#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import argparse import numpy as np import wave from deepspeech import Model # These constants control the beam search decoder # Beam width used in the CTC decoder when building candidate transcriptions BEAM_WIDTH = 500 # The alpha hyperparameter of the CTC decoder. Language Model weight LM_ALPHA = 0.75 # The beta hyperparameter of the CTC decoder. Word insertion bonus. LM_BETA = 1.85 def main(): parser = argparse.ArgumentParser(description='Running DeepSpeech inference.') parser.add_argument('--model', required=True, help='Path to the model (protocol buffer binary file)') parser.add_argument('--lm', nargs='?', help='Path to the language model binary file') parser.add_argument('--trie', nargs='?', help='Path to the language model trie file created with native_client/generate_trie') parser.add_argument('--audio1', required=True, help='First audio file to use in interleaved streams') parser.add_argument('--audio2', required=True, help='Second audio file to use in interleaved streams') args = parser.parse_args() ds = Model(args.model, BEAM_WIDTH) if args.lm and args.trie: ds.enableDecoderWithLM(args.lm, args.trie, LM_ALPHA, LM_BETA) fin = wave.open(args.audio1, 'rb') fs1 = fin.getframerate() audio1 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) fin.close() fin = wave.open(args.audio2, 'rb') fs2 = fin.getframerate() audio2 = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) fin.close() stream1 = ds.createStream() stream2 = ds.createStream() splits1 = np.array_split(audio1, 10) splits2 = np.array_split(audio2, 10) for part1, part2 in zip(splits1, splits2): ds.feedAudioContent(stream1, part1) ds.feedAudioContent(stream2, part2) print(ds.finishStream(stream1)) print(ds.finishStream(stream2)) if __name__ == '__main__': main()