16` (**Required.** Specify the sampling rate (`rate`) and optionally the
number of channels (`channels`) and endianness (`endianness`) of the audio.)
* `audio/mp3`
* `audio/mpeg`
* `audio/mulaw` (**Required.** Specify the sampling rate (`rate`) of the audio.)
* `audio/ogg` (The service automatically detects the codec of the input audio.)
* `audio/ogg;codecs=opus`
* `audio/ogg;codecs=vorbis`
* `audio/wav` (Provide audio with a maximum of nine channels.)
* `audio/webm` (The service automatically detects the codec of the input audio.)
* `audio/webm;codecs=opus`
* `audio/webm;codecs=vorbis`
The sampling rate of the audio must match the sampling rate of the model for the
recognition request: for broadband models, at least 16 kHz; for narrowband models,
at least 8 kHz. If the sampling rate of the audio is higher than the minimum
required rate, the service down-samples the audio to the appropriate rate. If the
sampling rate of the audio is lower than the minimum required rate, the request
fails.
**See also:** [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#audio-formats).
:param file audio: The audio to transcribe.
:param str model: The identifier of the model that is to be used for the
recognition request. See [Languages and
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-models#models).
:param str callback_url: A URL to which callback notifications are to be sent. The
URL must already be successfully white-listed by using the **Register a callback**
method. You can include the same callback URL with any number of job creation
requests. Omit the parameter to poll the service for job completion and results.
Use the `user_token` parameter to specify a unique user-specified string with each
job to differentiate the callback notifications for the jobs.
:param str events: If the job includes a callback URL, a comma-separated list of
notification events to which to subscribe. Valid events are
* `recognitions.started` generates a callback notification when the service begins
to process the job.
* `recognitions.completed` generates a callback notification when the job is
complete. You must use the **Check a job** method to retrieve the results before
they time out or are deleted.
* `recognitions.completed_with_results` generates a callback notification when the
job is complete. The notification includes the results of the request.
* `recognitions.failed` generates a callback notification if the service
experiences an error while processing the job.
The `recognitions.completed` and `recognitions.completed_with_results` events are
incompatible. You can specify only of the two events.
If the job includes a callback URL, omit the parameter to subscribe to the default
events: `recognitions.started`, `recognitions.completed`, and
`recognitions.failed`. If the job does not include a callback URL, omit the
parameter.
:param str user_token: If the job includes a callback URL, a user-specified string
that the service is to include with each callback notification for the job; the
token allows the user to maintain an internal mapping between jobs and
notification events. If the job does not include a callback URL, omit the
parameter.
:param int results_ttl: The number of minutes for which the results are to be
available after the job has finished. If not delivered via a callback, the results
must be retrieved within this time. Omit the parameter to use a time to live of
one week. The parameter is valid with or without a callback URL.
:param str language_customization_id: The customization ID (GUID) of a custom
language model that is to be used with the recognition request. The base model of
the specified custom language model must match the model specified with the
`model` parameter. You must make the request with credentials for the instance of
the service that owns the custom model. By default, no custom language model is
used. See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
**Note:** Use this parameter instead of the deprecated `customization_id`
parameter.
:param str acoustic_customization_id: The customization ID (GUID) of a custom
acoustic model that is to be used with the recognition request. The base model of
the specified custom acoustic model must match the model specified with the
`model` parameter. You must make the request with credentials for the instance of
the service that owns the custom model. By default, no custom acoustic model is
used. See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
:param str base_model_version: The version of the specified base model that is to
be used with the recognition request. Multiple versions of a base model can exist
when a model is updated for internal improvements. The parameter is intended
primarily for use with custom models that have been upgraded for a new base model.
The default value depends on whether the parameter is used with or without a
custom model. See [Base model
version](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#version).
:param float customization_weight: If you specify the customization ID (GUID) of a
custom language model with the recognition request, the customization weight tells
the service how much weight to give to words from the custom language model
compared to those from the base model for the current request.
Specify a value between 0.0 and 1.0. Unless a different customization weight was
specified for the custom model when it was trained, the default value is 0.3. A
customization weight that you specify overrides a weight that was specified when
the custom model was trained.
The default value yields the best performance in general. Assign a higher value if
your audio makes frequent use of OOV words from the custom model. Use caution when
setting the weight: a higher value can improve the accuracy of phrases from the
custom model's domain, but it can negatively affect performance on non-domain
phrases.
See [Custom
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#custom-input).
:param int inactivity_timeout: The time in seconds after which, if only silence
(no speech) is detected in streaming audio, the connection is closed with a 400
error. The parameter is useful for stopping audio submission from a live
microphone when a user simply walks away. Use `-1` for infinity. See [Inactivity
timeout](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#timeouts-inactivity).
:param list[str] keywords: An array of keyword strings to spot in the audio. Each
keyword string can include one or more string tokens. Keywords are spotted only in
the final results, not in interim hypotheses. If you specify any keywords, you
must also specify a keywords threshold. You can spot a maximum of 1000 keywords.
Omit the parameter or specify an empty array if you do not need to spot keywords.
See [Keyword
spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
:param float keywords_threshold: A confidence value that is the lower bound for
spotting a keyword. A word is considered to match a keyword if its confidence is
greater than or equal to the threshold. Specify a probability between 0.0 and 1.0.
If you specify a threshold, you must also specify one or more keywords. The
service performs no keyword spotting if you omit either parameter. See [Keyword
spotting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#keyword_spotting).
:param int max_alternatives: The maximum number of alternative transcripts that
the service is to return. By default, the service returns a single transcript. If
you specify a value of `0`, the service uses the default value, `1`. See [Maximum
alternatives](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#max_alternatives).
:param float word_alternatives_threshold: A confidence value that is the lower
bound for identifying a hypothesis as a possible word alternative (also known as
\"Confusion Networks\"). An alternative word is considered if its confidence is
greater than or equal to the threshold. Specify a probability between 0.0 and 1.0.
By default, the service computes no alternative words. See [Word
alternatives](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_alternatives).
:param bool word_confidence: If `true`, the service returns a confidence measure
in the range of 0.0 to 1.0 for each word. By default, the service returns no word
confidence scores. See [Word
confidence](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_confidence).
:param bool timestamps: If `true`, the service returns time alignment for each
word. By default, no timestamps are returned. See [Word
timestamps](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#word_timestamps).
:param bool profanity_filter: If `true`, the service filters profanity from all
output except for keyword results by replacing inappropriate words with a series
of asterisks. Set the parameter to `false` to return results with no censoring.
Applies to US English transcription only. See [Profanity
filtering](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#profanity_filter).
:param bool smart_formatting: If `true`, the service converts dates, times, series
of digits and numbers, phone numbers, currency values, and internet addresses into
more readable, conventional representations in the final transcript of a
recognition request. For US English, the service also converts certain keyword
strings to punctuation symbols. By default, the service performs no smart
formatting.
**Note:** Applies to US English, Japanese, and Spanish transcription only.
See [Smart
formatting](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#smart_formatting).
: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, the service returns no speaker labels. Setting `speaker_labels` to `true`
forces the `timestamps` parameter to be `true`, regardless of whether you specify
`false` for the parameter.
**Note:** Applies to US English, Japanese, and Spanish transcription only. To
determine whether a language model supports speaker labels, you can also use the
**Get a model** method and check that the attribute `speaker_labels` is set to
`true`.
See [Speaker
labels](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#speaker_labels).
:param str customization_id: **Deprecated.** Use the `language_customization_id`
parameter to specify the customization ID (GUID) of a custom language model that
is to be used with the recognition request. Do not specify both parameters with a
request.
:param str grammar_name: The name of a grammar that is to be used with the
recognition request. If you specify a grammar, you must also use the
`language_customization_id` parameter to specify the name of the custom language
model for which the grammar is defined. The service recognizes only strings that
are recognized by the specified grammar; it does not recognize other custom words
from the model's words resource. See
[Grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-input#grammars-input).
:param bool redaction: If `true`, the service redacts, or masks, numeric data from
final transcripts. The feature redacts any number that has three or more
consecutive digits by replacing each digit with an `X` character. It is intended
to redact sensitive numeric data, such as credit card numbers. By default, the
service performs no redaction.
When you enable redaction, the service automatically enables smart formatting,
regardless of whether you explicitly disable that feature. To ensure maximum
security, the service also disables keyword spotting (ignores the `keywords` and
`keywords_threshold` parameters) and returns only a single final transcript
(forces the `max_alternatives` parameter to be `1`).
**Note:** Applies to US English, Japanese, and Korean transcription only.
See [Numeric
redaction](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-output#redaction).
:param bool processing_metrics: If `true`, requests processing metrics about the
service's transcription of the input audio. The service returns processing metrics
at the interval specified by the `processing_metrics_interval` parameter. It also
returns processing metrics for transcription events, for example, for final and
interim results. By default, the service returns no processing metrics.
:param float processing_metrics_interval: Specifies the interval in real
wall-clock seconds at which the service is to return processing metrics. The
parameter is ignored unless the `processing_metrics` parameter is set to `true`.
The parameter accepts a minimum value of 0.1 seconds. The level of precision is
not restricted, so you can specify values such as 0.25 and 0.125.
The service does not impose a maximum value. If you want to receive processing
metrics only for transcription events instead of at periodic intervals, set the
value to a large number. If the value is larger than the duration of the audio,
the service returns processing metrics only for transcription events.
:param bool audio_metrics: If `true`, requests detailed information about the
signal characteristics of the input audio. The service returns audio metrics with
the final transcription results. By default, the service returns no audio metrics.
:param str content_type: The format (MIME type) of the audio. For more information
about specifying an audio format, see **Audio formats (content types)** in the
method description.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if audio is None:
raise ValueError('audio must be provided')
headers = {'Content-Type': content_type}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'create_job')
headers.update(sdk_headers)
params = {
'model': model,
'callback_url': callback_url,
'events': events,
'user_token': user_token,
'results_ttl': results_ttl,
'language_customization_id': language_customization_id,
'acoustic_customization_id': acoustic_customization_id,
'base_model_version': base_model_version,
'customization_weight': customization_weight,
'inactivity_timeout': inactivity_timeout,
'keywords': self._convert_list(keywords),
'keywords_threshold': keywords_threshold,
'max_alternatives': max_alternatives,
'word_alternatives_threshold': word_alternatives_threshold,
'word_confidence': word_confidence,
'timestamps': timestamps,
'profanity_filter': profanity_filter,
'smart_formatting': smart_formatting,
'speaker_labels': speaker_labels,
'customization_id': customization_id,
'grammar_name': grammar_name,
'redaction': redaction,
'processing_metrics': processing_metrics,
'processing_metrics_interval': processing_metrics_interval,
'audio_metrics': audio_metrics
}
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 check_jobs(self, **kwargs):
"""
Check jobs.
Returns the ID and status of the latest 100 outstanding jobs associated with the
credentials with which it is called. The method also returns the creation and
update times of each job, and, if a job was created with a callback URL and a user
token, the user token for the job. To obtain the results for a job whose status is
`completed` or not one of the latest 100 outstanding jobs, use the **Check a job**
method. A job and its results remain available until you delete them with the
**Delete a job** method or until the job's time to live expires, whichever comes
first.
**See also:** [Checking the status of the latest
jobs](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#jobs).
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'check_jobs')
headers.update(sdk_headers)
url = '/v1/recognitions'
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[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 use
credentials for the instance of the service that owns a job to list information
about it.
You can use the method to retrieve the results of any job, regardless of whether
it was submitted with a callback URL and the `recognitions.completed_with_results`
event, and you can retrieve the results multiple times for as long as they remain
available. Use the **Check jobs** method to request information about the most
recent jobs associated with the calling credentials.
**See also:** [Checking the status and retrieving the results of a
job](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#job).
:param str id: The identifier of the asynchronous job that is to be used for the
request. You must make the request with credentials for the instance of the
service that owns the job.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if id is None:
raise ValueError('id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'check_job')
headers.update(sdk_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 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 use credentials for the instance of the service that
owns a job to delete it.
**See also:** [Deleting a
job](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-async#delete-async).
:param str id: The identifier of the asynchronous job that is to be used for the
request. You must make the request with credentials for the instance of the
service that owns the job.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if id is None:
raise ValueError('id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'delete_job')
headers.update(sdk_headers)
url = '/v1/recognitions/{0}'.format(*self._encode_path_vars(id))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=False)
return response
#########################
# 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.
**See also:** [Create a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#createModel-language).
:param str name: A user-defined name for the new custom language model. Use a name
that is unique among all custom language models that you own. Use a localized name
that matches the language of the custom model. Use a name that describes the
domain of the custom model, such as `Medical custom model` or `Legal custom
model`.
:param str base_model_name: The name of the base language model that is to be
customized by the new custom language model. The new custom model can be used only
with the base model that it customizes.
To determine whether a base model supports language model customization, use the
**Get a model** method and check that the attribute `custom_language_model` is set
to `true`. You can also refer to [Language support for
customization](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customization#languageSupport).
:param str dialect: The dialect of the specified language that is to be used with
the custom language model. The parameter is meaningful only for Spanish models,
for which the service creates a custom language model that is suited for speech in
one of the following dialects:
* `es-ES` for Castilian Spanish (the default)
* `es-LA` for Latin American Spanish
* `es-US` for North American (Mexican) Spanish
A specified dialect must be valid for the base model. By default, the dialect
matches the language of the base model; for example, `en-US` for either of the US
English language models.
:param str description: A description of the new custom language model. Use a
localized description that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if name is None:
raise ValueError('name must be provided')
if base_model_name is None:
raise ValueError('base_model_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'create_language_model')
headers.update(sdk_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
[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.
**See also:** [Listing custom language
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language).
: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 that are owned by
the requesting credentials.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'list_language_models')
headers.update(sdk_headers)
params = {'language': language}
url = '/v1/customizations'
response = self.request(
method='GET',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[docs] def get_language_model(self, customization_id, **kwargs):
"""
Get a custom language model.
Gets information about a specified custom language model. You must use credentials
for the instance of the service that owns a model to list information about it.
**See also:** [Listing custom language
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#listModels-language).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'get_language_model')
headers.update(sdk_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
[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 or grammar to the model, is currently
being processed. You must use credentials for the instance of the service that
owns a model to delete it.
**See also:** [Deleting a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#deleteModel-language).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'delete_language_model')
headers.update(sdk_headers)
url = '/v1/customizations/{0}'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
[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 resources such as
corpora, grammars, and custom words. After adding, modifying, or deleting
resources for a custom language model, use this method to begin the actual
training of the model on the latest data. You can specify whether the custom
language model is to be trained with all words from its words resource or only
with words that were added or modified by the user directly. You must use
credentials for the instance of the service that owns a model to train it.
The training method is asynchronous. It can take on the order of minutes to
complete depending on the amount of data on which the service is being trained and
the current load on the service. The method returns an HTTP 200 response code to
indicate that the training process has begun.
You can monitor the status of the training by using the **Get a custom language
model** method to poll the model's status. Use a loop to check the status every 10
seconds. The method returns a `LanguageModel` object that includes `status` and
`progress` fields. A status of `available` means that the custom model is trained
and ready to use. The service cannot accept subsequent training requests or
requests to add new resources until the existing request completes.
**See also:** [Train the custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#trainModel-language).
### Training failures
Training can fail to start for the following reasons:
* The service is currently handling another request for the custom model, such as
another training request or a request to add a corpus or grammar to the model.
* No training data have been added to the custom model.
* The custom model contains one or more invalid corpora, grammars, or words (for
example, a custom word has an invalid sounds-like pronunciation). You can correct
the invalid resources or set the `strict` parameter to `false` to exclude the
invalid resources from the training. The model must contain at least one valid
resource for training to succeed.
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str word_type_to_add: The type of words from the custom language model's
words resource on which to train the model:
* `all` (the default) trains the model on all new words, regardless of whether
they were extracted from corpora or grammars or were added or modified by the
user.
