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122 lines
4.9 KiB
122 lines
4.9 KiB
"""Evaluation 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 functools
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import paddle.v2 as paddle
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from data_utils.data import DataGenerator
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from models.model import DeepSpeech2Model
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from utils.error_rate import wer, cer
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from utils.utility import add_arguments, print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('batch_size', int, 128, "Minibatch size.")
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add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
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add_arg('beam_size', int, 500, "Beam search width.")
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add_arg('num_proc_bsearch', int, 12, "# of CPUs for beam search.")
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add_arg('num_proc_data', int, 12, "# of CPUs for data preprocessing.")
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('alpha', float, 0.36, "Coef of LM for beam search.")
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add_arg('beta', float, 0.25, "Coef of WC for beam search.")
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add_arg('cutoff_prob', float, 0.99, "Cutoff probability for pruning.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
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"bi-directional RNNs. Not for GRU.")
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add_arg('test_manifest', str,
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'data/librispeech/manifest.test-clean',
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"Filepath of manifest to evaluate.")
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add_arg('mean_std_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'data/librispeech/eng_vocab.txt',
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"Filepath of vocabulary.")
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add_arg('model_path', str,
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'./checkpoints/params.latest.tar.gz',
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('lang_model_path', str,
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'lm/data/common_crawl_00.prune01111.trie.klm',
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"Filepath for language model.")
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add_arg('decoding_method', str,
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'ctc_beam_search',
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"Decoding method. Options: ctc_beam_search, ctc_greedy",
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choices = ['ctc_beam_search', 'ctc_greedy'])
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add_arg('error_rate_type', str,
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'wer',
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"Error rate type for evaluation.",
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choices=['wer', 'cer'])
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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# yapf: disable
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args = parser.parse_args()
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def evaluate():
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"""Evaluate on whole test data for DeepSpeech2."""
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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num_threads=args.num_proc_data)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.test_manifest,
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batch_size=args.batch_size,
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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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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_path,
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share_rnn_weights=args.share_rnn_weights)
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error_rate_func = cer if args.error_rate_type == 'cer' else wer
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error_sum, num_ins = 0.0, 0
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for infer_data in batch_reader():
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result_transcripts = ds2_model.infer_batch(
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infer_data=infer_data,
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decoding_method=args.decoding_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.lang_model_path,
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num_processes=args.num_proc_bsearch)
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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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error_sum += error_rate_func(target, result)
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num_ins += 1
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print("Error rate [%s] (%d/?) = %f" %
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(args.error_rate_type, num_ins, error_sum / num_ins))
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print("Final error rate [%s] (%d/%d) = %f" %
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(args.error_rate_type, num_ins, num_ins, error_sum / num_ins))
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def main():
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print_arguments(args)
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paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
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evaluate()
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if __name__ == '__main__':
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main()
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