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PaddleSpeech/train.py

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"""
Trainer for a simplifed version of Baidu DeepSpeech2 model.
"""
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
import time
import sys
from model import deep_speech2
from audio_data_utils import DataGenerator
import numpy as np
#TODO: add WER metric
parser = argparse.ArgumentParser(
description='Simplified version of DeepSpeech2 trainer.')
parser.add_argument(
"--batch_size", default=32, type=int, help="Minibatch size.")
parser.add_argument(
"--num_passes",
default=20,
type=int,
help="Training pass number. (default: %(default)s)")
parser.add_argument(
"--num_conv_layers",
default=2,
type=int,
help="Convolution layer number. (default: %(default)s)")
parser.add_argument(
"--num_rnn_layers",
default=3,
type=int,
help="RNN layer number. (default: %(default)s)")
parser.add_argument(
"--rnn_layer_size",
default=512,
type=int,
help="RNN layer cell number. (default: %(default)s)")
parser.add_argument(
"--adam_learning_rate",
default=5e-4,
type=float,
help="Learning rate for ADAM Optimizer. (default: %(default)s)")
parser.add_argument(
"--use_gpu",
default=True,
type=distutils.util.strtobool,
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--use_sortagrad",
default=False,
type=distutils.util.strtobool,
help="Use sortagrad or not. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=4,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--normalizer_manifest_path",
default='data/manifest.libri.train-clean-100',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--train_manifest_path",
default='data/manifest.libri.train-clean-100',
type=str,
help="Manifest path for training. (default: %(default)s)")
parser.add_argument(
"--dev_manifest_path",
default='data/manifest.libri.dev-clean',
type=str,
help="Manifest path for validation. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='data/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
args = parser.parse_args()
def train():
"""
DeepSpeech2 training.
"""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
# create network config
dict_size = data_generator.vocabulary_size()
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
cost = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
is_inference=False)
# create parameters and optimizer
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Adam(
learning_rate=args.adam_learning_rate, gradient_clipping_threshold=400)
trainer = paddle.trainer.SGD(
cost=cost, parameters=parameters, update_equation=optimizer)
# prepare data reader
train_batch_reader_sortagrad = data_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=True,
shuffle=False)
train_batch_reader_nosortagrad = data_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=True)
test_batch_reader = data_generator.batch_reader_creator(
manifest_path=args.dev_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
feeding = data_generator.data_name_feeding()
# create event handler
def event_handler(event):
global start_time, cost_sum, cost_counter
if isinstance(event, paddle.event.EndIteration):
cost_sum += event.cost
cost_counter += 1
if event.batch_id % 50 == 0:
print "\nPass: %d, Batch: %d, TrainCost: %f" % (
event.pass_id, event.batch_id, cost_sum / cost_counter)
cost_sum, cost_counter = 0.0, 0
with gzip.open("params.tar.gz", 'w') as f:
parameters.to_tar(f)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.BeginPass):
start_time = time.time()
cost_sum, cost_counter = 0.0, 0
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=test_batch_reader, feeding=feeding)
print "\n------- Time: %d sec, Pass: %d, ValidationCost: %s" % (
time.time() - start_time, event.pass_id, result.cost)
# run train
# first pass with sortagrad
if args.use_sortagrad:
trainer.train(
reader=train_batch_reader_sortagrad,
event_handler=event_handler,
num_passes=1,
feeding=feeding)
args.num_passes -= 1
# other passes without sortagrad
trainer.train(
reader=train_batch_reader_nosortagrad,
event_handler=event_handler,
num_passes=args.num_passes,
feeding=feeding)
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
train()
if __name__ == '__main__':
main()