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140 lines
5.3 KiB
140 lines
5.3 KiB
"""Trainer for DeepSpeech2 model."""
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import argparse
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import functools
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import io
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from model_utils.model import DeepSpeech2Model
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from model_utils.model_check import check_cuda, check_version
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from data_utils.data import DataGenerator
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from utils.utility import add_arguments, print_arguments
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import paddle.fluid as fluid
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('batch_size', int, 256, "Minibatch size.")
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add_arg('num_epoch', int, 200, "# of training epochs.")
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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_iter_print', int, 100, "Every # batch for printing "
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"train cost.")
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add_arg('save_epoch', int, 10, "# Every # batch for save checkpoint and modle params ")
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add_arg('num_samples', int, 10000, "The num of train samples.")
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add_arg('learning_rate', float, 5e-4, "Learning rate.")
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add_arg('max_duration', float, 27.0, "Longest audio duration allowed.")
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add_arg('min_duration', float, 0.0, "Shortest audio duration allowed.")
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add_arg('test_off', bool, False, "Turn off testing.")
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add_arg('use_sortagrad', bool, True, "Use SortaGrad or not.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('is_local', bool, True, "Use pserver 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('init_from_pretrained_model',str,
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None,
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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('train_manifest', str,
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'data/librispeech/manifest.train',
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"Filepath of train manifest.")
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add_arg('dev_manifest', str,
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'data/librispeech/manifest.dev-clean',
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"Filepath of validation manifest.")
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add_arg('mean_std_path', str,
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'data/librispeech/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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'data/librispeech/vocab.txt',
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"Filepath of vocabulary.")
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add_arg('output_model_dir', str,
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"./checkpoints/libri",
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"Directory for saving checkpoints.")
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add_arg('augment_conf_path',str,
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'conf/augmentation.config',
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"Filepath of augmentation configuration file (json-format).")
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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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add_arg('shuffle_method', str,
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'batch_shuffle_clipped',
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"Shuffle method.",
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choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
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# yapf: disable
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args = parser.parse_args()
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def train():
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"""DeepSpeech2 training."""
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# check if set use_gpu=True in paddlepaddle cpu version
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check_cuda(args.use_gpu)
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# check if paddlepaddle version is satisfied
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check_version()
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if args.use_gpu:
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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train_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=io.open(args.augment_conf_path, mode='r', encoding='utf8').read(),
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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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place=place)
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dev_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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place = place)
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train_batch_reader = train_generator.batch_reader_creator(
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manifest_path=args.train_manifest,
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batch_size=args.batch_size,
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sortagrad=args.use_sortagrad if args.init_from_pretrained_model 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,
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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=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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share_rnn_weights=args.share_rnn_weights,
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place=place,
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init_from_pretrained_model=args.init_from_pretrained_model,
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output_model_dir=args.output_model_dir)
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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.learning_rate,
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gradient_clipping=400,
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batch_size=args.batch_size,
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num_samples=args.num_samples,
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num_epoch=args.num_epoch,
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save_epoch=args.save_epoch,
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num_iterations_print=args.num_iter_print,
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test_off=args.test_off)
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def main():
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print_arguments(args)
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train()
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if __name__ == '__main__':
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main()
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