* `user` trains the model only on new words that were added or modified by the
user directly. The model is not trained on new words extracted from corpora or
grammars.
:param float customization_weight: Specifies a customization weight for the custom
language model. The customization weight tells the service how much weight to give
to words from the custom language model compared to those from the base model for
speech recognition. Specify a value between 0.0 and 1.0; the default is 0.3.
The default value yields the best performance in general. Assign a higher value if
your audio makes frequent use of OOV words from the custom model. Use caution when
setting the weight: a higher value can improve the accuracy of phrases from the
custom model's domain, but it can negatively affect performance on non-domain
phrases.
The value that you assign is used for all recognition requests that use the model.
You can override it for any recognition request by specifying a customization
weight for that request.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'train_language_model')
headers.update(sdk_headers)
params = {
'word_type_to_add': word_type_to_add,
'customization_weight': customization_weight
}
url = '/v1/customizations/{0}/train'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[docs] def reset_language_model(self, customization_id, **kwargs):
"""
Reset a custom language model.
Resets a custom language model by removing all corpora, grammars, and words from
the model. Resetting a custom language model initializes the model to its state
when it was first created. Metadata such as the name and language of the model are
preserved, but the model's words resource is removed and must be re-created. You
must use credentials for the instance of the service that owns a model to reset
it.
**See also:** [Resetting a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageLanguageModels#resetModel-language).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'reset_language_model')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/reset'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST', url=url, headers=headers, accept_json=True)
return response
[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
**Get a custom language model** method to poll the model's status. The method
returns a `LanguageModel` object that includes `status` and `progress` fields. Use
a loop to check the status every 10 seconds. While it is being upgraded, the
custom model has the status `upgrading`. When the upgrade is complete, the model
resumes the status that it had prior to upgrade. The service cannot accept
subsequent requests for the model until the upgrade completes.
**See also:** [Upgrading a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeLanguage).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'upgrade_language_model')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/upgrade_model'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST', url=url, headers=headers, accept_json=True)
return response
#########################
# Custom corpora
#########################
[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.
**See also:** [Listing corpora for a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#listCorpora).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'list_corpora')
headers.update(sdk_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
[docs] def add_corpus(self,
customization_id,
corpus_name,
corpus_file,
allow_overwrite=None,
**kwargs):
"""
Add a corpus.
Adds a single corpus text file of new training data to a custom language model.
Use multiple requests to submit multiple corpus text files. You must use
credentials for the instance of the service that owns a model to add a corpus to
it. Adding a corpus does not affect the custom language model until you train the
model for the new data by using the **Train a custom language model** method.
Submit a plain text file that contains sample sentences from the domain of
interest to enable the service to extract words in context. The more sentences you
add that represent the context in which speakers use words from the domain, the
better the service's recognition accuracy.
The call returns an HTTP 201 response code if the corpus is valid. The service
then asynchronously processes the contents of the corpus and automatically
extracts new words that it finds. This can take on the order of a minute or two to
complete depending on the total number of words and the number of new words in the
corpus, as well as the current load on the service. You cannot submit requests to
add additional resources to the custom model or to train the model until the
service's analysis of the corpus for the current request completes. Use the **List
a corpus** method to check the status of the analysis.
The service auto-populates the model's words resource with words from the corpus
that are not found in its base vocabulary. These are referred to as
out-of-vocabulary (OOV) words. You can use the **List custom words** method to
examine the words resource. You can use other words method to eliminate typos and
modify how words are pronounced as needed.
To add a corpus file that has the same name as an existing corpus, set the
`allow_overwrite` parameter to `true`; otherwise, the request fails. Overwriting
an existing corpus causes the service to process the corpus text file and extract
OOV words anew. Before doing so, it removes any OOV words associated with the
existing corpus from the model's words resource unless they were also added by
another corpus or grammar, or they have been modified in some way with the **Add
custom words** or **Add a custom word** method.
The service limits the overall amount of data that you can add to a custom model
to a maximum of 10 million total words from all sources combined. Also, you can
add no more than 90 thousand custom (OOV) words to a model. This includes words
that the service extracts from corpora and grammars, and words that you add
directly.
**See also:**
* [Working with
corpora](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingCorpora)
* [Add a corpus to the custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addCorpus).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str corpus_name: The name of the new corpus for the custom language model.
Use a localized name that matches the language of the custom model and reflects
the contents of the corpus.
* Include a maximum of 128 characters in the name.
* Do not use characters that need to be URL-encoded. For example, do not use
spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs,
equals signs, questions marks, and so on in the name. (The service does not
prevent the use of these characters. But because they must be URL-encoded wherever
used, their use is strongly discouraged.)
* Do not use the name of an existing corpus or grammar that is already defined for
the custom model.
* Do not use the name `user`, which is reserved by the service to denote custom
words that are added or modified by the user.
* Do not use the name `base_lm` or `default_lm`. Both names are reserved for
future use by the service.
:param file corpus_file: A plain text file that contains the training data for the
corpus. Encode the file in UTF-8 if it contains non-ASCII characters; the service
assumes UTF-8 encoding if it encounters non-ASCII characters.
Make sure that you know the character encoding of the file. You must use that
encoding when working with the words in the custom language model. For more
information, see [Character
encoding](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
With the `curl` command, use the `--data-binary` option to upload the file for the
request.
:param bool allow_overwrite: If `true`, the specified corpus overwrites an
existing corpus with the same name. If `false`, the request fails if a corpus with
the same name already exists. The parameter has no effect if a corpus with the
same name does not already exist.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if corpus_name is None:
raise ValueError('corpus_name must be provided')
if corpus_file is None:
raise ValueError('corpus_file must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'add_corpus')
headers.update(sdk_headers)
params = {'allow_overwrite': allow_overwrite}
form_data = {}
form_data['corpus_file'] = (None, corpus_file, 'text/plain')
url = '/v1/customizations/{0}/corpora/{1}'.format(
*self._encode_path_vars(customization_id, corpus_name))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
files=form_data,
accept_json=True)
return response
[docs] def get_corpus(self, customization_id, corpus_name, **kwargs):
"""
Get a corpus.
Gets information about a corpus from a custom language model. The information
includes the total number of words and out-of-vocabulary (OOV) words, name, and
status of the corpus. You must use credentials for the instance of the service
that owns a model to list its corpora.
**See also:** [Listing corpora for a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#listCorpora).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str corpus_name: The name of the corpus for the custom language model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
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'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'get_corpus')
headers.update(sdk_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 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 that are associated with the corpus from the custom
model's words resource unless they were also added by another corpus or grammar,
or they were modified in some way with the **Add custom words** or **Add a custom
word** method. Removing a corpus does not affect the custom model until you train
the model with the **Train a custom language model** method. You must use
credentials for the instance of the service that owns a model to delete its
corpora.
**See also:** [Deleting a corpus from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageCorpora#deleteCorpus).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str corpus_name: The name of the corpus for the custom language model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if corpus_name is None:
raise ValueError('corpus_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'delete_corpus')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/corpora/{1}'.format(
*self._encode_path_vars(customization_id, corpus_name))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
#########################
# Custom words
#########################
[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 or are recognized by grammars. You can also indicate the
order in which the service is to return words; by default, the service lists words
in ascending alphabetical order. You must use credentials for the instance of the
service that owns a model to list information about its words.
**See also:** [Listing words from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#listWords).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str word_type: The type of words to be listed from the custom language
model's words resource:
* `all` (the default) shows all words.
* `user` shows only custom words that were added or modified by the user directly.
* `corpora` shows only OOV that were extracted from corpora.
* `grammars` shows only OOV words that are recognized by grammars.
:param str sort: Indicates the order in which the words are to be listed,
`alphabetical` or by `count`. You can prepend an optional `+` or `-` to an
argument to indicate whether the results are to be sorted in ascending or
descending order. By default, words are sorted in ascending alphabetical order.
For alphabetical ordering, the lexicographical precedence is numeric values,
uppercase letters, and lowercase letters. For count ordering, values with the same
count are ordered alphabetically. With the `curl` command, URL-encode the `+`
symbol as `%2B`.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'list_words')
headers.update(sdk_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
[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 from each
corpus or grammar that is added to the model. You can use this method to add
additional words or to modify existing words in the words resource. The words
resource for a model can contain a maximum of 90 thousand custom (OOV) words. This
includes words that the service extracts from corpora and grammars and words that
you add directly.
You must use credentials for the instance of the service that owns a model to add
or modify custom words for the model. Adding or modifying custom words does not
affect the custom model until you train the model for the new data by using the
**Train a custom language model** method.
You add custom words by providing a `CustomWords` object, which is an array of
`CustomWord` objects, one per word. You must use the object's `word` parameter to
identify the word that is to be added. You can also provide one or both of the
optional `sounds_like` and `display_as` fields for each word.
* The `sounds_like` field provides an array of one or more pronunciations for the
word. Use the parameter to specify how the word can be pronounced by users. Use
the parameter for words that are difficult to pronounce, foreign words, acronyms,
and so on. For example, you might specify that the word `IEEE` can sound like `i
triple e`. You can specify a maximum of five sounds-like pronunciations for a
word.
* The `display_as` field provides a different way of spelling the word in a
transcript. Use the parameter when you want the word to appear different from its
usual representation or from its spelling in training data. For example, you might
indicate that the word `IBM(trademark)` is to be displayed as `IBM™`.
If you add a custom word that already exists in the words resource for the custom
model, the new definition overwrites the existing data for the word. If the
service encounters an error with the input data, it returns a failure code and
does not add any of the words to the words resource.
The call returns an HTTP 201 response code if the input data is valid. It then
asynchronously processes the words to add them to the model's words resource. The
time that it takes for the analysis to complete depends on the number of new words
that you add but is generally faster than adding a corpus or grammar.
You can monitor the status of the request by using the **List a custom language
model** method to poll the model's status. Use a loop to check the status every 10
seconds. The method returns a `Customization` object that includes a `status`
field. A status of `ready` means that the words have been added to the custom
model. The service cannot accept requests to add new data or to train the model
until the existing request completes.
You can use the **List custom words** or **List a custom word** method to review
the words that you add. Words with an invalid `sounds_like` field include an
`error` field that describes the problem. You can use other words-related methods
to correct errors, eliminate typos, and modify how words are pronounced as needed.
**See also:**
* [Working with custom
words](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingWords)
* [Add words to the custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addWords).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param list[CustomWord] words: An array of `CustomWord` objects that provides
information about each custom word that is to be added to or updated in the custom
language model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if words is None:
raise ValueError('words must be provided')
words = [self._convert_model(x, CustomWord) for x in words]
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'add_words')
headers.update(sdk_headers)
data = {'words': words}
url = '/v1/customizations/{0}/words'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST',
url=url,
headers=headers,
json=data,
accept_json=True)
return response
[docs] def add_word(self,
customization_id,
word_name,
word=None,
sounds_like=None,
display_as=None,
**kwargs):
"""
Add a custom word.
Adds a custom word to a custom language model. The service populates the words
resource for a custom model with out-of-vocabulary (OOV) words from each corpus or
grammar that is added to the model. You can use this method to add a word or to
modify an existing word in the words resource. The words resource for a model can
contain a maximum of 90 thousand custom (OOV) words. This includes words that the
service extracts from corpora and grammars and words that you add directly.
You must use credentials for the instance of the service that owns a model to add
or modify a custom word for the model. Adding or modifying a custom word does not
affect the custom model until you train the model for the new data by using the
**Train a custom language model** method.
Use the `word_name` parameter to specify the custom word that is to be added or
modified. Use the `CustomWord` object to provide one or both of the optional
`sounds_like` and `display_as` fields for the word.
* The `sounds_like` field provides an array of one or more pronunciations for the
word. Use the parameter to specify how the word can be pronounced by users. Use
the parameter for words that are difficult to pronounce, foreign words, acronyms,
and so on. For example, you might specify that the word `IEEE` can sound like `i
triple e`. You can specify a maximum of five sounds-like pronunciations for a
word.
* The `display_as` field provides a different way of spelling the word in a
transcript. Use the parameter when you want the word to appear different from its
usual representation or from its spelling in training data. For example, you might
indicate that the word `IBM(trademark)` is to be displayed as `IBM™`.
If you add a custom word that already exists in the words resource for the custom
model, the new definition overwrites the existing data for the word. If the
service encounters an error, it does not add the word to the words resource. Use
the **List a custom word** method to review the word that you add.
**See also:**
* [Working with custom
words](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#workingWords)
* [Add words to the custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-languageCreate#addWords).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str word_name: The custom word that is to be added to or updated in the
custom language model. Do not include spaces in the word. Use a `-` (dash) or `_`
(underscore) to connect the tokens of compound words. URL-encode the word if it
includes non-ASCII characters. For more information, see [Character
encoding](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
:param str word: For the **Add custom words** method, you must specify the custom
word that is to be added to or updated in the custom model. Do not include spaces
in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of
compound words.
Omit this parameter for the **Add a custom word** method.
:param list[str] sounds_like: An array of sounds-like pronunciations for the
custom word. Specify how words that are difficult to pronounce, foreign words,
acronyms, and so on can be pronounced by users.
* For a word that is not in the service's base vocabulary, omit the parameter to
have the service automatically generate a sounds-like pronunciation for the word.
* For a word that is in the service's base vocabulary, use the parameter to
specify additional pronunciations for the word. You cannot override the default
pronunciation of a word; pronunciations you add augment the pronunciation from the
base vocabulary.
A word can have at most five sounds-like pronunciations. A pronunciation can
include at most 40 characters not including spaces.
:param str display_as: An alternative spelling for the custom word when it appears
in a transcript. Use the parameter when you want the word to have a spelling that
is different from its usual representation or from its spelling in corpora
training data.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'add_word')
headers.update(sdk_headers)
data = {
'word': word,
'sounds_like': sounds_like,
'display_as': display_as
}
url = '/v1/customizations/{0}/words/{1}'.format(
*self._encode_path_vars(customization_id, word_name))
response = self.request(
method='PUT', url=url, headers=headers, json=data, accept_json=True)
return response
[docs] def get_word(self, customization_id, word_name, **kwargs):
"""
Get a custom word.
Gets information about a custom word from a custom language model. You must use
credentials for the instance of the service that owns a model to list information
about its words.
**See also:** [Listing words from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#listWords).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str word_name: The custom word that is to be read from the custom language
model. URL-encode the word if it includes non-ASCII characters. For more
information, see [Character
encoding](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'get_word')
headers.update(sdk_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
[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.
**See also:** [Deleting a word from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageWords#deleteWord).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str word_name: The custom word that is to be deleted from the custom
language model. URL-encode the word if it includes non-ASCII characters. For more
information, see [Character
encoding](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-corporaWords#charEncoding).
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if word_name is None:
raise ValueError('word_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'delete_word')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/words/{1}'.format(
*self._encode_path_vars(customization_id, word_name))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
#########################
# Custom grammars
#########################
[docs] def list_grammars(self, customization_id, **kwargs):
"""
List grammars.
Lists information about all grammars from a custom language model. The information
includes the total number of out-of-vocabulary (OOV) words, name, and status of
each grammar. You must use credentials for the instance of the service that owns a
model to list its grammars.
**See also:** [Listing grammars from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'list_grammars')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/grammars'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def add_grammar(self,
customization_id,
grammar_name,
grammar_file,
content_type,
allow_overwrite=None,
**kwargs):
"""
Add a grammar.
Adds a single grammar file to a custom language model. Submit a plain text file in
UTF-8 format that defines the grammar. Use multiple requests to submit multiple
grammar files. You must use credentials for the instance of the service that owns
a model to add a grammar to it. Adding a grammar does not affect the custom
language model until you train the model for the new data by using the **Train a
custom language model** method.
The call returns an HTTP 201 response code if the grammar is valid. The service
then asynchronously processes the contents of the grammar and automatically
extracts new words that it finds. This can take a few seconds to complete
depending on the size and complexity of the grammar, as well as the current load
on the service. You cannot submit requests to add additional resources to the
custom model or to train the model until the service's analysis of the grammar for
the current request completes. Use the **Get a grammar** method to check the
status of the analysis.
