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200 lines
6.2 KiB
200 lines
6.2 KiB
"""Trainer 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 distutils.util
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import multiprocessing
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import paddle.v2 as paddle
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from model import DeepSpeech2Model
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from data_utils.data import DataGenerator
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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", default=256, type=int, help="Minibatch size.")
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parser.add_argument(
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"--num_passes",
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default=200,
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type=int,
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help="Training pass number. (default: %(default)s)")
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parser.add_argument(
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"--num_iterations_print",
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default=100,
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type=int,
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help="Number of iterations for every train cost printing. "
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"(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=1024,
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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_gru",
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default=False,
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type=bool,
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help="Use GRU or simple RNN. (default: %(default)s)")
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parser.add_argument(
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"--adam_learning_rate",
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default=5e-4,
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type=float,
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help="Learning rate for ADAM Optimizer. (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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"--use_sortagrad",
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default=True,
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type=distutils.util.strtobool,
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help="Use sortagrad or not. (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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"--max_duration",
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default=27.0,
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type=float,
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help="Audios with duration larger than this will be discarded. "
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"(default: %(default)s)")
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parser.add_argument(
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"--min_duration",
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default=0.0,
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type=float,
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help="Audios with duration smaller than this will be discarded. "
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"(default: %(default)s)")
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parser.add_argument(
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"--shuffle_method",
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default='batch_shuffle_clipped',
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type=str,
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help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
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"'batch_shuffle_batch'. (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=multiprocessing.cpu_count() // 2,
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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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"--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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"--train_manifest_path",
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default='datasets/manifest.train',
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type=str,
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help="Manifest path for training. (default: %(default)s)")
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parser.add_argument(
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"--dev_manifest_path",
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default='datasets/manifest.dev',
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type=str,
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help="Manifest path for validation. (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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"--init_model_path",
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default=None,
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type=str,
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help="If set None, the training will start from scratch. "
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"Otherwise, the training will resume from "
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"the existing model of this path. (default: %(default)s)")
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parser.add_argument(
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"--output_model_dir",
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default="./checkpoints",
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type=str,
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help="Directory for saving models. (default: %(default)s)")
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parser.add_argument(
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"--augmentation_config",
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default=open('conf/augmentation.config', 'r').read(),
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type=str,
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help="Augmentation configuration in json-format. "
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"(default: %(default)s)")
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parser.add_argument(
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"--is_local",
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default=True,
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type=distutils.util.strtobool,
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help="Set to false if running with pserver in paddlecloud. "
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"(default: %(default)s)")
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args = parser.parse_args()
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def train():
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"""DeepSpeech2 training."""
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train_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=args.augmentation_config,
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max_duration=args.max_duration,
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min_duration=args.min_duration,
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specgram_type=args.specgram_type,
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num_threads=args.num_threads_data)
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dev_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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train_batch_reader = train_generator.batch_reader_creator(
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manifest_path=args.train_manifest_path,
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batch_size=args.batch_size,
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min_batch_size=args.trainer_count,
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sortagrad=args.use_sortagrad if args.init_model_path is None else False,
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shuffle_method=args.shuffle_method)
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dev_batch_reader = dev_generator.batch_reader_creator(
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manifest_path=args.dev_manifest_path,
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batch_size=args.batch_size,
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min_batch_size=1, # must be 1, but will have errors.
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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=train_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.init_model_path)
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ds2_model.train(
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train_batch_reader=train_batch_reader,
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dev_batch_reader=dev_batch_reader,
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feeding_dict=train_generator.feeding,
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learning_rate=args.adam_learning_rate,
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gradient_clipping=400,
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num_passes=args.num_passes,
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num_iterations_print=args.num_iterations_print,
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output_model_dir=args.output_model_dir,
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is_local=args.is_local)
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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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train()
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if __name__ == '__main__':
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main()
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