msb-public
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PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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Add infererence and add SortaGrad for only first pass.

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pull/2/head
Xinghai Sun 7 years ago
parent 3fc94427db
commit 70a343a499
7 changed files with 248 additions and 114 deletions
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2
README.md
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@ -5,3 +5,5 @@ sh requirements.sh
python librispeech.py
python train.py
```
Please add warp-ctc library path (usually $PADDLE_INSTALL_DIR/Paddle/third_party/install/warpctc/lib) to LD_LIBRARY_PATH.

4
audio_data_utils.py
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@ -90,6 +90,10 @@ def get_vocabulary_size():
return len(vocab_dict)
def get_vocabulary():
return vocabulary_from_file(ENGLISH_CHAR_VOCAB_FILEPATH)
def parse_transcript(text, vocabulary):
"""
Convert the transcript text string to list of token index integers..

94
infer.py
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@ -0,0 +1,94 @@
import paddle.v2 as paddle
import audio_data_utils
import argparse
from model import deep_speech2
import gzip
from itertools import groupby
parser = argparse.ArgumentParser(
description='Simpled version of DeepSpeech2 inference.')
parser.add_argument(
"--num_samples", default=10, type=int, help="Number of inference samples.")
parser.add_argument(
"--num_conv_layers", default=2, type=int, help="Convolution layer number.")
parser.add_argument(
"--num_rnn_layers", default=3, type=int, help="RNN layer number.")
parser.add_argument(
"--rnn_layer_size", default=512, type=int, help="RNN layer cell number.")
parser.add_argument(
"--use_gpu", default=True, type=bool, help="Use gpu or not.")
args = parser.parse_args()
def remove_duplicate_and_blank(id_list, blank_id):
# remove consecutive duplicate tokens
id_list = [x[0] for x in groupby(id_list)]
# remove blank
return [id for id in id_list if id != blank_id]
def max_infer():
# create network config
_, vocab_list = audio_data_utils.get_vocabulary()
dict_size = len(vocab_list)
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=1000,
type=paddle.data_type.dense_vector(161000))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
_, max_id = 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)
# load parameters
parameters = paddle.parameters.Parameters.from_tar(
gzip.open("params.tar.gz"))
# prepare infer data
feeding = {
"audio_spectrogram": 0,
"transcript_text": 1,
}
test_batch_reader = audio_data_utils.padding_batch_reader(
paddle.batch(
audio_data_utils.reader_creator(
manifest_path="./libri.manifest.test", sort_by_duration=False),
batch_size=args.num_samples),
padding=[-1, 1000])
infer_data = test_batch_reader().next()
# run inference
max_id_results = paddle.infer(
output_layer=max_id,
parameters=parameters,
input=infer_data,
field=['id'])
# postprocess
instance_length = len(max_id_results) / args.num_samples
instance_list = [
max_id_results[i:i + instance_length]
for i in xrange(0, args.num_samples)
]
for i, instance in enumerate(instance_list):
id_list = remove_duplicate_and_blank(instance, dict_size)
output_transcript = ''.join([vocab_list[id] for id in id_list])
target_transcript = ''.join([vocab_list[id] for id in infer_data[i][1]])
print("Target Transcript: %s \nOutput Transcript: %s \n" %
(target_transcript, output_transcript))
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
max_infer()
if __name__ == '__main__':
main()

2
librispeech.py
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@ -23,7 +23,7 @@ parser.add_argument(
"--manifest",
default="./libri.manifest",
type=str,
help="Filepath prefix of output manifests.")
help="Filepath prefix for output manifests.")
