mirror of
https://github.com/kkroening/ffmpeg-python.git
synced 2025-04-06 04:15:44 +08:00
63 lines
1.7 KiB
Python
Executable File
63 lines
1.7 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import unicode_literals
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from google.cloud import speech
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from google.cloud.speech import enums
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from google.cloud.speech import types
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import argparse
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import ffmpeg
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import logging
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import subprocess
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import sys
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logging.basicConfig(level=logging.INFO, format='%(message)s')
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logger = logging.getLogger(__file__)
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logger.setLevel(logging.INFO)
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parser = argparse.ArgumentParser(description='Convert speech audio to text using Google Speech API')
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parser.add_argument('in_filename', help='Input filename (`-` for stdin)')
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def decode_audio(in_filename, **input_kwargs):
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p = subprocess.Popen(
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(ffmpeg
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.input(in_filename, **input_kwargs)
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.output('-', format='s16le', acodec='pcm_s16le', ac=1, ar='16k')
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.overwrite_output()
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.compile()
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),
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE
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)
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out = p.communicate()
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if p.returncode != 0:
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sys.stderr.write(out[1])
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sys.exit(1)
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return out[0]
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def get_transcripts(audio_data):
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client = speech.SpeechClient()
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audio = types.RecognitionAudio(content=audio_data)
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config = types.RecognitionConfig(
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encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,
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sample_rate_hertz=16000,
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language_code='en-US'
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)
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response = client.recognize(config, audio)
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return [result.alternatives[0].transcript for result in response.results]
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def transcribe(in_filename):
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audio_data = decode_audio(in_filename)
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transcripts = get_transcripts(audio_data)
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for transcript in transcripts:
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print(repr(transcript.encode('utf-8')))
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if __name__ == '__main__':
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args = parser.parse_args()
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transcribe(args.in_filename)
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