The service populates the model's words resource with any word that is recognized
by the grammar that is not found in the model's base vocabulary. These are
referred to as out-of-vocabulary (OOV) words. You can use the **List custom
words** method to examine the words resource and use other words-related methods
to eliminate typos and modify how words are pronounced as needed.
To add a grammar that has the same name as an existing grammar, set the
`allow_overwrite` parameter to `true`; otherwise, the request fails. Overwriting
an existing grammar causes the service to process the grammar file and extract OOV
words anew. Before doing so, it removes any OOV words associated with the existing
grammar from the model's words resource unless they were also added by another
resource or they have been modified in some way with the **Add custom words** or
**Add a custom word** method.
The service limits the overall amount of data that you can add to a custom model
to a maximum of 10 million total words from all sources combined. Also, you can
add no more than 90 thousand OOV words to a model. This includes words that the
service extracts from corpora and grammars and words that you add directly.
**See also:**
* [Understanding
grammars](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-grammarUnderstand#grammarUnderstand)
* [Add a grammar to the custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-grammarAdd#addGrammar).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str grammar_name: The name of the new grammar for the custom language
model. Use a localized name that matches the language of the custom model and
reflects the contents of the grammar.
* Include a maximum of 128 characters in the name.
* Do not use characters that need to be URL-encoded. For example, do not use
spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs,
equals signs, questions marks, and so on in the name. (The service does not
prevent the use of these characters. But because they must be URL-encoded wherever
used, their use is strongly discouraged.)
* Do not use the name of an existing grammar or corpus that is already defined for
the custom model.
* Do not use the name `user`, which is reserved by the service to denote custom
words that are added or modified by the user.
* Do not use the name `base_lm` or `default_lm`. Both names are reserved for
future use by the service.
:param str grammar_file: A plain text file that contains the grammar in the format
specified by the `Content-Type` header. Encode the file in UTF-8 (ASCII is a
subset of UTF-8). Using any other encoding can lead to issues when compiling the
grammar or to unexpected results in decoding. The service ignores an encoding that
is specified in the header of the grammar.
With the `curl` command, use the `--data-binary` option to upload the file for the
request.
:param str content_type: The format (MIME type) of the grammar file:
* `application/srgs` for Augmented Backus-Naur Form (ABNF), which uses a
plain-text representation that is similar to traditional BNF grammars.
* `application/srgs+xml` for XML Form, which uses XML elements to represent the
grammar.
:param bool allow_overwrite: If `true`, the specified grammar overwrites an
existing grammar with the same name. If `false`, the request fails if a grammar
with the same name already exists. The parameter has no effect if a grammar with
the same name does not already exist.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if grammar_name is None:
raise ValueError('grammar_name must be provided')
if grammar_file is None:
raise ValueError('grammar_file must be provided')
if content_type is None:
raise ValueError('content_type must be provided')
headers = {'Content-Type': content_type}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'add_grammar')
headers.update(sdk_headers)
params = {'allow_overwrite': allow_overwrite}
data = grammar_file
url = '/v1/customizations/{0}/grammars/{1}'.format(
*self._encode_path_vars(customization_id, grammar_name))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return response
[docs] def get_grammar(self, customization_id, grammar_name, **kwargs):
"""
Get a grammar.
Gets information about a grammar from a custom language model. The information
includes the total number of out-of-vocabulary (OOV) words, name, and status of
the grammar. You must use credentials for the instance of the service that owns a
model to list its grammars.
**See also:** [Listing grammars from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#listGrammars).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str grammar_name: The name of the grammar for the custom language model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if grammar_name is None:
raise ValueError('grammar_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'get_grammar')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/grammars/{1}'.format(
*self._encode_path_vars(customization_id, grammar_name))
response = self.request(
method='GET', url=url, headers=headers, accept_json=True)
return response
[docs] def delete_grammar(self, customization_id, grammar_name, **kwargs):
"""
Delete a grammar.
Deletes an existing grammar from a custom language model. The service removes any
out-of-vocabulary (OOV) words associated with the grammar from the custom model's
words resource unless they were also added by another resource or they were
modified in some way with the **Add custom words** or **Add a custom word**
method. Removing a grammar does not affect the custom model until you train the
model with the **Train a custom language model** method. You must use credentials
for the instance of the service that owns a model to delete its grammar.
**See also:** [Deleting a grammar from a custom language
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageGrammars#deleteGrammar).
:param str customization_id: The customization ID (GUID) of the custom language
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str grammar_name: The name of the grammar for the custom language model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if grammar_name is None:
raise ValueError('grammar_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'delete_grammar')
headers.update(sdk_headers)
url = '/v1/customizations/{0}/grammars/{1}'.format(
*self._encode_path_vars(customization_id, grammar_name))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
#########################
# 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.
**See also:** [Create a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#createModel-acoustic).
:param str name: A user-defined name for the new custom acoustic model. Use a name
that is unique among all custom acoustic models that you own. Use a localized name
that matches the language of the custom model. Use a name that describes the
acoustic environment of the custom model, such as `Mobile custom model` or `Noisy
car custom model`.
:param str base_model_name: The name of the base language model that is to be
customized by the new custom acoustic model. The new custom model can be used only
with the base model that it customizes.
To determine whether a base model supports acoustic model customization, refer to
[Language support for
customization](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customization#languageSupport).
:param str description: A description of the new custom acoustic model. Use a
localized description that matches the language of the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if name is None:
raise ValueError('name must be provided')
if base_model_name is None:
raise ValueError('base_model_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'create_acoustic_model')
headers.update(sdk_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 list_acoustic_models(self, language=None, **kwargs):
"""
List custom acoustic models.
Lists information about all custom acoustic models that are owned by an instance
of the service. Use the `language` parameter to see all custom acoustic models for
the specified language. Omit the parameter to see all custom acoustic models for
all languages. You must use credentials for the instance of the service that owns
a model to list information about it.
**See also:** [Listing custom acoustic
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic).
: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 that are owned by
the requesting credentials.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'list_acoustic_models')
headers.update(sdk_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 get_acoustic_model(self, customization_id, **kwargs):
"""
Get a custom acoustic model.
Gets information about a specified custom acoustic model. You must use credentials
for the instance of the service that owns a model to list information about it.
**See also:** [Listing custom acoustic
models](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#listModels-acoustic).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'get_acoustic_model')
headers.update(sdk_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 delete_acoustic_model(self, customization_id, **kwargs):
"""
Delete a custom acoustic model.
Deletes an existing custom acoustic model. The custom model cannot be deleted if
another request, such as adding an audio resource to the model, is currently being
processed. You must use credentials for the instance of the service that owns a
model to delete it.
**See also:** [Deleting a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#deleteModel-acoustic).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'delete_acoustic_model')
headers.update(sdk_headers)
url = '/v1/acoustic_customizations/{0}'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
[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 **Get a custom acoustic
model** method to poll the model's status. Use a loop to check the status once a
minute. The method returns an `AcousticModel` object that includes `status` and
`progress` fields. A status of `available` indicates that the custom model is
trained and ready to use. The service cannot train a model while it is handling
another request for the model. The service cannot accept subsequent training
requests, or requests to add new audio resources, until the existing training
request completes.
You can use the optional `custom_language_model_id` parameter to specify the GUID
of a separately created custom language model that is to be used during training.
Train with a custom language model if you have verbatim transcriptions of the
audio files that you have added to the custom model or you have either corpora
(text files) or a list of words that are relevant to the contents of the audio
files. Both of the custom models must be based on the same version of the same
base model for training to succeed.
**See also:**
* [Train the custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#trainModel-acoustic)
* [Using custom acoustic and custom language models
together](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-useBoth#useBoth)
### Training failures
Training can fail to start for the following reasons:
* The service is currently handling another request for the custom model, such as
another training request or a request to add audio resources to the model.
* The custom model contains less than 10 minutes or more than 200 hours of audio
data.
* You passed an incompatible custom language model with the
`custom_language_model_id` query parameter. Both custom models must be based on
the same version of the same base model.
* The custom model contains one or more invalid audio resources. You can correct
the invalid audio resources or set the `strict` parameter to `false` to exclude
the invalid resources from the training. The model must contain at least one valid
resource for training to succeed.
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str custom_language_model_id: The customization ID (GUID) of a custom
language model that is to be used during training of the custom acoustic model.
Specify a custom language model that has been trained with verbatim transcriptions
of the audio resources or that contains words that are relevant to the contents of
the audio resources. The custom language model must be based on the same version
of the same base model as the custom acoustic model. The credentials specified
with the request must own both custom models.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'train_acoustic_model')
headers.update(sdk_headers)
params = {'custom_language_model_id': custom_language_model_id}
url = '/v1/acoustic_customizations/{0}/train'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
[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. The service
cannot reset a model while it is handling another request for the model. The
service cannot accept subsequent requests for the model until the existing reset
request completes. You must use credentials for the instance of the service that
owns a model to reset it.
**See also:** [Resetting a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAcousticModels#resetModel-acoustic).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'reset_acoustic_model')
headers.update(sdk_headers)
url = '/v1/acoustic_customizations/{0}/reset'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST', url=url, headers=headers, accept_json=True)
return response
[docs] def upgrade_acoustic_model(self,
customization_id,
custom_language_model_id=None,
force=None,
**kwargs):
"""
Upgrade a custom acoustic model.
Initiates the upgrade of a custom acoustic model to the latest version of its base
language model. The upgrade method is asynchronous. It can take on the order of
minutes or hours to complete depending on the amount of data in the custom model
and the current load on the service; typically, upgrade takes approximately twice
the length of the total audio contained in the custom model. A custom model must
be in the `ready` or `available` state to be upgraded. You must use credentials
for the instance of the service that owns a model to upgrade it.
The method returns an HTTP 200 response code to indicate that the upgrade process
has begun successfully. You can monitor the status of the upgrade by using the
**Get a custom acoustic model** method to poll the model's status. The method
returns an `AcousticModel` object that includes `status` and `progress` fields.
Use a loop to check the status once a minute. While it is being upgraded, the
custom model has the status `upgrading`. When the upgrade is complete, the model
resumes the status that it had prior to upgrade. The service cannot upgrade a
model while it is handling another request for the model. The service cannot
accept subsequent requests for the model until the existing upgrade request
completes.
If the custom acoustic model was trained with a separately created custom language
model, you must use the `custom_language_model_id` parameter to specify the GUID
of that custom language model. The custom language model must be upgraded before
the custom acoustic model can be upgraded. Omit the parameter if the custom
acoustic model was not trained with a custom language model.
**See also:** [Upgrading a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeAcoustic).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str custom_language_model_id: If the custom acoustic model was trained with
a custom language model, the customization ID (GUID) of that custom language
model. The custom language model must be upgraded before the custom acoustic model
can be upgraded. The credentials specified with the request must own both custom
models.
:param bool force: If `true`, forces the upgrade of a custom acoustic model for
which no input data has been modified since it was last trained. Use this
parameter only to force the upgrade of a custom acoustic model that is trained
with a custom language model, and only if you receive a 400 response code and the
message `No input data modified since last training`. See [Upgrading a custom
acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-customUpgrade#upgradeAcoustic).
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'upgrade_acoustic_model')
headers.update(sdk_headers)
params = {
'custom_language_model_id': custom_language_model_id,
'force': force
}
url = '/v1/acoustic_customizations/{0}/upgrade_model'.format(
*self._encode_path_vars(customization_id))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
accept_json=True)
return response
#########################
# Custom audio resources
#########################
[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.
**See also:** [Listing audio resources for a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#listAudio).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'list_audio')
headers.update(sdk_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
[docs] def add_audio(self,
customization_id,
audio_name,
audio_resource,
contained_content_type=None,
allow_overwrite=None,
content_type=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. You can add multiple
different audio resources at the same time. You must add a minimum of 10 minutes
and a maximum of 200 hours of audio that includes speech, not just silence, to a
custom acoustic model before you can train it. No audio resource, audio- or
archive-type, can be larger than 100 MB. To add an audio resource that has the
same name as an existing audio resource, set the `allow_overwrite` parameter to
`true`; otherwise, the request fails.
The method is asynchronous. It can take several seconds to complete depending on
the duration of the audio and, in the case of an archive file, the total number of
audio files being processed. The service returns a 201 response code if the audio
is valid. It then asynchronously analyzes the contents of the audio file or files
and automatically extracts information about the audio such as its length,
sampling rate, and encoding. You cannot submit requests to train or upgrade the
model until the service's analysis of all audio resources for current requests
completes.
To determine the status of the service's analysis of the audio, use the **Get an
audio resource** method to poll the status of the audio. The method accepts the
customization ID of the custom model and the name of the audio resource, and it
returns the status of the resource. Use a loop to check the status of the audio
every few seconds until it becomes `ok`.
**See also:** [Add audio to the custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-acoustic#addAudio).
### Content types for audio-type resources
You can add an individual audio file in any format that the service supports for
speech recognition. For an audio-type resource, use the `Content-Type` parameter
to specify the audio format (MIME type) of the audio file, including specifying
the sampling rate, channels, and endianness where indicated.
* `audio/alaw` (Specify the sampling rate (`rate`) of the audio.)
* `audio/basic` (Use only with narrowband models.)
* `audio/flac`
* `audio/g729` (Use only with narrowband models.)
* `audio/l16` (Specify the sampling rate (`rate`) and optionally the number of
channels (`channels`) and endianness (`endianness`) of the audio.)
* `audio/mp3`
* `audio/mpeg`
* `audio/mulaw` (Specify the sampling rate (`rate`) of the audio.)
* `audio/ogg` (The service automatically detects the codec of the input audio.)
* `audio/ogg;codecs=opus`
* `audio/ogg;codecs=vorbis`
* `audio/wav` (Provide audio with a maximum of nine channels.)
* `audio/webm` (The service automatically detects the codec of the input audio.)
* `audio/webm;codecs=opus`
* `audio/webm;codecs=vorbis`
The sampling rate of an audio file must match the sampling rate of the base model
for the custom model: for broadband models, at least 16 kHz; for narrowband
models, at least 8 kHz. If the sampling rate of the audio is higher than the
minimum required rate, the service down-samples the audio to the appropriate rate.
If the sampling rate of the audio is lower than the minimum required rate, the
service labels the audio file as `invalid`.
**See also:** [Audio
formats](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-audio-formats#audio-formats).
### Content types for archive-type resources
You can add an archive file (**.zip** or **.tar.gz** file) that contains audio
files in any format that the service supports for speech recognition. For an
archive-type resource, use the `Content-Type` parameter to specify the media type
of the archive file:
* `application/zip` for a **.zip** file
* `application/gzip` for a **.tar.gz** file.
When you add an archive-type resource, the `Contained-Content-Type` header is
optional depending on the format of the files that you are adding:
* For audio files of type `audio/alaw`, `audio/basic`, `audio/l16`, or
`audio/mulaw`, you must use the `Contained-Content-Type` header to specify the
format of the contained audio files. Include the `rate`, `channels`, and
`endianness` parameters where necessary. In this case, all audio files contained
in the archive file must have the same audio format.
* For audio files of all other types, you can omit the `Contained-Content-Type`
header. In this case, the audio files contained in the archive file can have any
of the formats not listed in the previous bullet. The audio files do not need to
have the same format.
Do not use the `Contained-Content-Type` header when adding an audio-type resource.
### Naming restrictions for embedded audio files
The name of an audio file that is contained in an archive-type resource can
include a maximum of 128 characters. This includes the file extension and all
elements of the name (for example, slashes).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str audio_name: The name of the new audio resource for the custom acoustic
model. Use a localized name that matches the language of the custom model and
reflects the contents of the resource.
* Include a maximum of 128 characters in the name.