args = parser.parse_args()

106
model.py
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@ -0,0 +1,106 @@
import paddle.v2 as paddle
def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
padding, act):
conv_layer = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=num_channels_in,
num_filters=num_channels_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=conv_layer, act=act)
def bidirectonal_simple_rnn_bn_layer(name, input, size, act):
def __simple_rnn_step__(input):
last_state = paddle.layer.memory(name=name + "_state", size=size)
input_fc = paddle.layer.fc(
input=input,
size=size,
act=paddle.activation.Linear(),
bias_attr=False)
input_fc_bn = paddle.layer.batch_norm(
input=input_fc, act=paddle.activation.Linear())
state_fc = paddle.layer.fc(
input=last_state,
size=size,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.addto(
name=name + "_state", input=[input_fc_bn, state_fc], act=act)
forward = paddle.layer.recurrent_group(
step=__simple_rnn_step__, input=input)
return forward
# argument reverse is not exposed in V2 recurrent_group
#backward = paddle.layer.recurrent_group(
#step=__simple_rnn_step__,
#input=input,
#reverse=True)
#return paddle.layer.concat(input=[forward, backward])
def conv_group(input, num_stacks):
conv = conv_bn_layer(
input=input,
filter_size=(11, 41),
num_channels_in=1,
num_channels_out=32,
stride=(3, 2),
padding=(5, 20),
act=paddle.activation.BRelu())
for i in xrange(num_stacks - 1):
conv = conv_bn_layer(
input=conv,
filter_size=(11, 21),
num_channels_in=32,
num_channels_out=32,
stride=(1, 2),
padding=(5, 10),
act=paddle.activation.BRelu())
return conv
def rnn_group(input, size, num_stacks):
output = input
for i in xrange(num_stacks):
output = bidirectonal_simple_rnn_bn_layer(
name=str(i), input=output, size=size, act=paddle.activation.BRelu())
return output
def deep_speech2(audio_data,
text_data,
dict_size,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=256):
conv_group_output = conv_group(input=audio_data, num_stacks=num_conv_layers)
conv2seq = paddle.layer.block_expand(
input=conv_group_output,
num_channels=32,
stride_x=1,
stride_y=1,
block_x=1,
block_y=21)
rnn_group_output = rnn_group(
input=conv2seq, size=rnn_size, num_stacks=num_rnn_layers)
fc = paddle.layer.fc(
input=rnn_group_output,
size=dict_size + 1,
act=paddle.activation.Linear(),
bias_attr=True)
cost = paddle.layer.warp_ctc(
input=fc,
label=text_data,
size=dict_size + 1,
blank=dict_size,
norm_by_times=True)
max_id = paddle.layer.max_id(input=fc)
return cost, max_id

2
requirements.sh
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@ -1,5 +1,5 @@
pip install wget
pip install soundfile
# For Linux only
# For Ubuntu only
apt-get install libsndfile1

152
train.py
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@ -1,6 +1,8 @@
import paddle.v2 as paddle
import audio_data_utils
import argparse
from model import deep_speech2
import gzip
parser = argparse.ArgumentParser(
description='Simpled version of DeepSpeech2 trainer.')
@ -9,114 +11,19 @@ parser.add_argument(
parser.add_argument("--trainer", default=1, type=int, help="Trainer number.")
parser.add_argument(
"--num_passes", default=20, type=int, help="Training pass number.")
parser.add_argument(
"--num_conv_layers", default=2, type=int, help="Convolution layer number.")
parser.add_argument(
"--num_rnn_layers", default=3, type=int, help="RNN layer number.")
parser.add_argument(
"--rnn_layer_size", default=256, type=int, help="RNN layer cell number.")
parser.add_argument(
"--use_gpu", default=True, type=bool, help="Use gpu or not.")
parser.add_argument(
"--trainer_count", default=8, type=int, help="Trainer number.")
