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217 lines
6.9 KiB
217 lines
6.9 KiB
"""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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import utils
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--batch_size",
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default=128,
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type=int,
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help="Minibatch size for parameters tuning. (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=512,
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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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"--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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"--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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"--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(),
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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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"--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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"--tune_manifest_path",
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default='datasets/manifest.dev',
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type=str,
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help="Manifest path for tuning. (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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"--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_from",
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default=0.1,
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type=float,
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help="Where alpha starts from. (default: %(default)f)")
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parser.add_argument(
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"--num_alphas",
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default=14,
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type=int,
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help="Number of candidate alphas. (default: %(default)d)")
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parser.add_argument(
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"--alpha_to",
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default=0.36,
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type=float,
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help="Where alpha ends with. (default: %(default)f)")
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parser.add_argument(
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"--beta_from",
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default=0.05,
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type=float,
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help="Where beta starts from. (default: %(default)f)")
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parser.add_argument(
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"--num_betas",
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default=20,
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type=float,
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help="Number of candidate betas. (default: %(default)d)")
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parser.add_argument(
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"--beta_to",
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default=1.0,
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type=float,
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help="Where beta ends with. (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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args = parser.parse_args()
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def tune():
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"""Tune parameters alpha and beta for the CTC beam search decoder
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incrementally. The optimal parameters up to now would be output real time
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at the end of each minibatch data, until all the development data is
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taken into account. And the tuning process can be terminated at any time
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as long as the two parameters get stable.
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"""
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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_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.tune_manifest_path,
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batch_size=args.batch_size,
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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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pretrained_model_path=args.model_filepath)
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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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wer_sum = [0.0 for i in xrange(len(params_grid))]
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ave_wer = [0.0 for i in xrange(len(params_grid))]
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num_ins = 0
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num_batches = 0
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## incremental tuning parameters over multiple batches
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for infer_data in batch_reader():
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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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num_ins += len(target_transcripts)
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# grid search
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for index, (alpha, beta) in enumerate(params_grid):
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result_transcripts = ds2_model.infer_batch(
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infer_data=infer_data,
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decode_method='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.language_model_path,
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num_processes=args.num_processes_beam_search)
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for target, result in zip(target_transcripts, result_transcripts):
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wer_sum[index] += wer(target, result)
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ave_wer[index] = wer_sum[index] / num_ins
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print("alpha = %f, beta = %f, WER = %f" %
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(alpha, beta, ave_wer[index]))
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# output on-line tuning result at the the end of current batch
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ave_wer_min = min(ave_wer)
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min_index = ave_wer.index(ave_wer_min)
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print("Finish batch %d, optimal (alpha, beta, WER) = (%f, %f, %f)\n" %
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(num_batches, params_grid[min_index][0],
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params_grid[min_index][1], ave_wer_min))
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num_batches += 1
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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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tune()
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if __name__ == '__main__':
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main()
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