* Do not use characters that need to be URL-encoded. For example, do not use
spaces, slashes, backslashes, colons, ampersands, double quotes, plus signs,
equals signs, questions marks, and so on in the name. (The service does not
prevent the use of these characters. But because they must be URL-encoded wherever
used, their use is strongly discouraged.)
* Do not use the name of an audio resource that has already been added to the
custom model.
:param file audio_resource: The audio resource that is to be added to the custom
acoustic model, an individual audio file or an archive file.
With the `curl` command, use the `--data-binary` option to upload the file for the
request.
:param str contained_content_type: **For an archive-type resource,** specify the
format of the audio files that are contained in the archive file if they are of
type `audio/alaw`, `audio/basic`, `audio/l16`, or `audio/mulaw`. Include the
`rate`, `channels`, and `endianness` parameters where necessary. In this case, all
audio files that are contained in the archive file must be of the indicated type.
For all other audio formats, you can omit the header. In this case, the audio
files can be of multiple types as long as they are not of the types listed in the
previous paragraph.
The parameter accepts all of the audio formats that are supported for use with
speech recognition. For more information, see **Content types for audio-type
resources** in the method description.
**For an audio-type resource,** omit the header.
:param bool allow_overwrite: If `true`, the specified audio resource overwrites an
existing audio resource with the same name. If `false`, the request fails if an
audio resource with the same name already exists. The parameter has no effect if
an audio resource with the same name does not already exist.
:param str content_type: For an audio-type resource, the format (MIME type) of the
audio. For more information, see **Content types for audio-type resources** in the
method description.
For an archive-type resource, the media type of the archive file. For more
information, see **Content types for archive-type resources** in the method
description.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if audio_name is None:
raise ValueError('audio_name must be provided')
if audio_resource is None:
raise ValueError('audio_resource must be provided')
headers = {
'Contained-Content-Type': contained_content_type,
'Content-Type': content_type
}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'add_audio')
headers.update(sdk_headers)
params = {'allow_overwrite': allow_overwrite}
data = audio_resource
url = '/v1/acoustic_customizations/{0}/audio/{1}'.format(
*self._encode_path_vars(customization_id, audio_name))
response = self.request(
method='POST',
url=url,
headers=headers,
params=params,
data=data,
accept_json=True)
return response
[docs] def get_audio(self, customization_id, audio_name, **kwargs):
"""
Get an audio resource.
Gets information about an audio resource from a custom acoustic model. The method
returns an `AudioListing` object whose fields depend on the type of audio resource
that you specify with the method's `audio_name` parameter:
* **For an audio-type resource,** the object's fields match those of an
`AudioResource` object: `duration`, `name`, `details`, and `status`.
* **For an archive-type resource,** the object includes a `container` field whose
fields match those of an `AudioResource` object. It also includes an `audio`
field, which contains an array of `AudioResource` objects that provides
information about the audio files that are contained in the archive.
The information includes the status of the specified audio resource. The status is
important for checking the service's analysis of a resource that you add to the
custom model.
* For an audio-type resource, the `status` field is located in the `AudioListing`
object.
* For an archive-type resource, the `status` field is located in the
`AudioResource` object that is returned in the `container` field.
You must use credentials for the instance of the service that owns a model to list
its audio resources.
**See also:** [Listing audio resources for a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#listAudio).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str audio_name: The name of the audio resource for the custom acoustic
model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
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'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'get_audio')
headers.update(sdk_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 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 service does
not allow deletion of individual files from an archive resource.
Removing an audio resource does not affect the custom model until you train the
model on its updated data by using the **Train a custom acoustic model** method.
You can delete an existing audio resource from a model while a different resource
is being added to the model. You must use credentials for the instance of the
service that owns a model to delete its audio resources.
**See also:** [Deleting an audio resource from a custom acoustic
model](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-manageAudio#deleteAudio).
:param str customization_id: The customization ID (GUID) of the custom acoustic
model that is to be used for the request. You must make the request with
credentials for the instance of the service that owns the custom model.
:param str audio_name: The name of the audio resource for the custom acoustic
model.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customization_id is None:
raise ValueError('customization_id must be provided')
if audio_name is None:
raise ValueError('audio_name must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1', 'delete_audio')
headers.update(sdk_headers)
url = '/v1/acoustic_customizations/{0}/audio/{1}'.format(
*self._encode_path_vars(customization_id, audio_name))
response = self.request(
method='DELETE', url=url, headers=headers, accept_json=True)
return response
#########################
# 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.
**See also:** [Information
security](https://cloud.ibm.com/docs/services/speech-to-text?topic=speech-to-text-information-security#information-security).
:param str customer_id: The customer ID for which all data is to be deleted.
:param dict headers: A `dict` containing the request headers
:return: A `DetailedResponse` containing the result, headers and HTTP status code.
:rtype: DetailedResponse
"""
if customer_id is None:
raise ValueError('customer_id must be provided')
headers = {}
if 'headers' in kwargs:
headers.update(kwargs.get('headers'))
sdk_headers = get_sdk_headers('speech_to_text', 'V1',
'delete_user_data')
headers.update(sdk_headers)
params = {'customer_id': customer_id}
url = '/v1/user_data'
response = self.request(
method='DELETE',
url=url,
headers=headers,
params=params,
accept_json=False)
return response
##############################################################################
# Models
##############################################################################
[docs]class AcousticModel(object):
"""
Information about an existing custom acoustic model.
:attr str customization_id: The customization ID (GUID) of the custom acoustic model.
The **Create a custom acoustic model** method returns only this field of the object;
it does not return the other fields.
:attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at
which the custom acoustic model was created. The value is provided in full ISO 8601
format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at
which the custom acoustic model was last modified. The `created` and `updated` fields
are equal when an acoustic model is first added but has yet to be updated. The value
is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
:attr str language: (optional) The language identifier of the custom acoustic model
(for example, `en-US`).
:attr list[str] versions: (optional) A list of the available versions of the custom
acoustic model. Each element of the array indicates a version of the base model with
which the custom model can be used. Multiple versions exist only if the custom model
has been upgraded; otherwise, only a single version is shown.
:attr str owner: (optional) The GUID of the credentials for the instance of the
service that owns the custom acoustic model.
:attr str name: (optional) The name of the custom acoustic model.
:attr str description: (optional) The description of the custom acoustic model.
:attr str base_model_name: (optional) The name of the language model for which the
custom acoustic model was created.
:attr str status: (optional) The current status of the custom acoustic model:
* `pending`: The model was created but is waiting either for valid training data to be
added or for the service to finish analyzing added data.
* `ready`: The model contains valid data and is ready to be trained. If the model
contains a mix of valid and invalid resources, you need to set the `strict` parameter
to `false` for the training to proceed.
* `training`: The model is currently being trained.
* `available`: The model is trained and ready to use.
* `upgrading`: The model is currently being upgraded.
* `failed`: Training of the model failed.
:attr int progress: (optional) A percentage that indicates the progress of the custom
acoustic model's current training. A value of `100` means that the model is fully
trained. **Note:** The `progress` field does not currently reflect the progress of the
training. The field changes from `0` to `100` when training is complete.
:attr str warnings: (optional) If the request included unknown parameters, the
following message: `Unexpected query parameter(s) ['parameters'] detected`, where
`parameters` is a list that includes a quoted string for each unknown parameter.
"""
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,
updated=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 updated: (optional) The date and time in Coordinated Universal Time
(UTC) at which the custom acoustic model was last modified. The `created` and
`updated` fields are equal when an acoustic model is first added but has yet to be
updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
:param str language: (optional) The language identifier of the custom acoustic
model (for example, `en-US`).
:param list[str] versions: (optional) A list of the available versions of the
custom acoustic model. Each element of the array indicates a version of the base
model with which the custom model can be used. Multiple versions exist only if the
custom model has been upgraded; otherwise, only a single version is shown.
:param str owner: (optional) The GUID of the credentials for the instance of the
service that owns the custom acoustic model.
:param str name: (optional) The name of the custom acoustic model.
:param str description: (optional) The description of the custom acoustic model.
:param str base_model_name: (optional) The name of the language model for which
the custom acoustic model was created.
:param str status: (optional) The current status of the custom acoustic model:
* `pending`: The model was created but is waiting either for valid training data
to be added or for the service to finish analyzing added data.
* `ready`: The model contains valid data and is ready to be trained. If the model
contains a mix of valid and invalid resources, you need to set the `strict`
parameter to `false` for the training to proceed.
* `training`: The model is currently being trained.
* `available`: The model is trained and ready to use.
* `upgrading`: The model is currently being upgraded.
* `failed`: Training of the model failed.
:param int progress: (optional) A percentage that indicates the progress of the
custom acoustic model's current training. A value of `100` means that the model is
fully trained. **Note:** The `progress` field does not currently reflect the
progress of the training. The field changes from `0` to `100` when training is
complete.
:param str warnings: (optional) If the request included unknown parameters, the
following message: `Unexpected query parameter(s) ['parameters'] detected`, where
`parameters` is a list that includes a quoted string for each unknown parameter.
"""
self.customization_id = customization_id
self.created = created
self.updated = updated
self.language = language
self.versions = versions
self.owner = owner
self.name = name
self.description = description
self.base_model_name = base_model_name
self.status = status
self.progress = progress
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AcousticModel object from a json dictionary."""
args = {}
validKeys = [
'customization_id', 'created', 'updated', 'language', 'versions',
'owner', 'name', 'description', 'base_model_name', 'status',
'progress', 'warnings'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AcousticModel: '
+ ', '.join(badKeys))
if 'customization_id' in _dict:
args['customization_id'] = _dict.get('customization_id')
else:
raise ValueError(
'Required property \'customization_id\' not present in AcousticModel JSON'
)
if 'created' in _dict:
args['created'] = _dict.get('created')
if 'updated' in _dict:
args['updated'] = _dict.get('updated')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'versions' in _dict:
args['versions'] = _dict.get('versions')
if 'owner' in _dict:
args['owner'] = _dict.get('owner')
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'base_model_name' in _dict:
args['base_model_name'] = _dict.get('base_model_name')
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'progress' in _dict:
args['progress'] = _dict.get('progress')
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
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, 'updated') and self.updated is not None:
_dict['updated'] = self.updated
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'versions') and self.versions is not None:
_dict['versions'] = self.versions
if hasattr(self, 'owner') and self.owner is not None:
_dict['owner'] = self.owner
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self,
'base_model_name') and self.base_model_name is not None:
_dict['base_model_name'] = self.base_model_name
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'progress') and self.progress is not None:
_dict['progress'] = self.progress
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __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):
"""
Information about existing custom acoustic models.
:attr list[AcousticModel] customizations: An array of `AcousticModel` objects that
provides information about each available custom acoustic model. The array is empty if
the requesting credentials own no custom acoustic models (if no language is specified)
or own no custom acoustic models for the specified language.
"""
def __init__(self, customizations):
"""
Initialize a AcousticModels object.
:param list[AcousticModel] customizations: An array of `AcousticModel` objects
that provides information about each available custom acoustic model. The array is
empty if the requesting credentials own no custom acoustic models (if no language
is specified) or own no custom acoustic models for the specified language.
"""
self.customizations = customizations
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AcousticModels object from a json dictionary."""
args = {}
validKeys = ['customizations']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AcousticModels: '
+ ', '.join(badKeys))
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):
"""
Information about an audio resource from a custom acoustic model.
:attr str type: (optional) The type of the audio resource:
* `audio` for an individual audio file
* `archive` for an archive (**.zip** or **.tar.gz**) file that contains audio files
* `undetermined` for a resource that the service cannot validate (for example, if the
user mistakenly passes a file that does not contain audio, such as a JPEG file).
:attr str codec: (optional) **For an audio-type resource,** the codec in which the
audio is encoded. Omitted for an archive-type resource.
:attr int frequency: (optional) **For an audio-type resource,** the sampling rate of
the audio in Hertz (samples per second). Omitted for an archive-type resource.
:attr str compression: (optional) **For an archive-type resource,** the format of the
compressed archive:
* `zip` for a **.zip** file
* `gzip` for a **.tar.gz** file
Omitted for an audio-type resource.
"""
def __init__(self, type=None, codec=None, frequency=None, compression=None):
"""
Initialize a AudioDetails object.
:param str type: (optional) The type of the audio resource:
* `audio` for an individual audio file
* `archive` for an archive (**.zip** or **.tar.gz**) file that contains audio
files
* `undetermined` for a resource that the service cannot validate (for example, if
the user mistakenly passes a file that does not contain audio, such as a JPEG
file).
:param str codec: (optional) **For an audio-type resource,** the codec in which
the audio is encoded. Omitted for an archive-type resource.
:param int frequency: (optional) **For an audio-type resource,** the sampling rate
of the audio in Hertz (samples per second). Omitted for an archive-type resource.
:param str compression: (optional) **For an archive-type resource,** the format of
the compressed archive:
* `zip` for a **.zip** file
* `gzip` for a **.tar.gz** file
Omitted for an audio-type resource.
"""
self.type = type
self.codec = codec
self.frequency = frequency
self.compression = compression
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioDetails object from a json dictionary."""
args = {}
validKeys = ['type', 'codec', 'frequency', 'compression']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioDetails: '
+ ', '.join(badKeys))
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):
"""
Information about an audio resource from a custom acoustic model.
:attr int duration: (optional) **For an audio-type resource,** the total seconds of
audio in the resource. Omitted for an archive-type resource.
:attr str name: (optional) **For an audio-type resource,** the user-specified name of
the resource. Omitted for an archive-type resource.
:attr AudioDetails details: (optional) **For an audio-type resource,** an
`AudioDetails` object that provides detailed information about the resource. The
object is empty until the service finishes processing the audio. Omitted for an
archive-type resource.
:attr str status: (optional) **For an audio-type resource,** the status of the
resource:
* `ok`: The service successfully analyzed the audio data. The data can be used to
train the custom model.
* `being_processed`: The service is still analyzing the audio data. The service cannot
accept requests to add new audio resources or to train the custom model until its
analysis is complete.
* `invalid`: The audio data is not valid for training the custom model (possibly
because it has the wrong format or sampling rate, or because it is corrupted).
Omitted for an archive-type resource.
:attr AudioResource container: (optional) **For an archive-type resource,** an object
of type `AudioResource` that provides information about the resource. Omitted for an
audio-type resource.
:attr list[AudioResource] audio: (optional) **For an archive-type resource,** an array
of `AudioResource` objects that provides information about the audio-type resources
that are contained in the resource. Omitted for an audio-type resource.
"""
def __init__(self,
duration=None,
name=None,
details=None,
status=None,
container=None,
audio=None):
"""
Initialize a AudioListing object.
:param int duration: (optional) **For an audio-type resource,** the total seconds
of audio in the resource. Omitted for an archive-type resource.
:param str name: (optional) **For an audio-type resource,** the user-specified
name of the resource. Omitted for an archive-type resource.
:param AudioDetails details: (optional) **For an audio-type resource,** an
`AudioDetails` object that provides detailed information about the resource. The
object is empty until the service finishes processing the audio. Omitted for an
archive-type resource.
:param str status: (optional) **For an audio-type resource,** the status of the
resource:
* `ok`: The service successfully analyzed the audio data. The data can be used to
train the custom model.
* `being_processed`: The service is still analyzing the audio data. The service
cannot accept requests to add new audio resources or to train the custom model
until its analysis is complete.
* `invalid`: The audio data is not valid for training the custom model (possibly
because it has the wrong format or sampling rate, or because it is corrupted).
Omitted for an archive-type resource.
:param AudioResource container: (optional) **For an archive-type resource,** an
object of type `AudioResource` that provides information about the resource.
Omitted for an audio-type resource.
:param list[AudioResource] audio: (optional) **For an archive-type resource,** an
array of `AudioResource` objects that provides information about the audio-type
resources that are contained in the resource. Omitted for an audio-type resource.
