mirror of
https://github.com/mozilla/DeepSpeech.git
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81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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from __future__ import absolute_import, division, print_function
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import argparse
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import numpy as np
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import wave
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from deepspeech import Model
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# These constants control the beam search decoder
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# Beam width used in the CTC decoder when building candidate transcriptions
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BEAM_WIDTH = 500
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# The alpha hyperparameter of the CTC decoder. Language Model weight
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LM_ALPHA = 0.75
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# The beta hyperparameter of the CTC decoder. Word insertion bonus.
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LM_BETA = 1.85
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# These constants are tied to the shape of the graph used (changing them changes
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# the geometry of the first layer), so make sure you use the same constants that
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# were used during training
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# Number of MFCC features to use
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N_FEATURES = 26
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# Size of the context window used for producing timesteps in the input vector
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N_CONTEXT = 9
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def main():
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parser = argparse.ArgumentParser(description='Running DeepSpeech inference.')
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parser.add_argument('--model', required=True,
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help='Path to the model (protocol buffer binary file)')
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parser.add_argument('--alphabet', required=True,
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help='Path to the configuration file specifying the alphabet used by the network')
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parser.add_argument('--lm', nargs='?',
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help='Path to the language model binary file')
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parser.add_argument('--trie', nargs='?',
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help='Path to the language model trie file created with native_client/generate_trie')
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parser.add_argument('--audio1', required=True,
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help='First audio file to use in interleaved streams')
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parser.add_argument('--audio2', required=True,
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help='Second audio file to use in interleaved streams')
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args = parser.parse_args()
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ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
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if args.lm and args.trie:
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ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_ALPHA, LM_BETA)
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fin = wave.open(args.audio1, 'rb')
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fs1 = fin.getframerate()
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audio1 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
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fin.close()
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fin = wave.open(args.audio2, 'rb')
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fs2 = fin.getframerate()
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audio2 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16)
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fin.close()
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stream1 = ds.setupStream(sample_rate=fs1)
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stream2 = ds.setupStream(sample_rate=fs2)
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splits1 = np.array_split(audio1, 10)
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splits2 = np.array_split(audio2, 10)
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for part1, part2 in zip(splits1, splits2):
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ds.feedAudioContent(stream1, part1)
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ds.feedAudioContent(stream2, part2)
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print(ds.finishStream(stream1))
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print(ds.finishStream(stream2))
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if __name__ == '__main__':
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main()
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