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150 lines
5.6 KiB
150 lines
5.6 KiB
"""Beam search parameters tuning 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 numpy as np
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import distutils.util
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import argparse
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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
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NUM_CPU = multiprocessing.cpu_count() // 2
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parser = argparse.ArgumentParser(description=__doc__)
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def add_arg(argname, type, default, help, **kwargs):
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type = distutils.util.strtobool if type == bool else type
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parser.add_argument(
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"--" + argname,
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default=default,
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type=type,
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help=help + ' Default: %(default)s.',
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**kwargs)
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# yapf: disable
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add_arg('num_samples', int, 100, "# of samples to infer.")
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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('parallels_bsearch',int, NUM_CPU,"# of CPUs for beam search.")
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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('num_alphas', int, 14, "# of alpha candidates for tuning.")
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add_arg('num_betas', int, 20, "# of beta candidates for tuning.")
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add_arg('alpha_from', float, 0.1, "Where alpha starts tuning from.")
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add_arg('alpha_to', float, 0.36, "Where alpha ends tuning with.")
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add_arg('beta_from', float, 0.05, "Where beta starts tuning from.")
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add_arg('beta_to', float, 0.36, "Where beta ends tuning with.")
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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('tune_manifest', str,
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'datasets/manifest.test',
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"Filepath of manifest to tune.")
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add_arg('mean_std_path', str,
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'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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'datasets/vocab/eng_vocab.txt',
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"Filepath of vocabulary.")
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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('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('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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args = parser.parse_args()
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# yapf: disable
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def tune():
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"""Tune parameters alpha and beta on one minibatch."""
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if not args.num_alphas >= 0:
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raise ValueError("num_alphas must be non-negative!")
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if not args.num_betas >= 0:
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raise ValueError("num_betas must be non-negative!")
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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=1)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.tune_manifest,
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batch_size=args.num_samples,
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sortagrad=False,
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shuffle_method=None)
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tune_data = batch_reader().next()
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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 tune_data
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]
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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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# create grid for search
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cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
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cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
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params_grid = [(alpha, beta) for alpha in cand_alphas
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for beta in cand_betas]
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## tune parameters in loop
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for alpha, beta in params_grid:
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result_transcripts = ds2_model.infer_batch(
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infer_data=tune_data,
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decoder_method='ctc_beam_search',
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beam_alpha=alpha,
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beam_beta=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.parallels_bsearch)
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wer_sum, num_ins = 0.0, 0
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for target, result in zip(target_transcripts, result_transcripts):
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wer_sum += wer(target, result)
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num_ins += 1
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print("alpha = %f\tbeta = %f\tWER = %f" %
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(alpha, beta, wer_sum / num_ins))
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def print_arguments(args):
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print("----------- Configuration Arguments -----------")
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for arg, value in sorted(vars(args).iteritems()):
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print("%s: %s" % (arg, value))
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print("------------------------------------------------")
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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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tune()
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if __name__ == '__main__':
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main()
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