mirror of
https://github.com/mozilla/DeepSpeech.git
synced 2025-10-26 11:19:39 +00:00
93 lines
4.2 KiB
Python
93 lines
4.2 KiB
Python
import sys
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import os
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import logging
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import argparse
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import subprocess
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import shlex
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import numpy as np
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import wavTranscriber
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# Debug helpers
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logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
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def main(args):
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parser = argparse.ArgumentParser(description='Transcribe long audio files using webRTC VAD or use the streaming interface')
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parser.add_argument('--aggressive', type=int, choices=range(4), required=False,
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help='Determines how aggressive filtering out non-speech is. (Interger between 0-3)')
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parser.add_argument('--audio', required=False,
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help='Path to the audio file to run (WAV format)')
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parser.add_argument('--model', required=True,
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help='Path to directory that contains all model files (output_graph, lm, trie and alphabet)')
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parser.add_argument('--stream', required=False, action='store_true',
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help='To use deepspeech streaming interface')
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args = parser.parse_args()
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if args.stream is True:
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print("Opening mic for streaming")
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elif args.audio is not None:
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logging.debug("Transcribing audio file @ %s" % args.audio)
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else:
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parser.print_help()
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parser.exit()
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# Point to a path containing the pre-trained models & resolve ~ if used
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dirName = os.path.expanduser(args.model)
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# Resolve all the paths of model files
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output_graph, alphabet, lm, trie = wavTranscriber.resolve_models(dirName)
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# Load output_graph, alpahbet, lm and trie
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model_retval = wavTranscriber.load_model(output_graph, alphabet, lm, trie)
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if args.audio is not None:
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title_names = ['Filename', 'Duration(s)', 'Inference Time(s)', 'Model Load Time(s)', 'LM Load Time(s)']
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print("\n%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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inference_time = 0.0
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# Run VAD on the input file
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waveFile = args.audio
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segments, sample_rate, audio_length = wavTranscriber.vad_segment_generator(waveFile, args.aggressive)
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f = open(waveFile.rstrip(".wav") + ".txt", 'w')
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logging.debug("Saving Transcript @: %s" % waveFile.rstrip(".wav") + ".txt")
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for i, segment in enumerate(segments):
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# Run deepspeech on the chunk that just completed VAD
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logging.debug("Processing chunk %002d" % (i,))
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audio = np.frombuffer(segment, dtype=np.int16)
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output = wavTranscriber.stt(model_retval[0], audio, sample_rate)
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inference_time += output[1]
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logging.debug("Transcript: %s" % output[0])
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f.write(output[0] + " ")
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# Summary of the files processed
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f.close()
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# Extract filename from the full file path
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filename, ext = os.path.split(os.path.basename(waveFile))
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logging.debug("************************************************************************************************************")
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logging.debug("%-30s %-20s %-20s %-20s %s" % (title_names[0], title_names[1], title_names[2], title_names[3], title_names[4]))
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logging.debug("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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logging.debug("************************************************************************************************************")
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print("%-30s %-20.3f %-20.3f %-20.3f %-0.3f" % (filename + ext, audio_length, inference_time, model_retval[1], model_retval[2]))
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else:
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sctx = model_retval[0].createStream()
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subproc = subprocess.Popen(shlex.split('rec -q -V0 -e signed -L -c 1 -b 16 -r 16k -t raw - gain -2'),
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stdout=subprocess.PIPE,
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bufsize=0)
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print('You can start speaking now. Press Control-C to stop recording.')
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try:
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while True:
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data = subproc.stdout.read(512)
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model_retval[0].feedAudioContent(sctx, np.frombuffer(data, np.int16))
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except KeyboardInterrupt:
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print('Transcription: ', model_retval[0].finishStream(sctx))
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subproc.terminate()
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subproc.wait()
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if __name__ == '__main__':
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main(sys.argv[1:])
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