8000 Merge pull request #52 from kkroening/split-silence · PearlStudio/ffmpeg-python@09e6e46 · GitHub
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Merge pull request kkroening#52 from kkroening/split-silence
Add examples: `split_silence` + `transcribe`
2 parents 0672fd0 + 3a818cc commit 09e6e46

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‎examples/requirements.txt

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ffmpeg-python
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google-cloud-speech

‎examples/split_silence.py

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#!/usr/bin/env python
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from __future__ import unicode_literals
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import argparse
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import errno
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import ffmpeg
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import logging
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import os
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import re
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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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DEFAULT_DURATION = 0.3
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DEFAULT_THRESHOLD = -60
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parser = argparse.ArgumentParser(description='Split media into separate chunks wherever silence occurs')
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parser.add_argument('in_filename', help='Input filename (`-` for stdin)')
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parser.add_argument('out_pattern', help='Output filename pattern (e.g. `out/chunk_{:04d}.wav`)')
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parser.add_argument('--silence-threshold', default=DEFAULT_THRESHOLD, type=int, help='Silence threshold (in dB)')
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parser.add_argument('--silence-duration', default=DEFAULT_DURATION, type=float, help='Silence duration')
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parser.add_argument('--start-time', type=float, help='Start time (seconds)')
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parser.add_argument('--end-time', type=float, help='End time (seconds)')
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parser.add_argument('-v', dest='verbose', action='store_true', help='Verbose mode')
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silence_start_re = re.compile(' silence_start: (?P<start>[0-9]+(\.?[0-9]*))$')
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silence_end_re = re.compile(' silence_end: (?P<end>[0-9]+(\.?[0-9]*)) ')
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total_duration_re = re.compile(
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'size=[^ ]+ time=(?P<hours>[0-9]{2}):(?P<minutes>[0-9]{2}):(?P<seconds>[0-9\.]{5}) bitrate=')
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def _logged_popen(cmd_line, *args, **kwargs):
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logger.debug('Running command: {}'.format(subprocess.list2cmdline(cmd_line)))
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return subprocess.Popen(cmd_line, *args, **kwargs)
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def get_chunk_times(in_filename, silence_threshold, silence_duration, start_time=None, end_time=None):
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input_kwargs = {}
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if start_time is not None:
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input_kwargs['ss'] = start_time
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else:
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start_time = 0.
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if end_time is not None:
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input_kwargs['t'] = end_time - start_time
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p = _logged_popen(
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(ffmpeg
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.input(in_filename, **input_kwargs)
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.filter_('silencedetect', n='{}dB'.format(silence_threshold), d=silence_duration)
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.output('-', format='null')
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.compile()
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) + ['-nostats'], # FIXME: use .nostats() once it's implemented in ffmpeg-python.
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stderr=subprocess.PIPE
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)
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output = p.communicate()[1].decode('utf-8')
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if p.returncode != 0:
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sys.stderr.write(output)
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sys.exit(1)
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logger.debug(output)
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lines = output.splitlines()
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# Chunks start when silence ends, and chunks end when silence starts.
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chunk_starts = []
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chunk_ends = []
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for line in lines:
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silence_start_match = silence_start_re.search(line)
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silence_end_match = silence_end_re.search(line)
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total_duration_match = total_duration_re.search(line)
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if silence_start_match:
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chunk_ends.append(float(silence_start_match.group('start')))
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if len(chunk_starts) == 0:
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# Started with non-silence.
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chunk_starts.append(start_time or 0.)
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elif silence_end_match:
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chunk_starts.append(float(silence_end_match.group('end')))
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elif total_duration_match:
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hours = int(total_duration_match.group('hours'))
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minutes = int(total_duration_match.group('minutes'))
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seconds = float(total_duration_match.group('seconds'))
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end_time = hours * 3600 + minutes * 60 + seconds
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if len(chunk_starts) == 0:
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# No silence found.
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chunk_starts.append(start_time)
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if len(chunk_starts) > len(chunk_ends):
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# Finished with non-silence.
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chunk_ends.append(end_time or 10000000.)
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return list(zip(chunk_starts, chunk_ends))
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def _makedirs(path):
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"""Python2-compatible version of ``os.makedirs(path, exist_ok=True)``."""
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try:
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os.makedirs(path)
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except OSError as exc:
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if exc.errno != errno.EEXIST or not os.path.isdir(path):
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raise
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def split_audio(
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in_filename,
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out_pattern,
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silence_threshold=DEFAULT_THRESHOLD,
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silence_duration=DEFAULT_DURATION,
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start_time=None,
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end_time=None,
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verbose=False,
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):
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chunk_times = get_chunk_times(in_filename, silence_threshold, silence_duration, start_time, end_time)
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for i, (start_time, end_time) in enumerate(chunk_times):
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time = end_time - start_time
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out_filename = out_pattern.format(i, i=i)
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_makedirs(os.path.dirname(out_filename))
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logger.info('{}: start={:.02f}, end={:.02f}, duration={:.02f}'.format(out_filename, start_time, end_time,
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time))
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_logged_popen(
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(ffmpeg
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.input(in_filename, ss=start_time, t=time)
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.output(out_filename)
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.overwrite_output()
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.compile()
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),
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stdout=subprocess.PIPE if not verbose else None,
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stderr=subprocess.PIPE if not verbose else None,
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).communicate()
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if __name__ == '__main__':
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kwargs = vars(parser.parse_args())
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if kwargs['verbose']:
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logging.basicConfig(level=logging.DEBUG, format='%(levels): %(message)s')
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logger.setLevel(logging.DEBUG)
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split_audio(**kwargs)

‎examples/transcribe.py

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#!/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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