"""
self.duration = duration
self.name = name
self.details = details
self.status = status
self.container = container
self.audio = audio
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioListing object from a json dictionary."""
args = {}
validKeys = [
'duration', 'name', 'details', 'status', 'container', 'audio'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioListing: '
+ ', '.join(badKeys))
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 AudioMetrics(object):
"""
If audio metrics are requested, information about the signal characteristics of the
input audio.
:attr float sampling_interval: The interval in seconds (typically 0.1 seconds) at
which the service calculated the audio metrics. In other words, how often the service
calculated the metrics. A single unit in each histogram (see the
`AudioMetricsHistogramBin` object) is calculated based on a `sampling_interval` length
of audio.
:attr AudioMetricsDetails accumulated: Detailed information about the signal
characteristics of the input audio.
"""
def __init__(self, sampling_interval, accumulated):
"""
Initialize a AudioMetrics object.
:param float sampling_interval: The interval in seconds (typically 0.1 seconds) at
which the service calculated the audio metrics. In other words, how often the
service calculated the metrics. A single unit in each histogram (see the
`AudioMetricsHistogramBin` object) is calculated based on a `sampling_interval`
length of audio.
:param AudioMetricsDetails accumulated: Detailed information about the signal
characteristics of the input audio.
"""
self.sampling_interval = sampling_interval
self.accumulated = accumulated
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioMetrics object from a json dictionary."""
args = {}
validKeys = ['sampling_interval', 'accumulated']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioMetrics: '
+ ', '.join(badKeys))
if 'sampling_interval' in _dict:
args['sampling_interval'] = _dict.get('sampling_interval')
else:
raise ValueError(
'Required property \'sampling_interval\' not present in AudioMetrics JSON'
)
if 'accumulated' in _dict:
args['accumulated'] = AudioMetricsDetails._from_dict(
_dict.get('accumulated'))
else:
raise ValueError(
'Required property \'accumulated\' not present in AudioMetrics JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'sampling_interval') and self.sampling_interval is not None:
_dict['sampling_interval'] = self.sampling_interval
if hasattr(self, 'accumulated') and self.accumulated is not None:
_dict['accumulated'] = self.accumulated._to_dict()
return _dict
def __str__(self):
"""Return a `str` version of this AudioMetrics 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 AudioMetricsDetails(object):
"""
Detailed information about the signal characteristics of the input audio.
:attr bool final: If `true`, indicates the end of the audio stream, meaning that
transcription is complete. Currently, the field is always `true`. The service returns
metrics just once per audio stream. The results provide aggregated audio metrics that
pertain to the complete audio stream.
:attr float end_time: The end time in seconds of the block of audio to which the
metrics apply.
:attr float signal_to_noise_ratio: (optional) The signal-to-noise ratio (SNR) for the
audio signal. The value indicates the ratio of speech to noise in the audio. A valid
value lies in the range of 0 to 100 decibels (dB). The service omits the field if it
cannot compute the SNR for the audio.
:attr float speech_ratio: The ratio of speech to non-speech segments in the audio
signal. The value lies in the range of 0.0 to 1.0.
:attr float high_frequency_loss: The probability that the audio signal is missing the
upper half of its frequency content.
* A value close to 1.0 typically indicates artificially up-sampled audio, which
negatively impacts the accuracy of the transcription results.
* A value at or near 0.0 indicates that the audio signal is good and has a full
spectrum.
* A value around 0.5 means that detection of the frequency content is unreliable or
not available.
:attr list[AudioMetricsHistogramBin] direct_current_offset: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the cumulative direct
current (DC) component of the audio signal.
:attr list[AudioMetricsHistogramBin] clipping_rate: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the clipping rate for
the audio segments. The clipping rate is defined as the fraction of samples in the
segment that reach the maximum or minimum value that is offered by the audio
quantization range. The service auto-detects either a 16-bit Pulse-Code
Modulation(PCM) audio range (-32768 to +32767) or a unit range (-1.0 to +1.0). The
clipping rate is between 0.0 and 1.0, with higher values indicating possible
degradation of speech recognition.
:attr list[AudioMetricsHistogramBin] speech_level: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the signal level in
segments of the audio that contain speech. The signal level is computed as the
Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0
(minimum level) to 1.0 (maximum level).
:attr list[AudioMetricsHistogramBin] non_speech_level: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the signal level in
segments of the audio that do not contain speech. The signal level is computed as the
Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0
(minimum level) to 1.0 (maximum level).
"""
def __init__(self,
final,
end_time,
speech_ratio,
high_frequency_loss,
direct_current_offset,
clipping_rate,
speech_level,
non_speech_level,
signal_to_noise_ratio=None):
"""
Initialize a AudioMetricsDetails object.
:param bool final: If `true`, indicates the end of the audio stream, meaning that
transcription is complete. Currently, the field is always `true`. The service
returns metrics just once per audio stream. The results provide aggregated audio
metrics that pertain to the complete audio stream.
:param float end_time: The end time in seconds of the block of audio to which the
metrics apply.
:param float speech_ratio: The ratio of speech to non-speech segments in the audio
signal. The value lies in the range of 0.0 to 1.0.
:param float high_frequency_loss: The probability that the audio signal is missing
the upper half of its frequency content.
* A value close to 1.0 typically indicates artificially up-sampled audio, which
negatively impacts the accuracy of the transcription results.
* A value at or near 0.0 indicates that the audio signal is good and has a full
spectrum.
* A value around 0.5 means that detection of the frequency content is unreliable
or not available.
:param list[AudioMetricsHistogramBin] direct_current_offset: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the cumulative
direct current (DC) component of the audio signal.
:param list[AudioMetricsHistogramBin] clipping_rate: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the clipping rate
for the audio segments. The clipping rate is defined as the fraction of samples in
the segment that reach the maximum or minimum value that is offered by the audio
quantization range. The service auto-detects either a 16-bit Pulse-Code
Modulation(PCM) audio range (-32768 to +32767) or a unit range (-1.0 to +1.0). The
clipping rate is between 0.0 and 1.0, with higher values indicating possible
degradation of speech recognition.
:param list[AudioMetricsHistogramBin] speech_level: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the signal level in
segments of the audio that contain speech. The signal level is computed as the
Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range 0.0
(minimum level) to 1.0 (maximum level).
:param list[AudioMetricsHistogramBin] non_speech_level: An array of
`AudioMetricsHistogramBin` objects that defines a histogram of the signal level in
segments of the audio that do not contain speech. The signal level is computed as
the Root-Mean-Square (RMS) value in a decibel (dB) scale normalized to the range
0.0 (minimum level) to 1.0 (maximum level).
:param float signal_to_noise_ratio: (optional) The signal-to-noise ratio (SNR) for
the audio signal. The value indicates the ratio of speech to noise in the audio. A
valid value lies in the range of 0 to 100 decibels (dB). The service omits the
field if it cannot compute the SNR for the audio.
"""
self.final = final
self.end_time = end_time
self.signal_to_noise_ratio = signal_to_noise_ratio
self.speech_ratio = speech_ratio
self.high_frequency_loss = high_frequency_loss
self.direct_current_offset = direct_current_offset
self.clipping_rate = clipping_rate
self.speech_level = speech_level
self.non_speech_level = non_speech_level
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioMetricsDetails object from a json dictionary."""
args = {}
validKeys = [
'final', 'end_time', 'signal_to_noise_ratio', 'speech_ratio',
'high_frequency_loss', 'direct_current_offset', 'clipping_rate',
'speech_level', 'non_speech_level'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioMetricsDetails: '
+ ', '.join(badKeys))
if 'final' in _dict:
args['final'] = _dict.get('final')
else:
raise ValueError(
'Required property \'final\' not present in AudioMetricsDetails JSON'
)
if 'end_time' in _dict:
args['end_time'] = _dict.get('end_time')
else:
raise ValueError(
'Required property \'end_time\' not present in AudioMetricsDetails JSON'
)
if 'signal_to_noise_ratio' in _dict:
args['signal_to_noise_ratio'] = _dict.get('signal_to_noise_ratio')
if 'speech_ratio' in _dict:
args['speech_ratio'] = _dict.get('speech_ratio')
else:
raise ValueError(
'Required property \'speech_ratio\' not present in AudioMetricsDetails JSON'
)
if 'high_frequency_loss' in _dict:
args['high_frequency_loss'] = _dict.get('high_frequency_loss')
else:
raise ValueError(
'Required property \'high_frequency_loss\' not present in AudioMetricsDetails JSON'
)
if 'direct_current_offset' in _dict:
args['direct_current_offset'] = [
AudioMetricsHistogramBin._from_dict(x)
for x in (_dict.get('direct_current_offset'))
]
else:
raise ValueError(
'Required property \'direct_current_offset\' not present in AudioMetricsDetails JSON'
)
if 'clipping_rate' in _dict:
args['clipping_rate'] = [
AudioMetricsHistogramBin._from_dict(x)
for x in (_dict.get('clipping_rate'))
]
else:
raise ValueError(
'Required property \'clipping_rate\' not present in AudioMetricsDetails JSON'
)
if 'speech_level' in _dict:
args['speech_level'] = [
AudioMetricsHistogramBin._from_dict(x)
for x in (_dict.get('speech_level'))
]
else:
raise ValueError(
'Required property \'speech_level\' not present in AudioMetricsDetails JSON'
)
if 'non_speech_level' in _dict:
args['non_speech_level'] = [
AudioMetricsHistogramBin._from_dict(x)
for x in (_dict.get('non_speech_level'))
]
else:
raise ValueError(
'Required property \'non_speech_level\' not present in AudioMetricsDetails JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'final') and self.final is not None:
_dict['final'] = self.final
if hasattr(self, 'end_time') and self.end_time is not None:
_dict['end_time'] = self.end_time
if hasattr(self, 'signal_to_noise_ratio'
) and self.signal_to_noise_ratio is not None:
_dict['signal_to_noise_ratio'] = self.signal_to_noise_ratio
if hasattr(self, 'speech_ratio') and self.speech_ratio is not None:
_dict['speech_ratio'] = self.speech_ratio
if hasattr(
self,
'high_frequency_loss') and self.high_frequency_loss is not None:
_dict['high_frequency_loss'] = self.high_frequency_loss
if hasattr(self, 'direct_current_offset'
) and self.direct_current_offset is not None:
_dict['direct_current_offset'] = [
x._to_dict() for x in self.direct_current_offset
]
if hasattr(self, 'clipping_rate') and self.clipping_rate is not None:
_dict['clipping_rate'] = [x._to_dict() for x in self.clipping_rate]
if hasattr(self, 'speech_level') and self.speech_level is not None:
_dict['speech_level'] = [x._to_dict() for x in self.speech_level]
if hasattr(self,
'non_speech_level') and self.non_speech_level is not None:
_dict['non_speech_level'] = [
x._to_dict() for x in self.non_speech_level
]
return _dict
def __str__(self):
"""Return a `str` version of this AudioMetricsDetails 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 AudioMetricsHistogramBin(object):
"""
A bin with defined boundaries that indicates the number of values in a range of signal
characteristics for a histogram. The first and last bins of a histogram are the
boundary bins. They cover the intervals between negative infinity and the first
boundary, and between the last boundary and positive infinity, respectively.
:attr float begin: The lower boundary of the bin in the histogram.
:attr float end: The upper boundary of the bin in the histogram.
:attr int count: The number of values in the bin of the histogram.
"""
def __init__(self, begin, end, count):
"""
Initialize a AudioMetricsHistogramBin object.
:param float begin: The lower boundary of the bin in the histogram.
:param float end: The upper boundary of the bin in the histogram.
:param int count: The number of values in the bin of the histogram.
"""
self.begin = begin
self.end = end
self.count = count
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioMetricsHistogramBin object from a json dictionary."""
args = {}
validKeys = ['begin', 'end', 'count']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioMetricsHistogramBin: '
+ ', '.join(badKeys))
if 'begin' in _dict:
args['begin'] = _dict.get('begin')
else:
raise ValueError(
'Required property \'begin\' not present in AudioMetricsHistogramBin JSON'
)
if 'end' in _dict:
args['end'] = _dict.get('end')
else:
raise ValueError(
'Required property \'end\' not present in AudioMetricsHistogramBin JSON'
)
if 'count' in _dict:
args['count'] = _dict.get('count')
else:
raise ValueError(
'Required property \'count\' not present in AudioMetricsHistogramBin JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'begin') and self.begin is not None:
_dict['begin'] = self.begin
if hasattr(self, 'end') and self.end is not None:
_dict['end'] = self.end
if hasattr(self, 'count') and self.count is not None:
_dict['count'] = self.count
return _dict
def __str__(self):
"""Return a `str` version of this AudioMetricsHistogramBin 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):
"""
Information about an audio resource from a custom acoustic model.
:attr int duration: The total seconds of audio in the audio resource.
:attr str name: **For an archive-type resource,** the user-specified name of the
resource.
**For an audio-type resource,** the user-specified name of the resource or the name of
the audio file that the user added for the resource. The value depends on the method
that is called.
:attr AudioDetails details: An `AudioDetails` object that provides detailed
information about the audio resource. The object is empty until the service finishes
processing the audio.
:attr str status: The status of the audio resource:
* `ok`: The service successfully analyzed the audio data. The data can be used to
train the custom model.
* `being_processed`: The service is still analyzing the audio data. The service cannot
accept requests to add new audio resources or to train the custom model until its
analysis is complete.
* `invalid`: The audio data is not valid for training the custom model (possibly
because it has the wrong format or sampling rate, or because it is corrupted). For an
archive file, the entire archive is invalid if any of its audio files are invalid.
"""
def __init__(self, duration, name, details, status):
"""
Initialize a AudioResource object.
:param int duration: The total seconds of audio in the audio resource.
:param str name: **For an archive-type resource,** the user-specified name of the
resource.
**For an audio-type resource,** the user-specified name of the resource or the
name of the audio file that the user added for the resource. The value depends on
the method that is called.
:param AudioDetails details: An `AudioDetails` object that provides detailed
information about the audio resource. The object is empty until the service
finishes processing the audio.
:param str status: The status of the audio resource:
* `ok`: The service successfully analyzed the audio data. The data can be used to
train the custom model.
* `being_processed`: The service is still analyzing the audio data. The service
cannot accept requests to add new audio resources or to train the custom model
until its analysis is complete.
* `invalid`: The audio data is not valid for training the custom model (possibly
because it has the wrong format or sampling rate, or because it is corrupted). For
an archive file, the entire archive is invalid if any of its audio files are
invalid.
"""
self.duration = duration
self.name = name
self.details = details
self.status = status
@classmethod
def _from_dict(cls, _dict):
"""Initialize a AudioResource object from a json dictionary."""
args = {}
validKeys = ['duration', 'name', 'details', 'status']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioResource: '
+ ', '.join(badKeys))
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):
"""
Information about the audio resources from a custom acoustic model.
:attr float total_minutes_of_audio: The total minutes of accumulated audio summed over
all of the valid audio resources for the custom acoustic model. You can use this value
to determine whether the custom model has too little or too much audio to begin
training.
:attr list[AudioResource] audio: An array of `AudioResource` objects that provides
information about the audio resources of the custom acoustic model. The array is empty
if the custom model has no audio resources.
"""
def __init__(self, total_minutes_of_audio, 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 = {}
validKeys = ['total_minutes_of_audio', 'audio']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class AudioResources: '
+ ', '.join(badKeys))
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):
"""
Information about the corpora from a custom language model.
:attr list[Corpus] corpora: An array of `Corpus` objects that provides information
about the corpora for the custom model. The array is empty if the custom model has no
corpora.
"""
def __init__(self, corpora):
"""
Initialize a Corpora object.
:param list[Corpus] corpora: An array of `Corpus` objects that provides
information about the corpora for the custom model. The array is empty if the
custom model has no corpora.
"""
self.corpora = corpora
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Corpora object from a json dictionary."""
args = {}
validKeys = ['corpora']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Corpora: ' +
', '.join(badKeys))
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):
"""
Information about a corpus from a custom language model.
:attr str name: The name of the corpus.
