speech-usage.rst

Using the API

The Google Speech API enables developers to convert audio to text. The API recognizes over 80 languages and variants, to support your global user base.

Warning

This is a Beta release of Google Speech API. This API is not intended for real-time usage in critical applications.

Client

:class:`~google.cloud.speech.client.Client` objects provide a means to configure your application. Each instance holds an authenticated connection to the Natural Language service.

For an overview of authentication in google-cloud-python, see :doc:`google-cloud-auth`.

Assuming your environment is set up as described in that document, create an instance of :class:`~google.cloud.speech.client.Client`.

>>> from google.cloud import speech
>>> client = speech.Client()

Asychronous Recognition

The :meth:`~google.cloud.speech.Client.async_recognize` sends audio data to the Speech API and initiates a Long Running Operation. Using this operation, you can periodically poll for recognition results. Use asynchronous requests for audio data of any duration up to 80 minutes.

Note

Only the :attr:`Encoding.LINEAR16` encoding type is supported by asynchronous recognition.

See: Speech Asynchronous Recognize

>>> import time
>>> from google.cloud import speech
>>> client = speech.Client()
>>> sample = client.sample(source_uri='gs://my-bucket/recording.flac',
...                        encoding=speech.Encoding.LINEAR16,
...                        sample_rate=44100)
>>> operation = client.async_recognize(sample, max_alternatives=2)
>>> retry_count = 100
>>> while retry_count > 0 and not operation.complete:
...     retry_count -= 1
...     time.sleep(10)
...     operation.poll()  # API call
>>> operation.complete
True
>>> operation.results[0].transcript
'how old is the Brooklyn Bridge'
>>> operation.results[0].confidence
0.98267895

Synchronous Recognition

The :meth:`~google.cloud.speech.Client.sync_recognize` method converts speech data to text and returns alternative text transcriptons.

This example uses language_code='en-GB' to better recognize a dialect from Great Britian.

>>> from google.cloud import speech
>>> client = speech.Client()
>>> sample = client.sample(source_uri='gs://my-bucket/recording.flac',
...                        encoding=speech.Encoding.FLAC,
...                        sample_rate=44100)
>>> operation = client.async_recognize(sample, max_alternatives=2)
 >>> alternatives = client.sync_recognize(
 ...     'FLAC', 16000, source_uri='gs://my-bucket/recording.flac',
 ...     language_code='en-GB', max_alternatives=2)
 >>> for alternative in alternatives:
 ...     print('=' * 20)
 ...     print('transcript: ' + alternative['transcript'])
 ...     print('confidence: ' + alternative['confidence'])
 ====================
 transcript: Hello, this is a test
 confidence: 0.81
 ====================
 transcript: Hello, this is one test
 confidence: 0

Example of using the profanity filter.

>>> from google.cloud import speech
>>> client = speech.Client()
>>> sample = client.sample(source_uri='gs://my-bucket/recording.flac',
...                        encoding=speech.Encoding.FLAC,
...                        sample_rate=44100)
>>> alternatives = client.sync_recognize(sample, max_alternatives=1,
...                                      profanity_filter=True)
>>> for alternative in alternatives:
...     print('=' * 20)
...     print('transcript: ' + alternative['transcript'])
...     print('confidence: ' + alternative['confidence'])
====================
transcript: Hello, this is a f****** test
confidence: 0.81

Using speech context hints to get better results. This can be used to improve the accuracy for specific words and phrases. This can also be used to add new words to the vocabulary of the recognizer.

>>> from google.cloud import speech
>>> client = speech.Client()
>>> sample = client.sample(source_uri='gs://my-bucket/recording.flac',
...                        encoding=speech.Encoding.FLAC,
...                        sample_rate=44100)
>>> hints = ['hi', 'good afternoon']
>>> alternatives = client.sync_recognize(sample, max_alternatives=2,
...                                      speech_context=hints)
>>> for alternative in alternatives:
...     print('=' * 20)
...     print('transcript: ' + alternative['transcript'])
...     print('confidence: ' + alternative['confidence'])
====================
transcript: Hello, this is a test
confidence: 0.81

Streaming Recognition

The :meth:`~google.cloud.speech.Client.stream_recognize` method converts speech data to possible text alternatives on the fly.

Note

Streaming recognition requests are limited to 1 minute of audio.

See: https://cloud.google.com/speech/limits#content

>>> from google.cloud import speech
>>> client = speech.Client()
>>> with open('./hello.wav', 'rb') as stream:
...     sample = client.sample(stream=stream, encoding=speech.Encoding.LINEAR16,
...                            sample_rate=16000)
...     for response in client.stream_recognize(sample):
...         print(response.transcript)
...         print(response.is_final)
hello
True

By setting interim_results to :data:`True`, interim results (tentative hypotheses) may be returned as they become available (these interim results are indicated with the is_final=false flag). If :data:`False` or omitted, only is_final=true result(s) are returned.

>>> from google.cloud import speech
>>> client = speech.Client()
>>> with open('./hello.wav', 'rb') as stream:
...     sample = client.sample(stream=stream, encoding=speech.Encoding.LINEAR16,
...                            sample_rate=16000)
...     for response in client.stream_recognize(sample,
...                                             interim_results=True):
...         print('====Response====')
...         print(response.transcript)
...         print(response.is_final)
====Response====
he
False
====Response====
hell
False
====Repsonse====
hello
True

By default the recognizer will perform continuous recognition (continuing to process audio even if the user pauses speaking) until the client closes the output stream or when the maximum time limit has been reached.

If you only want to recognize a single utterance you can set

single_utterance to True and only one result will be returned.

See: Single Utterance

>>> with open('./hello_pause_goodbye.wav', 'rb') as stream:
...     sample = client.sample(stream=stream, encoding=speech.Encoding.LINEAR16,
...                            sample_rate=16000)
...     for response in client.stream_recognize(sample,
...                                             single_utterance=True):
...         print(response.transcript)
...         print(response.is_final)
hello
True