mirror of
https://github.com/mozilla/DeepSpeech.git
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97 lines
3.7 KiB
Python
97 lines
3.7 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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from timeit import default_timer as timer
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import argparse
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import subprocess
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import sys
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import scipy.io.wavfile as wav
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import numpy as np
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from deepspeech.model 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_WEIGHT = 1.75
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# The beta hyperparameter of the CTC decoder. Word insertion weight (penalty)
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WORD_COUNT_WEIGHT = 1.00
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# Valid word insertion weight. This is used to lessen the word insertion penalty
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# when the inserted word is part of the vocabulary
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VALID_WORD_COUNT_WEIGHT = 1.00
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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 convert_samplerate(audio_path):
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sox_cmd = 'sox --norm {} -b 16 -t wav - channels 1 rate 16000'.format(audio_path)
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try:
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p = subprocess.Popen(sox_cmd.split(),
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stderr=subprocess.PIPE, stdout=subprocess.PIPE)
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output, err = p.communicate()
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if p.returncode:
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raise RuntimeError('SoX returned non-zero status')
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except OSError as e:
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raise OSError('SoX not found, use 16kHz files or install it')
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# we already know the header information, get only the data from output
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audio = np.fromstring(output.split('data')[1], dtype=np.int16)
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return 16000, audio
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def main():
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parser = argparse.ArgumentParser(description='Benchmarking tooling for DeepSpeech native_client.')
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parser.add_argument('model', type=str,
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help='Path to the model (protocol buffer binary file)')
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parser.add_argument('audio', type=str,
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help='Path to the audio file to run (WAV format)')
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parser.add_argument('alphabet', type=str,
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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', type=str, nargs='?',
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help='Path to the language model binary file')
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parser.add_argument('trie', type=str, nargs='?',
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help='Path to the language model trie file created with native_client/generate_trie')
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args = parser.parse_args()
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print('Loading model from file %s' % (args.model), file=sys.stderr)
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model_load_start = timer()
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ds = Model(args.model, N_FEATURES, N_CONTEXT, args.alphabet, BEAM_WIDTH)
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model_load_end = timer() - model_load_start
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print('Loaded model in %0.3fs.' % (model_load_end), file=sys.stderr)
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if args.lm and args.trie:
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print('Loading language model from files %s %s' % (args.lm, args.trie), file=sys.stderr)
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lm_load_start = timer()
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ds.enableDecoderWithLM(args.alphabet, args.lm, args.trie, LM_WEIGHT,
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WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
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lm_load_end = timer() - lm_load_start
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print('Loaded language model in %0.3fs.' % (lm_load_end), file=sys.stderr)
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fs, audio = wav.read(args.audio)
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if fs != 16000:
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fs, audio = convert_samplerate(args.audio)
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audio_length = len(audio) * ( 1 / 16000)
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print('Running inference.', file=sys.stderr)
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inference_start = timer()
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print(ds.stt(audio, fs))
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inference_end = timer() - inference_start
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print('Inference took %0.3fs for %0.3fs audio file.' % (inference_end, audio_length), file=sys.stderr)
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if __name__ == '__main__':
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main()
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