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195 lines
6.3 KiB
195 lines
6.3 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 sys
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import os
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import argparse
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import gzip
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import time
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import distutils.util
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import paddle.v2 as paddle
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from model import deep_speech2
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from data_utils.data import DataGenerator
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--batch_size", default=32, type=int, help="Minibatch size.")
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parser.add_argument(
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"--num_passes",
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default=20,
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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_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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"--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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"--trainer_count",
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default=4,
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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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"--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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"--augmentation_config",
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default='{}',
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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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args = parser.parse_args()
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def train():
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"""
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DeepSpeech2 training.
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"""
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# initialize data generator
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def data_generator():
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return 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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train_generator = data_generator()
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test_generator = data_generator()
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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(
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train_generator.vocab_size))
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cost = deep_speech2(
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audio_data=audio_data,
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text_data=text_data,
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dict_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_size=args.rnn_layer_size,
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is_inference=False)
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# create/load parameters and optimizer
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if args.init_model_path is None:
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parameters = paddle.parameters.create(cost)
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else:
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if not os.path.isfile(args.init_model_path):
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raise IOError("Invalid model!")
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parameters = paddle.parameters.Parameters.from_tar(
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gzip.open(args.init_model_path))
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optimizer = paddle.optimizer.Adam(
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learning_rate=args.adam_learning_rate, gradient_clipping_threshold=400)
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trainer = paddle.trainer.SGD(
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cost=cost, parameters=parameters, update_equation=optimizer)
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# prepare data reader
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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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batch_shuffle=True)
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test_batch_reader = test_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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batch_shuffle=False)
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# create event handler
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def event_handler(event):
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global start_time, cost_sum, cost_counter
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if isinstance(event, paddle.event.EndIteration):
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cost_sum += event.cost
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cost_counter += 1
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if (event.batch_id + 1) % 100 == 0:
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print("\nPass: %d, Batch: %d, TrainCost: %f" % (
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event.pass_id, event.batch_id + 1, cost_sum / cost_counter))
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cost_sum, cost_counter = 0.0, 0
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with gzip.open("params.tar.gz", 'w') as f:
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parameters.to_tar(f)
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else:
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sys.stdout.write('.')
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sys.stdout.flush()
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if isinstance(event, paddle.event.BeginPass):
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start_time = time.time()
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cost_sum, cost_counter = 0.0, 0
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if isinstance(event, paddle.event.EndPass):
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result = trainer.test(
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reader=test_batch_reader, feeding=test_generator.feeding)
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print("\n------- Time: %d sec, Pass: %d, ValidationCost: %s" %
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(time.time() - start_time, event.pass_id, result.cost))
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# run train
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trainer.train(
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reader=train_batch_reader,
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event_handler=event_handler,
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num_passes=args.num_passes,
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feeding=train_generator.feeding)
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def main():
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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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