You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
130 lines
5.2 KiB
130 lines
5.2 KiB
"""Trainer for DeepSpeech2 model."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import argparse
|
|
import functools
|
|
import paddle.v2 as paddle
|
|
from model_utils.model import DeepSpeech2Model
|
|
from data_utils.data import DataGenerator
|
|
from utils.utility import add_arguments, print_arguments
|
|
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
add_arg = functools.partial(add_arguments, argparser=parser)
|
|
# yapf: disable
|
|
add_arg('batch_size', int, 256, "Minibatch size.")
|
|
add_arg('trainer_count', int, 8, "# of Trainers (CPUs or GPUs).")
|
|
add_arg('num_passes', int, 200, "# of training epochs.")
|
|
add_arg('num_proc_data', int, 16, "# of CPUs for data preprocessing.")
|
|
add_arg('num_conv_layers', int, 2, "# of convolution layers.")
|
|
add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
|
|
add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
|
|
add_arg('num_iter_print', int, 100, "Every # iterations for printing "
|
|
"train cost.")
|
|
add_arg('learning_rate', float, 5e-4, "Learning rate.")
|
|
add_arg('max_duration', float, 27.0, "Longest audio duration allowed.")
|
|
add_arg('min_duration', float, 0.0, "Shortest audio duration allowed.")
|
|
add_arg('test_off', bool, False, "Turn off testing.")
|
|
add_arg('use_sortagrad', bool, True, "Use SortaGrad or not.")
|
|
add_arg('use_gpu', bool, True, "Use GPU or not.")
|
|
add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
|
|
add_arg('is_local', bool, True, "Use pserver or not.")
|
|
add_arg('share_rnn_weights',bool, True, "Share input-hidden weights across "
|
|
"bi-directional RNNs. Not for GRU.")
|
|
add_arg('train_manifest', str,
|
|
'data/librispeech/manifest.train',
|
|
"Filepath of train manifest.")
|
|
add_arg('dev_manifest', str,
|
|
'data/librispeech/manifest.dev-clean',
|
|
"Filepath of validation manifest.")
|
|
add_arg('mean_std_path', str,
|
|
'data/librispeech/mean_std.npz',
|
|
"Filepath of normalizer's mean & std.")
|
|
add_arg('vocab_path', str,
|
|
'data/librispeech/vocab.txt',
|
|
"Filepath of vocabulary.")
|
|
add_arg('init_model_path', str,
|
|
None,
|
|
"If None, the training starts from scratch, "
|
|
"otherwise, it resumes from the pre-trained model.")
|
|
add_arg('output_model_dir', str,
|
|
"./checkpoints/libri",
|
|
"Directory for saving checkpoints.")
|
|
add_arg('augment_conf_path',str,
|
|
'conf/augmentation.config',
|
|
"Filepath of augmentation configuration file (json-format).")
|
|
add_arg('specgram_type', str,
|
|
'linear',
|
|
"Audio feature type. Options: linear, mfcc.",
|
|
choices=['linear', 'mfcc'])
|
|
add_arg('shuffle_method', str,
|
|
'batch_shuffle_clipped',
|
|
"Shuffle method.",
|
|
choices=['instance_shuffle', 'batch_shuffle', 'batch_shuffle_clipped'])
|
|
# yapf: disable
|
|
args = parser.parse_args()
|
|
|
|
|
|
def train():
|
|
"""DeepSpeech2 training."""
|
|
train_generator = DataGenerator(
|
|
vocab_filepath=args.vocab_path,
|
|
mean_std_filepath=args.mean_std_path,
|
|
augmentation_config=open(args.augment_conf_path, 'r').read(),
|
|
max_duration=args.max_duration,
|
|
min_duration=args.min_duration,
|
|
specgram_type=args.specgram_type,
|
|
num_threads=args.num_proc_data)
|
|
dev_generator = DataGenerator(
|
|
vocab_filepath=args.vocab_path,
|
|
mean_std_filepath=args.mean_std_path,
|
|
augmentation_config="{}",
|
|
specgram_type=args.specgram_type,
|
|
num_threads=args.num_proc_data)
|
|
train_batch_reader = train_generator.batch_reader_creator(
|
|
manifest_path=args.train_manifest,
|
|
batch_size=args.batch_size,
|
|
min_batch_size=args.trainer_count,
|
|
sortagrad=args.use_sortagrad if args.init_model_path is None else False,
|
|
shuffle_method=args.shuffle_method)
|
|
dev_batch_reader = dev_generator.batch_reader_creator(
|
|
manifest_path=args.dev_manifest,
|
|
batch_size=args.batch_size,
|
|
min_batch_size=1, # must be 1, but will have errors.
|
|
sortagrad=False,
|
|
shuffle_method=None)
|
|
|
|
ds2_model = DeepSpeech2Model(
|
|
vocab_size=train_generator.vocab_size,
|
|
num_conv_layers=args.num_conv_layers,
|
|
num_rnn_layers=args.num_rnn_layers,
|
|
rnn_layer_size=args.rnn_layer_size,
|
|
use_gru=args.use_gru,
|
|
pretrained_model_path=args.init_model_path,
|
|
share_rnn_weights=args.share_rnn_weights)
|
|
ds2_model.train(
|
|
train_batch_reader=train_batch_reader,
|
|
dev_batch_reader=dev_batch_reader,
|
|
feeding_dict=train_generator.feeding,
|
|
learning_rate=args.learning_rate,
|
|
gradient_clipping=400,
|
|
num_passes=args.num_passes,
|
|
num_iterations_print=args.num_iter_print,
|
|
output_model_dir=args.output_model_dir,
|
|
is_local=args.is_local,
|
|
test_off=args.test_off)
|
|
|
|
|
|
def main():
|
|
print_arguments(args)
|
|
paddle.init(use_gpu=args.use_gpu,
|
|
rnn_use_batch=True,
|
|
trainer_count=args.trainer_count,
|
|
log_clipping=True)
|
|
train()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|