:attr int total_words: The total number of words in the corpus. The value is `0` while
the corpus is being processed.
:attr int out_of_vocabulary_words: The number of OOV words in the corpus. The value is
`0` while the corpus is being processed.
:attr str status: The status of the corpus:
* `analyzed`: The service successfully analyzed the corpus. The custom model can be
trained with data from the corpus.
* `being_processed`: The service is still analyzing the corpus. The service cannot
accept requests to add new resources or to train the custom model.
* `undetermined`: The service encountered an error while processing the corpus. The
`error` field describes the failure.
:attr str error: (optional) If the status of the corpus is `undetermined`, the
following message: `Analysis of corpus 'name' failed. Please try adding the corpus
again by setting the 'allow_overwrite' flag to 'true'`.
"""
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`: The service successfully analyzed the corpus. The custom model can
be trained with data from the corpus.
* `being_processed`: The service is still analyzing the corpus. The service cannot
accept requests to add new resources or to train the custom model.
* `undetermined`: The service encountered an error while processing the corpus.
The `error` field describes the failure.
:param str error: (optional) If the status of the corpus is `undetermined`, the
following message: `Analysis of corpus 'name' failed. Please try adding the corpus
again by setting the 'allow_overwrite' flag to 'true'`.
"""
self.name = name
self.total_words = total_words
self.out_of_vocabulary_words = out_of_vocabulary_words
self.status = status
self.error = error
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Corpus object from a json dictionary."""
args = {}
validKeys = [
'name', 'total_words', 'out_of_vocabulary_words', 'status', 'error'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Corpus: ' +
', '.join(badKeys))
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):
"""
Information about a word that is to be added to a custom language model.
:attr str word: (optional) For the **Add custom words** method, you must specify the
custom word that is to be added to or updated in the custom model. Do not include
spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the tokens of
compound words.
Omit this parameter for the **Add a custom word** method.
:attr list[str] sounds_like: (optional) An array of sounds-like pronunciations for the
custom word. Specify how words that are difficult to pronounce, foreign words,
acronyms, and so on can be pronounced by users.
* For a word that is not in the service's base vocabulary, omit the parameter to have
the service automatically generate a sounds-like pronunciation for the word.
* For a word that is in the service's base vocabulary, use the parameter to specify
additional pronunciations for the word. You cannot override the default pronunciation
of a word; pronunciations you add augment the pronunciation from the base vocabulary.
A word can have at most five sounds-like pronunciations. A pronunciation can include
at most 40 characters not including spaces.
:attr str display_as: (optional) An alternative spelling for the custom word when it
appears in a transcript. Use the parameter when you want the word to have a spelling
that is different from its usual representation or from its spelling in corpora
training data.
"""
def __init__(self, word=None, sounds_like=None, display_as=None):
"""
Initialize a CustomWord object.
:param str word: (optional) For the **Add custom words** method, you must specify
the custom word that is to be added to or updated in the custom model. Do not
include spaces in the word. Use a `-` (dash) or `_` (underscore) to connect the
tokens of compound words.
Omit this parameter for the **Add a custom word** method.
:param list[str] sounds_like: (optional) An array of sounds-like pronunciations
for the custom word. Specify how words that are difficult to pronounce, foreign
words, acronyms, and so on can be pronounced by users.
* For a word that is not in the service's base vocabulary, omit the parameter to
have the service automatically generate a sounds-like pronunciation for the word.
* For a word that is in the service's base vocabulary, use the parameter to
specify additional pronunciations for the word. You cannot override the default
pronunciation of a word; pronunciations you add augment the pronunciation from the
base vocabulary.
A word can have at most five sounds-like pronunciations. A pronunciation can
include at most 40 characters not including spaces.
:param str display_as: (optional) An alternative spelling for the custom word when
it appears in a transcript. Use the parameter when you want the word to have a
spelling that is different from its usual representation or from its spelling in
corpora training data.
"""
self.word = word
self.sounds_like = sounds_like
self.display_as = display_as
@classmethod
def _from_dict(cls, _dict):
"""Initialize a CustomWord object from a json dictionary."""
args = {}
validKeys = ['word', 'sounds_like', 'display_as']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class CustomWord: '
+ ', '.join(badKeys))
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 Grammar(object):
"""
Information about a grammar from a custom language model.
:attr str name: The name of the grammar.
:attr int out_of_vocabulary_words: The number of OOV words in the grammar. The value
is `0` while the grammar is being processed.
:attr str status: The status of the grammar:
* `analyzed`: The service successfully analyzed the grammar. The custom model can be
trained with data from the grammar.
* `being_processed`: The service is still analyzing the grammar. The service cannot
accept requests to add new resources or to train the custom model.
* `undetermined`: The service encountered an error while processing the grammar. The
`error` field describes the failure.
:attr str error: (optional) If the status of the grammar is `undetermined`, the
following message: `Analysis of grammar '{grammar_name}' failed. Please try fixing the
error or adding the grammar again by setting the 'allow_overwrite' flag to 'true'.`.
"""
def __init__(self, name, out_of_vocabulary_words, status, error=None):
"""
Initialize a Grammar object.
:param str name: The name of the grammar.
:param int out_of_vocabulary_words: The number of OOV words in the grammar. The
value is `0` while the grammar is being processed.
:param str status: The status of the grammar:
* `analyzed`: The service successfully analyzed the grammar. The custom model can
be trained with data from the grammar.
* `being_processed`: The service is still analyzing the grammar. The service
cannot accept requests to add new resources or to train the custom model.
* `undetermined`: The service encountered an error while processing the grammar.
The `error` field describes the failure.
:param str error: (optional) If the status of the grammar is `undetermined`, the
following message: `Analysis of grammar '{grammar_name}' failed. Please try fixing
the error or adding the grammar again by setting the 'allow_overwrite' flag to
'true'.`.
"""
self.name = name
self.out_of_vocabulary_words = out_of_vocabulary_words
self.status = status
self.error = error
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Grammar object from a json dictionary."""
args = {}
validKeys = ['name', 'out_of_vocabulary_words', 'status', 'error']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Grammar: ' +
', '.join(badKeys))
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in Grammar JSON')
if 'out_of_vocabulary_words' in _dict:
args['out_of_vocabulary_words'] = _dict.get(
'out_of_vocabulary_words')
else:
raise ValueError(
'Required property \'out_of_vocabulary_words\' not present in Grammar JSON'
)
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError(
'Required property \'status\' not present in Grammar JSON')
if 'error' in _dict:
args['error'] = _dict.get('error')
return cls(**args)
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, '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 Grammar 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 Grammars(object):
"""
Information about the grammars from a custom language model.
:attr list[Grammar] grammars: An array of `Grammar` objects that provides information
about the grammars for the custom model. The array is empty if the custom model has no
grammars.
"""
def __init__(self, grammars):
"""
Initialize a Grammars object.
:param list[Grammar] grammars: An array of `Grammar` objects that provides
information about the grammars for the custom model. The array is empty if the
custom model has no grammars.
"""
self.grammars = grammars
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Grammars object from a json dictionary."""
args = {}
validKeys = ['grammars']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Grammars: '
+ ', '.join(badKeys))
if 'grammars' in _dict:
args['grammars'] = [
Grammar._from_dict(x) for x in (_dict.get('grammars'))
]
else:
raise ValueError(
'Required property \'grammars\' not present in Grammars JSON')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'grammars') and self.grammars is not None:
_dict['grammars'] = [x._to_dict() for x in self.grammars]
return _dict
def __str__(self):
"""Return a `str` version of this Grammars 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):
"""
Information about a match for a keyword from speech recognition results.
:attr str normalized_text: A specified keyword normalized to the spoken phrase that
matched in the audio input.
:attr float start_time: The start time in seconds of the keyword match.
:attr float end_time: The end time in seconds of the keyword match.
:attr float confidence: A confidence score for the keyword match in the range of 0.0
to 1.0.
"""
def __init__(self, normalized_text, start_time, end_time, confidence):
"""
Initialize a KeywordResult object.
:param str normalized_text: A specified keyword normalized to the spoken phrase
that matched in the audio input.
:param float start_time: The start time in seconds of the keyword match.
:param float end_time: The end time in seconds of the keyword match.
:param float confidence: A confidence score for the keyword match in the range of
0.0 to 1.0.
"""
self.normalized_text = normalized_text
self.start_time = start_time
self.end_time = end_time
self.confidence = confidence
@classmethod
def _from_dict(cls, _dict):
"""Initialize a KeywordResult object from a json dictionary."""
args = {}
validKeys = ['normalized_text', 'start_time', 'end_time', 'confidence']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class KeywordResult: '
+ ', '.join(badKeys))
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):
"""
Information about an existing custom language model.
:attr str customization_id: The customization ID (GUID) of the custom language model.
The **Create a custom language model** method returns only this field of the object;
it does not return the other fields.
:attr str created: (optional) The date and time in Coordinated Universal Time (UTC) at
which the custom language model was created. The value is provided in full ISO 8601
format (`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at
which the custom language model was last modified. The `created` and `updated` fields
are equal when a language model is first added but has yet to be updated. The value is
provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
:attr str language: (optional) The language identifier of the custom language model
(for example, `en-US`).
:attr str dialect: (optional) The dialect of the language for the custom language
model. 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 credentials for the instance of the
service that owns the custom language model.
:attr str name: (optional) The name of the custom language model.
:attr str description: (optional) The description of the custom language model.
:attr str base_model_name: (optional) The name of the language model for which the
custom language model was created.
:attr str status: (optional) The current status of the custom language model:
* `pending`: The model was created but is waiting either for valid training data to be
added or for the service to finish analyzing added data.
* `ready`: The model contains valid data and is ready to be trained. If the model
contains a mix of valid and invalid resources, you need to set the `strict` parameter
to `false` for the training to proceed.
* `training`: The model is currently being trained.
* `available`: The model is trained and ready to use.
* `upgrading`: The model is currently being upgraded.
* `failed`: Training of the model failed.
:attr int progress: (optional) A percentage that indicates the progress of the custom
language model's current training. A value of `100` means that the model is fully
trained. **Note:** The `progress` field does not currently reflect the progress of the
training. The field changes from `0` to `100` when training is complete.
:attr str error: (optional) If an error occurred while adding a grammar file to the
custom language model, a message that describes an `Internal Server Error` and
includes the string `Cannot compile grammar`. The status of the custom model is not
affected by the error, but the grammar cannot be used with the model.
:attr str warnings: (optional) If the request included unknown parameters, the
following message: `Unexpected query parameter(s) ['parameters'] detected`, where
`parameters` is a list that includes a quoted string for each unknown parameter.
"""
def __init__(self,
customization_id,
created=None,
language=None,
dialect=None,
versions=None,
owner=None,
name=None,
description=None,
base_model_name=None,
status=None,
progress=None,
error=None,
warnings=None,
updated=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 updated: (optional) The date and time in Coordinated Universal Time
(UTC) at which the custom language model was last modified. The `created` and
`updated` fields are equal when a language model is first added but has yet to be
updated. The value is provided in full ISO 8601 format (YYYY-MM-DDThh:mm:ss.sTZD).
:param str language: (optional) The language identifier of the custom language
model (for example, `en-US`).
: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 credentials for the instance of the
service that owns the custom language model.
:param str name: (optional) The name of the custom language model.
:param str description: (optional) The description of the custom language model.
:param str base_model_name: (optional) The name of the language model for which
the custom language model was created.
:param str status: (optional) The current status of the custom language model:
* `pending`: The model was created but is waiting either for valid training data
to be added or for the service to finish analyzing added data.
* `ready`: The model contains valid data and is ready to be trained. If the model
contains a mix of valid and invalid resources, you need to set the `strict`
parameter to `false` for the training to proceed.
* `training`: The model is currently being trained.
* `available`: The model is trained and ready to use.
* `upgrading`: The model is currently being upgraded.
* `failed`: Training of the model failed.
:param int progress: (optional) A percentage that indicates the progress of the
custom language model's current training. A value of `100` means that the model is
fully trained. **Note:** The `progress` field does not currently reflect the
progress of the training. The field changes from `0` to `100` when training is
complete.
:param str error: (optional) If an error occurred while adding a grammar file to
the custom language model, a message that describes an `Internal Server Error` and
includes the string `Cannot compile grammar`. The status of the custom model is
not affected by the error, but the grammar cannot be used with the model.
:param str warnings: (optional) If the request included unknown parameters, the
following message: `Unexpected query parameter(s) ['parameters'] detected`, where
`parameters` is a list that includes a quoted string for each unknown parameter.
"""
self.customization_id = customization_id
self.created = created
self.updated = updated
self.language = language
self.dialect = dialect
self.versions = versions
self.owner = owner
self.name = name
self.description = description
self.base_model_name = base_model_name
self.status = status
self.progress = progress
self.error = error
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a LanguageModel object from a json dictionary."""
args = {}
validKeys = [
'customization_id', 'created', 'updated', 'language', 'dialect',
'versions', 'owner', 'name', 'description', 'base_model_name',
'status', 'progress', 'error', 'warnings'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class LanguageModel: '
+ ', '.join(badKeys))
if 'customization_id' in _dict:
args['customization_id'] = _dict.get('customization_id')
else:
raise ValueError(
'Required property \'customization_id\' not present in LanguageModel JSON'
)
if 'created' in _dict:
args['created'] = _dict.get('created')
if 'updated' in _dict:
args['updated'] = _dict.get('updated')
if 'language' in _dict:
args['language'] = _dict.get('language')
if 'dialect' in _dict:
args['dialect'] = _dict.get('dialect')
if 'versions' in _dict:
args['versions'] = _dict.get('versions')
if 'owner' in _dict:
args['owner'] = _dict.get('owner')
if 'name' in _dict:
args['name'] = _dict.get('name')
if 'description' in _dict:
args['description'] = _dict.get('description')
if 'base_model_name' in _dict:
args['base_model_name'] = _dict.get('base_model_name')
if 'status' in _dict:
args['status'] = _dict.get('status')
if 'progress' in _dict:
args['progress'] = _dict.get('progress')
if 'error' in _dict:
args['error'] = _dict.get('error')
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
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, 'updated') and self.updated is not None:
_dict['updated'] = self.updated
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'dialect') and self.dialect is not None:
_dict['dialect'] = self.dialect
if hasattr(self, 'versions') and self.versions is not None:
_dict['versions'] = self.versions
if hasattr(self, 'owner') and self.owner is not None:
_dict['owner'] = self.owner
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
if hasattr(self,
'base_model_name') and self.base_model_name is not None:
_dict['base_model_name'] = self.base_model_name
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'progress') and self.progress is not None:
_dict['progress'] = self.progress
if hasattr(self, 'error') and self.error is not None:
_dict['error'] = self.error
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __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):
"""
Information about existing custom language models.
:attr list[LanguageModel] customizations: An array of `LanguageModel` objects that
provides information about each available custom language model. The array is empty if
the requesting credentials own no custom language models (if no language is specified)
or own no custom language models for the specified language.
"""
def __init__(self, customizations):
"""
Initialize a LanguageModels object.
:param list[LanguageModel] customizations: An array of `LanguageModel` objects
that provides information about each available custom language model. The array is
empty if the requesting credentials own no custom language models (if no language
is specified) or own no custom language models for the specified language.
"""
self.customizations = customizations
@classmethod
def _from_dict(cls, _dict):
"""Initialize a LanguageModels object from a json dictionary."""
args = {}
validKeys = ['customizations']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class LanguageModels: '
+ ', '.join(badKeys))
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 ProcessedAudio(object):
"""
Detailed timing information about the service's processing of the input audio.
:attr float received: The seconds of audio that the service has received as of this
response. The value of the field is greater than the values of the `transcription` and
`speaker_labels` fields during speech recognition processing, since the service first
has to receive the audio before it can begin to process it. The final value can also
be greater than the value of the `transcription` and `speaker_labels` fields by a
fractional number of seconds.
