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220 lines
6.9 KiB
220 lines
6.9 KiB
"""Deployment 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 gzip
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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 deep_speech2
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from deploy.swig_decoders_wrapper import *
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from error_rate import wer
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import utils
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import time
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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=4,
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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=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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"--decode_method",
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default='beam_search',
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type=str,
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help="Method for ctc decoding: beam_search or beam_search_batch. "
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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=20,
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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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"--num_results_per_sample",
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default=1,
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type=int,
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help="Number of output per sample in beam search. (default: %(default)d)")
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parser.add_argument(
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"--language_model_path",
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default="/home/work/liuyibing/lm_bak/common_crawl_00.prune01111.trie.klm",
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#default="ptb_all.arpa",
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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=0.26,
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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=0.1,
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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=1.0,
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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 infer():
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"""Deployment for DeepSpeech2."""
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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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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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# 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(len(infer_data))
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]
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# targe transcription
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target_transcription = [
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''.join(
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[data_generator.vocab_list[index] for index in infer_data[i][1]])
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for i, probs in enumerate(probs_split)
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]
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# external scorer
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ext_scorer = Scorer(
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alpha=args.alpha, beta=args.beta, model_path=args.language_model_path)
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## decode and print
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time_begin = time.time()
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wer_sum, wer_counter = 0, 0
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batch_beam_results = []
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if args.decode_method == 'beam_search':
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for i, probs in enumerate(probs_split):
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beam_result = ctc_beam_search_decoder(
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probs_seq=probs,
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beam_size=args.beam_size,
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vocabulary=data_generator.vocab_list,
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blank_id=len(data_generator.vocab_list),
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cutoff_prob=args.cutoff_prob,
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ext_scoring_func=ext_scorer, )
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batch_beam_results += [beam_result]
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else:
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batch_beam_results = ctc_beam_search_decoder_batch(
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probs_split=probs_split,
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beam_size=args.beam_size,
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vocabulary=data_generator.vocab_list,
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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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cutoff_prob=args.cutoff_prob,
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ext_scoring_func=ext_scorer, )
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for i, beam_result in enumerate(batch_beam_results):
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print("\nTarget Transcription:\t%s" % target_transcription[i])
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print("Beam %d: %f \t%s" % (0, beam_result[0][0], beam_result[0][1]))
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wer_cur = wer(target_transcription[i], beam_result[0][1])
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wer_sum += wer_cur
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wer_counter += 1
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print("cur wer = %f , average wer = %f" %
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(wer_cur, wer_sum / wer_counter))
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time_end = time.time()
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print("total time = %f" % (time_end - time_begin))
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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=1)
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infer()
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if __name__ == '__main__':
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main()
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