args = parser.parse_args()
def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
padding, act):
conv_layer = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=num_channels_in,
num_filters=num_channels_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=conv_layer, act=act)
def bidirectonal_simple_rnn_bn_layer(name, input, size, act):
def __simple_rnn_step__(input):
last_state = paddle.layer.memory(name=name + "_state", size=size)
input_fc = paddle.layer.fc(
input=input,
size=size,
act=paddle.activation.Linear(),
bias_attr=False)
input_fc_bn = paddle.layer.batch_norm(
input=input_fc, act=paddle.activation.Linear())
state_fc = paddle.layer.fc(
input=last_state,
size=size,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.addto(
name=name + "_state", input=[input_fc_bn, state_fc], act=act)
forward = paddle.layer.recurrent_group(
step=__simple_rnn_step__, input=input)
return forward
# argument reverse is not exposed in V2 recurrent_group
#backward = paddle.layer.recurrent_group(
#step=__simple_rnn_step__,
#input=input,
#reverse=True)
#return paddle.layer.concat(input=[forward, backward])
def conv_group(input):
conv1 = conv_bn_layer(
input=input,
filter_size=(11, 41),
num_channels_in=1,
num_channels_out=32,
stride=(3, 2),
padding=(5, 20),
act=paddle.activation.BRelu())
conv2 = conv_bn_layer(
input=conv1,
filter_size=(11, 21),
num_channels_in=32,
num_channels_out=32,
stride=(1, 2),
padding=(5, 10),
act=paddle.activation.BRelu())
conv3 = conv_bn_layer(
input=conv2,
filter_size=(11, 21),
num_channels_in=32,
num_channels_out=32,
stride=(1, 2),
padding=(5, 10),
act=paddle.activation.BRelu())
return conv3
def rnn_group(input, size, num_stacks):
output = input
for i in xrange(num_stacks):
output = bidirectonal_simple_rnn_bn_layer(
name=str(i), input=output, size=size, act=paddle.activation.BRelu())
return output
def deep_speech2(audio_data, text_data, dict_size):
conv_group_output = conv_group(input=audio_data)
conv2seq = paddle.layer.block_expand(
input=conv_group_output,
num_channels=32,
stride_x=1,
stride_y=1,
block_x=1,
block_y=21)
rnn_group_output = rnn_group(input=conv2seq, size=256, num_stacks=5)
fc = paddle.layer.fc(
input=rnn_group_output,
size=dict_size + 1,
act=paddle.activation.Linear(),
bias_attr=True)
cost = paddle.layer.warp_ctc(
input=fc,
label=text_data,
size=dict_size + 1,
blank=dict_size,
norm_by_times=True)
return cost
def train():
# create network config
dict_size = audio_data_utils.get_vocabulary_size()
@ -128,7 +35,13 @@ def train():
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
cost = deep_speech2(audio_data, text_data, 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)
# create parameters and optimizer
parameters = paddle.parameters.create(cost)
@ -138,21 +51,30 @@ def train():
regularization=paddle.optimizer.L2Regularization(rate=8e-4))
trainer = paddle.trainer.SGD(
cost=cost, parameters=parameters, update_equation=optimizer)
return
# create data readers
feeding = {
"audio_spectrogram": 0,
"transcript_text": 1,
}
train_batch_reader = audio_data_utils.padding_batch_reader(
train_batch_reader_with_sortagrad = audio_data_utils.padding_batch_reader(
paddle.batch(
audio_data_utils.reader_creator("./libri.manifest.dev"),
audio_data_utils.reader_creator(
manifest_path="./libri.manifest.dev", sort_by_duration=True),
batch_size=args.batch_size // args.trainer),
padding=[-1, 1000])
train_batch_reader_without_sortagrad = audio_data_utils.padding_batch_reader(
paddle.batch(
audio_data_utils.reader_creator(
manifest_path="./libri.manifest.dev",
sort_by_duration=False,
shuffle=True),
batch_size=args.batch_size // args.trainer),
padding=[-1, 1000])
test_batch_reader = audio_data_utils.padding_batch_reader(
paddle.batch(
audio_data_utils.reader_creator("./libri.manifest.test"),
audio_data_utils.reader_creator(
manifest_path="./libri.manifest.test", sort_by_duration=False),
batch_size=args.batch_size // args.trainer),
padding=[-1, 1000])
@ -174,13 +96,19 @@ def train():
# run train
trainer.train(
reader=train_batch_reader,
reader=train_batch_reader_with_sortagrad,
event_handler=event_handler,
num_passes=1,
feeding=feeding)
trainer.train(
reader=train_batch_reader_without_sortagrad,
event_handler=event_handler,
num_passes=10,
num_passes=self.num_passes - 1,
feeding=feeding)
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
train()

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