:attr float seen_by_engine: The seconds of audio that the service has passed to its
speech-processing engine as of this response. The value of the field is greater than
the values of the `transcription` and `speaker_labels` fields during speech
recognition processing. The `received` and `seen_by_engine` fields have identical
values when the service has finished processing all audio. This final value can be
greater than the value of the `transcription` and `speaker_labels` fields by a
fractional number of seconds.
:attr float transcription: The seconds of audio that the service has processed for
speech recognition as of this response.
:attr float speaker_labels: (optional) If speaker labels are requested, the seconds of
audio that the service has processed to determine speaker labels as of this response.
This value often trails the value of the `transcription` field during speech
recognition processing. The `transcription` and `speaker_labels` fields have identical
values when the service has finished processing all audio.
"""
def __init__(self,
received,
seen_by_engine,
transcription,
speaker_labels=None):
"""
Initialize a ProcessedAudio object.
:param float received: The seconds of audio that the service has received as of
this response. The value of the field is greater than the values of the
`transcription` and `speaker_labels` fields during speech recognition processing,
since the service first has to receive the audio before it can begin to process
it. The final value can also be greater than the value of the `transcription` and
`speaker_labels` fields by a fractional number of seconds.
:param float seen_by_engine: The seconds of audio that the service has passed to
its speech-processing engine as of this response. The value of the field is
greater than the values of the `transcription` and `speaker_labels` fields during
speech recognition processing. The `received` and `seen_by_engine` fields have
identical values when the service has finished processing all audio. This final
value can be greater than the value of the `transcription` and `speaker_labels`
fields by a fractional number of seconds.
:param float transcription: The seconds of audio that the service has processed
for speech recognition as of this response.
:param float speaker_labels: (optional) If speaker labels are requested, the
seconds of audio that the service has processed to determine speaker labels as of
this response. This value often trails the value of the `transcription` field
during speech recognition processing. The `transcription` and `speaker_labels`
fields have identical values when the service has finished processing all audio.
"""
self.received = received
self.seen_by_engine = seen_by_engine
self.transcription = transcription
self.speaker_labels = speaker_labels
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ProcessedAudio object from a json dictionary."""
args = {}
validKeys = [
'received', 'seen_by_engine', 'transcription', 'speaker_labels'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ProcessedAudio: '
+ ', '.join(badKeys))
if 'received' in _dict:
args['received'] = _dict.get('received')
else:
raise ValueError(
'Required property \'received\' not present in ProcessedAudio JSON'
)
if 'seen_by_engine' in _dict:
args['seen_by_engine'] = _dict.get('seen_by_engine')
else:
raise ValueError(
'Required property \'seen_by_engine\' not present in ProcessedAudio JSON'
)
if 'transcription' in _dict:
args['transcription'] = _dict.get('transcription')
else:
raise ValueError(
'Required property \'transcription\' not present in ProcessedAudio JSON'
)
if 'speaker_labels' in _dict:
args['speaker_labels'] = _dict.get('speaker_labels')
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'received') and self.received is not None:
_dict['received'] = self.received
if hasattr(self, 'seen_by_engine') and self.seen_by_engine is not None:
_dict['seen_by_engine'] = self.seen_by_engine
if hasattr(self, 'transcription') and self.transcription is not None:
_dict['transcription'] = self.transcription
if hasattr(self, 'speaker_labels') and self.speaker_labels is not None:
_dict['speaker_labels'] = self.speaker_labels
return _dict
def __str__(self):
"""Return a `str` version of this ProcessedAudio 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 ProcessingMetrics(object):
"""
If processing metrics are requested, information about the service's processing of the
input audio. Processing metrics are not available with the synchronous **Recognize
audio** method.
:attr ProcessedAudio processed_audio: Detailed timing information about the service's
processing of the input audio.
:attr float wall_clock_since_first_byte_received: The amount of real time in seconds
that has passed since the service received the first byte of input audio. Values in
this field are generally multiples of the specified metrics interval, with two
differences:
* Values might not reflect exact intervals (for instance, 0.25, 0.5, and so on).
Actual values might be 0.27, 0.52, and so on, depending on when the service receives
and processes audio.
* The service also returns values for transcription events if you set the
`interim_results` parameter to `true`. The service returns both processing metrics and
transcription results when such events occur.
:attr bool periodic: An indication of whether the metrics apply to a periodic interval
or a transcription event:
* `true` means that the response was triggered by a specified processing interval. The
information contains processing metrics only.
* `false` means that the response was triggered by a transcription event. The
information contains processing metrics plus transcription results.
Use the field to identify why the service generated the response and to filter
different results if necessary.
"""
def __init__(self, processed_audio, wall_clock_since_first_byte_received,
periodic):
"""
Initialize a ProcessingMetrics object.
:param ProcessedAudio processed_audio: Detailed timing information about the
service's processing of the input audio.
:param float wall_clock_since_first_byte_received: The amount of real time in
seconds that has passed since the service received the first byte of input audio.
Values in this field are generally multiples of the specified metrics interval,
with two differences:
* Values might not reflect exact intervals (for instance, 0.25, 0.5, and so on).
Actual values might be 0.27, 0.52, and so on, depending on when the service
receives and processes audio.
* The service also returns values for transcription events if you set the
`interim_results` parameter to `true`. The service returns both processing metrics
and transcription results when such events occur.
:param bool periodic: An indication of whether the metrics apply to a periodic
interval or a transcription event:
* `true` means that the response was triggered by a specified processing interval.
The information contains processing metrics only.
* `false` means that the response was triggered by a transcription event. The
information contains processing metrics plus transcription results.
Use the field to identify why the service generated the response and to filter
different results if necessary.
"""
self.processed_audio = processed_audio
self.wall_clock_since_first_byte_received = wall_clock_since_first_byte_received
self.periodic = periodic
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ProcessingMetrics object from a json dictionary."""
args = {}
validKeys = [
'processed_audio', 'wall_clock_since_first_byte_received',
'periodic'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class ProcessingMetrics: '
+ ', '.join(badKeys))
if 'processed_audio' in _dict:
args['processed_audio'] = ProcessedAudio._from_dict(
_dict.get('processed_audio'))
else:
raise ValueError(
'Required property \'processed_audio\' not present in ProcessingMetrics JSON'
)
if 'wall_clock_since_first_byte_received' in _dict:
args['wall_clock_since_first_byte_received'] = _dict.get(
'wall_clock_since_first_byte_received')
else:
raise ValueError(
'Required property \'wall_clock_since_first_byte_received\' not present in ProcessingMetrics JSON'
)
if 'periodic' in _dict:
args['periodic'] = _dict.get('periodic')
else:
raise ValueError(
'Required property \'periodic\' not present in ProcessingMetrics JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self,
'processed_audio') and self.processed_audio is not None:
_dict['processed_audio'] = self.processed_audio._to_dict()
if hasattr(self, 'wall_clock_since_first_byte_received'
) and self.wall_clock_since_first_byte_received is not None:
_dict[
'wall_clock_since_first_byte_received'] = self.wall_clock_since_first_byte_received
if hasattr(self, 'periodic') and self.periodic is not None:
_dict['periodic'] = self.periodic
return _dict
def __str__(self):
"""Return a `str` version of this ProcessingMetrics 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):
"""
Information about a current asynchronous speech recognition job.
:attr str id: The ID of the asynchronous job.
:attr str status: The current status of the job:
* `waiting`: The service is preparing the job for processing. The service returns this
status when the job is initially created or when it is waiting for capacity to process
the job. The job remains in this state until the service has the capacity to begin
processing it.
* `processing`: The service is actively processing the job.
* `completed`: The service has finished processing the job. If the job specified a
callback URL and the event `recognitions.completed_with_results`, the service sent the
results with the callback notification. Otherwise, you must retrieve the results by
checking the individual job.
* `failed`: The job failed.
:attr str created: The date and time in Coordinated Universal Time (UTC) at which the
job was created. The value is provided in full ISO 8601 format
(`YYYY-MM-DDThh:mm:ss.sTZD`).
:attr str updated: (optional) The date and time in Coordinated Universal Time (UTC) at
which the job was last updated by the service. The value is provided in full ISO 8601
format (`YYYY-MM-DDThh:mm:ss.sTZD`). This field is returned only by the **Check jobs**
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 = {}
validKeys = [
'id', 'status', 'created', 'updated', 'url', 'user_token',
'results', 'warnings'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RecognitionJob: '
+ ', '.join(badKeys))
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):
"""
Information about current asynchronous speech recognition jobs.
:attr list[RecognitionJob] recognitions: An array of `RecognitionJob` objects that
provides the status for each of the user's current jobs. The array is empty if the
user has no current jobs.
"""
def __init__(self, recognitions):
"""
Initialize a RecognitionJobs object.
:param list[RecognitionJob] recognitions: An array of `RecognitionJob` objects
that provides the status for each of the user's current jobs. The array is empty
if the user has no current jobs.
"""
self.recognitions = recognitions
@classmethod
def _from_dict(cls, _dict):
"""Initialize a RecognitionJobs object from a json dictionary."""
args = {}
validKeys = ['recognitions']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RecognitionJobs: '
+ ', '.join(badKeys))
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):
"""
Information about a request to register a callback for asynchronous speech
recognition.
:attr str status: The current status of the job:
* `created`: The service successfully white-listed the callback URL as a result of the
call.
* `already created`: The URL was already white-listed.
:attr str url: The callback URL that is successfully registered.
"""
def __init__(self, status, url):
"""
Initialize a RegisterStatus object.
:param str status: The current status of the job:
* `created`: The service successfully white-listed the callback URL as a result of
the call.
* `already created`: The URL was already white-listed.
: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 = {}
validKeys = ['status', 'url']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class RegisterStatus: '
+ ', '.join(badKeys))
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):
"""
Information about the speakers from speech recognition results.
:attr float from_: The start time of a word from the transcript. The value matches the
start time of a word from the `timestamps` array.
:attr float to: The end time of a word from the transcript. The value matches the end
time of a word from the `timestamps` array.
:attr int speaker: The numeric identifier that the service assigns to a speaker from
the audio. Speaker IDs begin at `0` initially but can evolve and change across interim
results (if supported by the method) and between interim and final results as the
service processes the audio. They are not guaranteed to be sequential, contiguous, or
ordered.
:attr float confidence: A score that indicates the service's confidence in its
identification of the speaker in the range of 0.0 to 1.0.
:attr bool final_results: An indication of whether the service might further change
word and speaker-label results. A value of `true` means that the service guarantees
not to send any further updates for the current or any preceding results; `false`
means that the service might send further updates to the results.
"""
def __init__(self, from_, to, speaker, confidence, final_results):
"""
Initialize a SpeakerLabelsResult object.
:param float from_: The start time of a word from the transcript. The value
matches the start time of a word from the `timestamps` array.
:param float to: The end time of a word from the transcript. The value matches the
end time of a word from the `timestamps` array.
:param int speaker: The numeric identifier that the service assigns to a speaker
from the audio. Speaker IDs begin at `0` initially but can evolve and change
across interim results (if supported by the method) and between interim and final
results as the service processes the audio. They are not guaranteed to be
sequential, contiguous, or ordered.
:param float confidence: A score that indicates the service's confidence in its
identification of the speaker in the range of 0.0 to 1.0.
:param bool final_results: An indication of whether the service might further
change word and speaker-label results. A value of `true` means that the service
guarantees not to send any further updates for the current or any preceding
results; `false` means that the service might send further updates to the results.
"""
self.from_ = from_
self.to = to
self.speaker = speaker
self.confidence = confidence
self.final_results = final_results
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeakerLabelsResult object from a json dictionary."""
args = {}
validKeys = [
'from_', 'from', 'to', 'speaker', 'confidence', 'final_results',
'final'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeakerLabelsResult: '
+ ', '.join(badKeys))
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):
"""
Information about an available language model.
:attr str name: The name of the model for use as an identifier in calls to the service
(for example, `en-US_BroadbandModel`).
:attr str language: The language identifier of the model (for example, `en-US`).
:attr int rate: The sampling rate (minimum acceptable rate for audio) used by the
model in Hertz.
:attr str url: The URI for the model.
:attr SupportedFeatures supported_features: Additional service features that are
supported with the model.
:attr str description: A brief description of the model.
"""
def __init__(self, name, language, rate, url, supported_features,
description):
"""
Initialize a SpeechModel object.
:param str name: The name of the model for use as an identifier in calls to the
service (for example, `en-US_BroadbandModel`).
:param str language: The language identifier of the model (for example, `en-US`).
:param int rate: The sampling rate (minimum acceptable rate for audio) used by the
model in Hertz.
:param str url: The URI for the model.
:param SupportedFeatures supported_features: Additional service features that are
supported with the model.
:param str description: A brief description of the model.
"""
self.name = name
self.language = language
self.rate = rate
self.url = url
self.supported_features = supported_features
self.description = description
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechModel object from a json dictionary."""
args = {}
validKeys = [
'name', 'language', 'rate', 'url', 'supported_features',
'description'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeechModel: '
+ ', '.join(badKeys))
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError(
'Required property \'name\' not present in SpeechModel JSON')
if 'language' in _dict:
args['language'] = _dict.get('language')
else:
raise ValueError(
'Required property \'language\' not present in SpeechModel JSON'
)
if 'rate' in _dict:
args['rate'] = _dict.get('rate')
else:
raise ValueError(
'Required property \'rate\' not present in SpeechModel JSON')
if 'url' in _dict:
args['url'] = _dict.get('url')
else:
raise ValueError(
'Required property \'url\' not present in SpeechModel JSON')
if 'supported_features' in _dict:
args['supported_features'] = SupportedFeatures._from_dict(
_dict.get('supported_features'))
else:
raise ValueError(
'Required property \'supported_features\' not present in SpeechModel JSON'
)
if 'description' in _dict:
args['description'] = _dict.get('description')
else:
raise ValueError(
'Required property \'description\' not present in SpeechModel JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'language') and self.language is not None:
_dict['language'] = self.language
if hasattr(self, 'rate') and self.rate is not None:
_dict['rate'] = self.rate
if hasattr(self, 'url') and self.url is not None:
_dict['url'] = self.url
if hasattr(
self,
'supported_features') and self.supported_features is not None:
_dict['supported_features'] = self.supported_features._to_dict()
if hasattr(self, 'description') and self.description is not None:
_dict['description'] = self.description
return _dict
def __str__(self):
"""Return a `str` version of this SpeechModel object."""
return json.dumps(self._to_dict(), indent=2)
def __eq__(self, other):
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
[docs]class SpeechModels(object):
"""
Information about the available language models.
:attr list[SpeechModel] models: An array of `SpeechModel` objects that provides
information about each available model.
"""
def __init__(self, models):
"""
Initialize a SpeechModels object.
:param list[SpeechModel] models: An array of `SpeechModel` objects that provides
information about each available model.
"""
self.models = models
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechModels object from a json dictionary."""
args = {}
validKeys = ['models']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeechModels: '
+ ', '.join(badKeys))
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):
"""
An alternative transcript from speech recognition results.
:attr str transcript: A transcription of the audio.
:attr float confidence: (optional) A score that indicates the service's confidence in
the transcript in the range of 0.0 to 1.0. A confidence score is returned only for the
best alternative and only with results marked as final.
:attr list[str] timestamps: (optional) Time alignments for each word from the
transcript as a list of lists. Each inner list consists of three elements: the word
followed by its start and end time in seconds, for example:
`[["hello",0.0,1.2],["world",1.2,2.5]]`. Timestamps are returned only for the best
alternative.
:attr list[str] word_confidence: (optional) A confidence score for each word of the
transcript as a list of lists. Each inner list consists of two elements: the word and
its confidence score in the range of 0.0 to 1.0, for example:
`[["hello",0.95],["world",0.866]]`. Confidence scores are returned only for the best
alternative and only with results marked as final.
"""
def __init__(self,
transcript,
confidence=None,
timestamps=None,
word_confidence=None):
"""
Initialize a SpeechRecognitionAlternative object.
