mirror of
https://github.com/mozilla/DeepSpeech.git
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851fb4ea90
Changes: 1. Added streaming API support to the GUI tool 2. Minor modifciations to how models are loaded upon repeated transcriptions 3. Updated to Deepspeech v0.3.0 4. Image in the documentation changed Changes v2: 1. Added streaming support to cmd interface also
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 and len(sys.argv[1:]) == 3:
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print("Opening mic for streaming")
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elif args.audio is not None and len(sys.argv[1:]) == 6:
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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].setupStream()
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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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