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58 lines
2.1 KiB
Python
58 lines
2.1 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 sys
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import scipy.io.wavfile as wav
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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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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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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_WEIGHT,
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WORD_COUNT_WEIGHT, VALID_WORD_COUNT_WEIGHT)
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fs, audio = wav.read(args.audio)
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print(ds.stt(audio, fs))
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