https://github.com/korniichuk/google-speech
QuickStart. Google Cloud Speech-to-Text API with Python
https://github.com/korniichuk/google-speech
cloud-speech-api gcp google google-cloud-platform mp3 python speech speech-recognition speech-to-text
Last synced: about 2 months ago
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QuickStart. Google Cloud Speech-to-Text API with Python
- Host: GitHub
- URL: https://github.com/korniichuk/google-speech
- Owner: korniichuk
- License: unlicense
- Created: 2018-08-06T11:55:45.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-06T14:27:49.000Z (about 7 years ago)
- Last Synced: 2025-04-15T22:57:24.428Z (6 months ago)
- Topics: cloud-speech-api, gcp, google, google-cloud-platform, mp3, python, speech, speech-recognition, speech-to-text
- Language: Python
- Size: 156 KB
- Stars: 1
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# QuickStart. Google Cloud Speech-to-Text API with Python
## Check sample rate of audio (measured in Hz)
You must know sample rate of your audio files like 8000 Hz, 16000 Hz, etc.
In Ubuntu OS just click right button on your audio file and select `Properties-->Audio-->Sample rate`. See image below:
Example: 8000 Hz sample rate of audio on image above.
## Transcribe local audio file (1 minut max)
See [local.py](local.py) file or code below:
```python
import io
from google.cloud import speech_v1p1beta1 as speech
speech_file = 'example.mp3'
# Encoding: https://cloud.google.com/
# speech-to-text/docs/reference/rest/v1beta1/RecognitionConfig
encoding=speech.enums.RecognitionConfig.AudioEncoding.AMR
sample_rate_hertz=8000
# Language: https://cloud.google.com/
# speech-to-text/docs/languages
language_code='en-US'
client = speech.SpeechClient()
with io.open(speech_file, 'rb') as audio_file:
content = audio_file.read()
audio = speech.types.RecognitionAudio(content=content)
config = speech.types.RecognitionConfig(
encoding=encoding,
sample_rate_hertz=sample_rate_hertz,
language_code=language_code,
# Enhanced models are only available to projects that
# opt in for audio data collection.
use_enhanced=True,
# A model must be specified to use enhanced model.
model='phone_call',
profanity_filter=False,
enable_automatic_punctuation=True,
enable_word_confidence=True)
response = client.recognize(config, audio)
for i, result in enumerate(response.results):
alternative = result.alternatives[0]
print('-' * 20)
print('First alternative of result {}'.format(i))
print('Transcript: {}'.format(alternative.transcript))
```
## Transcribe audio file from Google Storage (longer that 1 minute)
See [storage.py](storage.py) file or code below:
```python
from google.cloud import speech_v1p1beta1 as speech
uri = 'gs://examplebucket/example.mp3'
# Encoding: https://cloud.google.com/
# speech-to-text/docs/reference/rest/v1beta1/RecognitionConfig
encoding='AMR'
sample_rate_hertz=8000
# Language: https://cloud.google.com/
# speech-to-text/docs/languages
language_code='en-US'
client = speech.SpeechClient()
operation = client.long_running_recognize(
audio=speech.types.RecognitionAudio(uri=uri),
config=speech.types.RecognitionConfig(
encoding=encoding,
sample_rate_hertz=sample_rate_hertz,
language_code=language_code,
use_enhanced=True,
model='phone_call',
profanity_filter=False,
enable_automatic_punctuation=True,
enable_word_confidence=True))
op_result = operation.result()
for result in op_result.results:
for alternative in result.alternatives:
print('=' * 20)
print(alternative.transcript)
print(alternative.confidence)
```