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192 lines
5.9 KiB
192 lines
5.9 KiB
"""Inferer for DeepSpeech2 model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import distutils.util
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import multiprocessing
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import paddle.v2 as paddle
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from data_utils.data import DataGenerator
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from model import DeepSpeech2Model
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from error_rate import wer, cer
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import utils
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--num_samples",
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default=10,
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type=int,
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help="Number of samples for inference. (default: %(default)s)")
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parser.add_argument(
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"--num_conv_layers",
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default=2,
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type=int,
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help="Convolution layer number. (default: %(default)s)")
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parser.add_argument(
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"--num_rnn_layers",
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default=3,
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type=int,
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help="RNN layer number. (default: %(default)s)")
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parser.add_argument(
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"--rnn_layer_size",
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default=2048,
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type=int,
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help="RNN layer cell number. (default: %(default)s)")
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parser.add_argument(
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"--share_rnn_weights",
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default=True,
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type=distutils.util.strtobool,
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help="Whether to share input-hidden weights between forword and backward "
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"directional simple RNNs. Only available when use_gru=False. "
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"(default: %(default)s)")
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parser.add_argument(
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"--use_gru",
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default=False,
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type=distutils.util.strtobool,
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help="Use GRU or simple RNN. (default: %(default)s)")
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parser.add_argument(
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"--use_gpu",
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default=True,
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type=distutils.util.strtobool,
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help="Use gpu or not. (default: %(default)s)")
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parser.add_argument(
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"--num_threads_data",
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default=1,
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type=int,
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help="Number of cpu threads for preprocessing data. (default: %(default)s)")
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parser.add_argument(
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"--num_processes_beam_search",
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default=multiprocessing.cpu_count() // 2,
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type=int,
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help="Number of cpu processes for beam search. (default: %(default)s)")
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parser.add_argument(
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"--specgram_type",
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default='linear',
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type=str,
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help="Feature type of audio data: 'linear' (power spectrum)"
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" or 'mfcc'. (default: %(default)s)")
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parser.add_argument(
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"--trainer_count",
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default=8,
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type=int,
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help="Trainer number. (default: %(default)s)")
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parser.add_argument(
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"--mean_std_filepath",
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default='mean_std.npz',
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type=str,
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help="Manifest path for normalizer. (default: %(default)s)")
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parser.add_argument(
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"--decode_manifest_path",
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default='datasets/manifest.test',
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type=str,
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help="Manifest path for decoding. (default: %(default)s)")
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parser.add_argument(
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"--model_filepath",
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default='checkpoints/params.latest.tar.gz',
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type=str,
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help="Model filepath. (default: %(default)s)")
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parser.add_argument(
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"--vocab_filepath",
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default='datasets/vocab/eng_vocab.txt',
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type=str,
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help="Vocabulary filepath. (default: %(default)s)")
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parser.add_argument(
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"--decode_method",
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default='beam_search',
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type=str,
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help="Method for ctc decoding: best_path or beam_search. "
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"(default: %(default)s)")
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parser.add_argument(
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"--beam_size",
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default=500,
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type=int,
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help="Width for beam search decoding. (default: %(default)d)")
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parser.add_argument(
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"--language_model_path",
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default="lm/data/common_crawl_00.prune01111.trie.klm",
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type=str,
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help="Path for language model. (default: %(default)s)")
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parser.add_argument(
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"--alpha",
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default=0.36,
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type=float,
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help="Parameter associated with language model. (default: %(default)f)")
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parser.add_argument(
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"--beta",
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default=0.25,
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type=float,
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help="Parameter associated with word count. (default: %(default)f)")
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parser.add_argument(
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"--cutoff_prob",
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default=0.99,
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type=float,
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help="The cutoff probability of pruning"
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"in beam search. (default: %(default)f)")
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parser.add_argument(
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"--error_rate_type",
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default='wer',
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choices=['wer', 'cer'],
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type=str,
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help="Error rate type for evaluation. 'wer' for word error rate and 'cer' "
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"for character error rate. "
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"(default: %(default)s)")
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args = parser.parse_args()
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def infer():
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"""Inference for DeepSpeech2."""
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_filepath,
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mean_std_filepath=args.mean_std_filepath,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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num_threads=args.num_threads_data)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.decode_manifest_path,
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batch_size=args.num_samples,
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min_batch_size=1,
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sortagrad=False,
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shuffle_method=None)
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infer_data = batch_reader().next()
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ds2_model = DeepSpeech2Model(
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vocab_size=data_generator.vocab_size,
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_layer_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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pretrained_model_path=args.model_filepath,
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share_rnn_weights=args.share_rnn_weights)
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result_transcripts = ds2_model.infer_batch(
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infer_data=infer_data,
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decode_method=args.decode_method,
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beam_alpha=args.alpha,
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beam_beta=args.beta,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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vocab_list=data_generator.vocab_list,
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language_model_path=args.language_model_path,
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num_processes=args.num_processes_beam_search)
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error_rate_func = cer if args.error_rate_type == 'cer' else wer
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target_transcripts = [
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''.join([data_generator.vocab_list[token] for token in transcript])
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for _, transcript in infer_data
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]
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for target, result in zip(target_transcripts, result_transcripts):
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print("\nTarget Transcription: %s\nOutput Transcription: %s" %
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(target, result))
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print("Current error rate [%s] = %f" %
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(args.error_rate_type, error_rate_func(target, result)))
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def main():
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utils.print_arguments(args)
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paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
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infer()
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if __name__ == '__main__':
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main()
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