:param str transcript: A transcription of the audio.
:param float confidence: (optional) A score that indicates the service's
confidence in the transcript in the range of 0.0 to 1.0. A confidence score is
returned only for the best alternative and only with results marked as final.
:param list[str] timestamps: (optional) Time alignments for each word from the
transcript as a list of lists. Each inner list consists of three elements: the
word followed by its start and end time in seconds, for example:
`[["hello",0.0,1.2],["world",1.2,2.5]]`. Timestamps are returned only for the best
alternative.
:param list[str] word_confidence: (optional) A confidence score for each word of
the transcript as a list of lists. Each inner list consists of two elements: the
word and its confidence score in the range of 0.0 to 1.0, for example:
`[["hello",0.95],["world",0.866]]`. Confidence scores are returned only for the
best alternative and only with results marked as final.
"""
self.transcript = transcript
self.confidence = confidence
self.timestamps = timestamps
self.word_confidence = word_confidence
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionAlternative object from a json dictionary."""
args = {}
validKeys = [
'transcript', 'confidence', 'timestamps', 'word_confidence'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeechRecognitionAlternative: '
+ ', '.join(badKeys))
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):
"""
Component results for a speech recognition request.
:attr bool final_results: An indication of whether the transcription results are
final. If `true`, the results for this utterance are not updated further; no
additional results are sent for a `result_index` once its results are indicated as
final.
:attr list[SpeechRecognitionAlternative] alternatives: An array of alternative
transcripts. The `alternatives` array can include additional requested output such as
word confidence or timestamps.
:attr dict keywords_result: (optional) A dictionary (or associative array) whose keys
are the strings specified for `keywords` if both that parameter and
`keywords_threshold` are specified. The value for each key is an array of matches
spotted in the audio for that keyword. Each match is described by a `KeywordResult`
object. A keyword for which no matches are found is omitted from the dictionary. The
dictionary is omitted entirely if no matches are found for any keywords.
:attr list[WordAlternativeResults] word_alternatives: (optional) An array of
alternative hypotheses found for words of the input audio if a
`word_alternatives_threshold` is specified.
"""
def __init__(self,
final_results,
alternatives,
keywords_result=None,
word_alternatives=None):
"""
Initialize a SpeechRecognitionResult object.
:param bool final_results: An indication of whether the transcription results are
final. If `true`, the results for this utterance are not updated further; no
additional results are sent for a `result_index` once its results are indicated as
final.
:param list[SpeechRecognitionAlternative] alternatives: An array of alternative
transcripts. The `alternatives` array can include additional requested output such
as word confidence or timestamps.
:param dict keywords_result: (optional) A dictionary (or associative array) whose
keys are the strings specified for `keywords` if both that parameter and
`keywords_threshold` are specified. The value for each key is an array of matches
spotted in the audio for that keyword. Each match is described by a
`KeywordResult` object. A keyword for which no matches are found is omitted from
the dictionary. The dictionary is omitted entirely if no matches are found for any
keywords.
:param list[WordAlternativeResults] word_alternatives: (optional) An array of
alternative hypotheses found for words of the input audio if a
`word_alternatives_threshold` is specified.
"""
self.final_results = final_results
self.alternatives = alternatives
self.keywords_result = keywords_result
self.word_alternatives = word_alternatives
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionResult object from a json dictionary."""
args = {}
validKeys = [
'final_results', 'final', 'alternatives', 'keywords_result',
'word_alternatives'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeechRecognitionResult: '
+ ', '.join(badKeys))
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):
"""
The complete results for a speech recognition request.
:attr list[SpeechRecognitionResult] results: (optional) An array of
`SpeechRecognitionResult` objects that can include interim and final results (interim
results are returned only if supported by the method). Final results are guaranteed
not to change; interim results might be replaced by further interim results and final
results. The service periodically sends updates to the results list; the
`result_index` is set to the lowest index in the array that has changed; it is
incremented for new results.
:attr int result_index: (optional) An index that indicates a change point in the
`results` array. The service increments the index only for additional results that it
sends for new audio for the same request.
:attr list[SpeakerLabelsResult] speaker_labels: (optional) An array of
`SpeakerLabelsResult` objects that identifies which words were spoken by which
speakers in a multi-person exchange. The array is returned only if the
`speaker_labels` parameter is `true`. When interim results are also requested for
methods that support them, it is possible for a `SpeechRecognitionResults` object to
include only the `speaker_labels` field.
:attr ProcessingMetrics processing_metrics: (optional) If processing metrics are
requested, information about the service's processing of the input audio. Processing
metrics are not available with the synchronous **Recognize audio** method.
:attr AudioMetrics audio_metrics: (optional) If audio metrics are requested,
information about the signal characteristics of the input audio.
:attr list[str] warnings: (optional) An array of warning messages associated with the
request:
* Warnings for invalid parameters or fields can include a descriptive message and a
list of invalid argument strings, for example, `"Unknown arguments:"` or `"Unknown url
query arguments:"` followed by a list of the form `"{invalid_arg_1},
{invalid_arg_2}."`
* The following warning is returned if the request passes a custom model that is based
on an older version of a base model for which an updated version is available: `"Using
previous version of base model, because your custom model has been built with it.
Please note that this version will be supported only for a limited time. Consider
updating your custom model to the new base model. If you do not do that you will be
automatically switched to base model when you used the non-updated custom model."`
In both cases, the request succeeds despite the warnings.
"""
def __init__(self,
results=None,
result_index=None,
speaker_labels=None,
audio_metrics=None,
warnings=None,
processing_metrics=None):
"""
Initialize a SpeechRecognitionResults object.
:param list[SpeechRecognitionResult] results: (optional) An array of
`SpeechRecognitionResult` objects that can include interim and final results
(interim results are returned only if supported by the method). Final results are
guaranteed not to change; interim results might be replaced by further interim
results and final results. The service periodically sends updates to the results
list; the `result_index` is set to the lowest index in the array that has changed;
it is incremented for new results.
:param int result_index: (optional) An index that indicates a change point in the
`results` array. The service increments the index only for additional results that
it sends for new audio for the same request.
:param list[SpeakerLabelsResult] speaker_labels: (optional) An array of
`SpeakerLabelsResult` objects that identifies which words were spoken by which
speakers in a multi-person exchange. The array is returned only if the
`speaker_labels` parameter is `true`. When interim results are also requested for
methods that support them, it is possible for a `SpeechRecognitionResults` object
to include only the `speaker_labels` field.
:param ProcessingMetrics processing_metrics: (optional) If processing metrics are
requested, information about the service's processing of the input audio.
Processing metrics are not available with the synchronous **Recognize audio**
method.
:param AudioMetrics audio_metrics: (optional) If audio metrics are requested,
information about the signal characteristics of the input audio.
:param list[str] warnings: (optional) An array of warning messages associated with
the request:
* Warnings for invalid parameters or fields can include a descriptive message and
a list of invalid argument strings, for example, `"Unknown arguments:"` or
`"Unknown url query arguments:"` followed by a list of the form `"{invalid_arg_1},
{invalid_arg_2}."`
* The following warning is returned if the request passes a custom model that is
based on an older version of a base model for which an updated version is
available: `"Using previous version of base model, because your custom model has
been built with it. Please note that this version will be supported only for a
limited time. Consider updating your custom model to the new base model. If you do
not do that you will be automatically switched to base model when you used the
non-updated custom model."`
In both cases, the request succeeds despite the warnings.
"""
self.results = results
self.result_index = result_index
self.speaker_labels = speaker_labels
self.processing_metrics = processing_metrics
self.audio_metrics = audio_metrics
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SpeechRecognitionResults object from a json dictionary."""
args = {}
validKeys = [
'results', 'result_index', 'speaker_labels', 'processing_metrics',
'audio_metrics', 'warnings'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SpeechRecognitionResults: '
+ ', '.join(badKeys))
if 'results' in _dict:
args['results'] = [
SpeechRecognitionResult._from_dict(x)
for x in (_dict.get('results'))
]
if 'result_index' in _dict:
args['result_index'] = _dict.get('result_index')
if 'speaker_labels' in _dict:
args['speaker_labels'] = [
SpeakerLabelsResult._from_dict(x)
for x in (_dict.get('speaker_labels'))
]
if 'processing_metrics' in _dict:
args['processing_metrics'] = ProcessingMetrics._from_dict(
_dict.get('processing_metrics'))
if 'audio_metrics' in _dict:
args['audio_metrics'] = AudioMetrics._from_dict(
_dict.get('audio_metrics'))
if 'warnings' in _dict:
args['warnings'] = _dict.get('warnings')
return cls(**args)
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,
'processing_metrics') and self.processing_metrics is not None:
_dict['processing_metrics'] = self.processing_metrics._to_dict()
if hasattr(self, 'audio_metrics') and self.audio_metrics is not None:
_dict['audio_metrics'] = self.audio_metrics._to_dict()
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = self.warnings
return _dict
def __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):
"""
Additional service features that are supported with the model.
:attr bool custom_language_model: Indicates whether the customization interface can be
used to create a custom language model based on the language model.
:attr bool speaker_labels: Indicates whether the `speaker_labels` parameter can be
used with the language model.
"""
def __init__(self, custom_language_model, speaker_labels):
"""
Initialize a SupportedFeatures object.
:param bool custom_language_model: Indicates whether the customization interface
can be used to create a custom language model based on the language model.
:param bool speaker_labels: Indicates whether the `speaker_labels` parameter can
be used with the language model.
"""
self.custom_language_model = custom_language_model
self.speaker_labels = speaker_labels
@classmethod
def _from_dict(cls, _dict):
"""Initialize a SupportedFeatures object from a json dictionary."""
args = {}
validKeys = ['custom_language_model', 'speaker_labels']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class SupportedFeatures: '
+ ', '.join(badKeys))
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 TrainingResponse(object):
"""
The response from training of a custom language or custom acoustic model.
:attr list[TrainingWarning] warnings: (optional) An array of `TrainingWarning` objects
that lists any invalid resources contained in the custom model. For custom language
models, invalid resources are grouped and identified by type of resource. The method
can return warnings only if the `strict` parameter is set to `false`.
"""
def __init__(self, warnings=None):
"""
Initialize a TrainingResponse object.
:param list[TrainingWarning] warnings: (optional) An array of `TrainingWarning`
objects that lists any invalid resources contained in the custom model. For custom
language models, invalid resources are grouped and identified by type of resource.
The method can return warnings only if the `strict` parameter is set to `false`.
"""
self.warnings = warnings
@classmethod
def _from_dict(cls, _dict):
"""Initialize a TrainingResponse object from a json dictionary."""
args = {}
validKeys = ['warnings']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class TrainingResponse: '
+ ', '.join(badKeys))
if 'warnings' in _dict:
args['warnings'] = [
TrainingWarning._from_dict(x) for x in (_dict.get('warnings'))
]
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = [x._to_dict() for x in self.warnings]
return _dict
def __str__(self):
"""Return a `str` version of this TrainingResponse 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 TrainingWarning(object):
"""
A warning from training of a custom language or custom acoustic model.
:attr str code: An identifier for the type of invalid resources listed in the
`description` field.
:attr str message: A warning message that lists the invalid resources that are
excluded from the custom model's training. The message has the following format:
`Analysis of the following {resource_type} has not completed successfully:
[{resource_names}]. They will be excluded from custom {model_type} model training.`.
"""
def __init__(self, code, message):
"""
Initialize a TrainingWarning object.
:param str code: An identifier for the type of invalid resources listed in the
`description` field.
:param str message: A warning message that lists the invalid resources that are
excluded from the custom model's training. The message has the following format:
`Analysis of the following {resource_type} has not completed successfully:
[{resource_names}]. They will be excluded from custom {model_type} model
training.`.
"""
self.code = code
self.message = message
@classmethod
def _from_dict(cls, _dict):
"""Initialize a TrainingWarning object from a json dictionary."""
args = {}
validKeys = ['code', 'message']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class TrainingWarning: '
+ ', '.join(badKeys))
if 'code' in _dict:
args['code'] = _dict.get('code')
else:
raise ValueError(
'Required property \'code\' not present in TrainingWarning JSON'
)
if 'message' in _dict:
args['message'] = _dict.get('message')
else:
raise ValueError(
'Required property \'message\' not present in TrainingWarning JSON'
)
return cls(**args)
def _to_dict(self):
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'code') and self.code is not None:
_dict['code'] = self.code
if hasattr(self, 'message') and self.message is not None:
_dict['message'] = self.message
return _dict
def __str__(self):
"""Return a `str` version of this TrainingWarning 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):
"""
Information about a word from a custom language model.
:attr str word: A word from the custom model's words resource. The spelling of the
word is used to train the model.
:attr list[str] sounds_like: An array of 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 = {}
validKeys = [
'word', 'sounds_like', 'display_as', 'count', 'source', 'error'
]
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Word: ' +
', '.join(badKeys))
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):
"""
An alternative hypothesis for a word from speech recognition results.
:attr float confidence: A confidence score for the word alternative hypothesis in the
range of 0.0 to 1.0.
:attr str word: An alternative hypothesis for a word from the input audio.
"""
def __init__(self, confidence, word):
"""
Initialize a WordAlternativeResult object.
:param float confidence: A confidence score for the word alternative hypothesis in
the range of 0.0 to 1.0.
:param str word: An alternative hypothesis for a word from the input audio.
"""
self.confidence = confidence
self.word = word
@classmethod
def _from_dict(cls, _dict):
"""Initialize a WordAlternativeResult object from a json dictionary."""
args = {}
validKeys = ['confidence', 'word']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class WordAlternativeResult: '
+ ', '.join(badKeys))
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):
"""
Information about alternative hypotheses for words from speech recognition results.
:attr float start_time: The start time in seconds of the word from the input audio
that corresponds to the word alternatives.
:attr float end_time: The end time in seconds of the word from the input audio that
corresponds to the word alternatives.
:attr list[WordAlternativeResult] alternatives: An array of alternative hypotheses for
a word from the input audio.
"""
def __init__(self, start_time, 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 = {}
validKeys = ['start_time', 'end_time', 'alternatives']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class WordAlternativeResults: '
+ ', '.join(badKeys))
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):
"""
An error associated with a word from a custom language model.
:attr str element: A key-value pair that describes an error associated with the
definition of a word in the words resource. The pair has the format `"element":
"message"`, where `element` is the aspect of the definition that caused the problem
and `message` describes the problem. The following example describes a problem with
one of the word's sounds-like definitions: `"{sounds_like_string}": "Numbers are not
allowed in sounds-like. You can try for example '{suggested_string}'."`.
"""
def __init__(self, element):
"""
Initialize a WordError object.
:param str element: A key-value pair that describes an error associated with the
definition of a word in the words resource. The pair has the format `"element":
"message"`, where `element` is the aspect of the definition that caused the
problem and `message` describes the problem. The following example describes a
problem with one of the word's sounds-like definitions: `"{sounds_like_string}":
"Numbers are not allowed in sounds-like. You can try for example
'{suggested_string}'."`.
"""
self.element = element
@classmethod
def _from_dict(cls, _dict):
"""Initialize a WordError object from a json dictionary."""
args = {}
validKeys = ['element']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class WordError: '
+ ', '.join(badKeys))
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):
"""
Information about the words from a custom language model.
:attr list[Word] words: An array of `Word` objects that provides information about
each word in the custom model's words resource. The array is empty if the custom model
has no words.
"""
def __init__(self, words):
"""
Initialize a Words object.
:param list[Word] words: An array of `Word` objects that provides information
about each word in the custom model's words resource. The array is empty if the
custom model has no words.
"""
self.words = words
@classmethod
def _from_dict(cls, _dict):
"""Initialize a Words object from a json dictionary."""
args = {}
validKeys = ['words']
badKeys = set(_dict.keys()) - set(validKeys)
if badKeys:
raise ValueError(
'Unrecognized keys detected in dictionary for class Words: ' +
', '.join(badKeys))
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