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218 lines
6.8 KiB
218 lines
6.8 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 distutils.util
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import argparse
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import gzip
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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 deep_speech2
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from decoder import *
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from lm.lm_scorer import LmScorer
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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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"--num_samples",
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default=100,
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type=int,
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help="Number of samples 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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"--num_threads_data",
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default=multiprocessing.cpu_count(),
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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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"--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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"--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/1Billion.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 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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# initialize data generator
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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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num_threads=args.num_threads_data)
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# create network config
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# paddle.data_type.dense_array is used for variable batch input.
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# The size 161 * 161 is only an placeholder value and the real shape
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# of input batch data will be induced during training.
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audio_data = paddle.layer.data(
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name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
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text_data = paddle.layer.data(
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name="transcript_text",
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type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
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output_probs = deep_speech2(
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audio_data=audio_data,
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text_data=text_data,
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dict_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_size=args.rnn_layer_size,
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is_inference=True)
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# load parameters
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parameters = paddle.parameters.Parameters.from_tar(
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gzip.open(args.model_filepath))
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# prepare infer 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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sortagrad=False,
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shuffle_method=None)
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# get one batch data for tuning
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infer_data = batch_reader().next()
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# run inference
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infer_results = paddle.infer(
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output_layer=output_probs, parameters=parameters, input=infer_data)
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num_steps = len(infer_results) // len(infer_data)
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probs_split = [
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infer_results[i * num_steps:(i + 1) * num_steps]
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for i in xrange(0, len(infer_data))
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]
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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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ext_scorer = LmScorer(args.alpha_from, args.beta_from,
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args.language_model_path)
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## tune parameters in loop
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for alpha, beta in params_grid:
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wer_sum, wer_counter = 0, 0
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# reset scorer
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ext_scorer.reset_params(alpha, beta)
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# beam search using multiple processes
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beam_search_results = ctc_beam_search_decoder_batch(
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probs_split=probs_split,
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vocabulary=data_generator.vocab_list,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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blank_id=len(data_generator.vocab_list),
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num_processes=args.num_processes_beam_search,
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ext_scoring_func=ext_scorer, )
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for i, beam_search_result in enumerate(beam_search_results):
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target_transcription = ''.join([
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data_generator.vocab_list[index] for index in infer_data[i][1]
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])
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wer_sum += wer(target_transcription, beam_search_result[0][1])
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wer_counter += 1
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print("alpha = %f\tbeta = %f\tWER = %f" %
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(alpha, beta, wer_sum / wer_counter))
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def main():
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paddle.init(use_gpu=args.use_gpu, trainer_count=1)
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tune()
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if __name__ == '__main__':
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main()
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