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DeepSpeech

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提交 a3807d9c 编写于 作者: X Xinghai Sun 提交者： GitHub
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Merge pull request #183 from xinghai-sun/refine_decoder2 

Simplify train.py, evaluate.py, infer.py and tune.py by adding DeepSpeech2Model class for DS2.
上级 175457bf 526e18b1
隐藏空白更改
内联 并排
Showing with 533 addition and 449 deletion
decoder.py
浏览文件 @ a3807d9c
......@@ -205,9 +205,9 @@ def ctc_beam_search_decoder_batch(probs_split,
:type num_processes: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param num_processes: Number of parallel processes.
:type num_processes: int
:type cutoff_prob: float
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
......
evaluate.py
浏览文件 @ a3807d9c
......@@ -5,20 +5,24 @@ from __future__ import print_function
import distutils.util
import argparse
import gzip
import multiprocessing
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from lm.lm_scorer import LmScorer
from model import DeepSpeech2Model
from error_rate import wer
import utils
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--batch_size",
default=100,
default=128,
type=int,
help="Minibatch size for evaluation. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--num_conv_layers",
default=2,
......@@ -58,8 +62,8 @@ parser.add_argument(
"--decode_method",
default='beam_search',
type=str,
help="Method for ctc decoding, best_path or beam_search. (default: %(default)s)"
)
help="Method for ctc decoding, best_path or beam_search. "
"(default: %(default)s)")
parser.add_argument(
"--language_model_path",
default="lm/data/common_crawl_00.prune01111.trie.klm",
......@@ -67,12 +71,12 @@ parser.add_argument(
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.26,
default=0.36,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.1,
default=0.25,
type=float,
help="Parameter associated with word count. (default: %(default)f)")
parser.add_argument(
......@@ -112,37 +116,12 @@ args = parser.parse_args()
def evaluate():
"""Evaluate on whole test data for DeepSpeech2."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
# create network config
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
is_inference=True)
# load parameters
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(args.model_filepath))
# prepare infer data
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.batch_size,
......@@ -150,61 +129,39 @@ def evaluate():
sortagrad=False,
shuffle_method=None)
# define inferer
inferer = paddle.inference.Inference(
output_layer=output_probs, parameters=parameters)
# initialize external scorer for beam search decoding
if args.decode_method == 'beam_search':
ext_scorer = LmScorer(args.alpha, args.beta, args.language_model_path)
ds2_model = DeepSpeech2Model(
vocab_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
pretrained_model_path=args.model_filepath)
wer_counter, wer_sum = 0, 0.0
wer_sum, num_ins = 0.0, 0
for infer_data in batch_reader():
# run inference
infer_results = inferer.infer(input=infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
]
# target transcription
target_transcription = [
''.join([
data_generator.vocab_list[index] for index in infer_data[i][1]
]) for i, probs in enumerate(probs_split)
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decode_method=args.decode_method,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in infer_data
]
# decode and print
# best path decode
if args.decode_method == "best_path":
for i, probs in enumerate(probs_split):
output_transcription = ctc_best_path_decoder(
probs_seq=probs, vocabulary=data_generator.vocab_list)
wer_sum += wer(target_transcription[i], output_transcription)
wer_counter += 1
# beam search decode
elif args.decode_method == "beam_search":
# beam search using multiple processes
beam_search_results = ctc_beam_search_decoder_batch(
probs_split=probs_split,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(data_generator.vocab_list),
num_processes=args.num_processes_beam_search,
ext_scoring_func=ext_scorer,
cutoff_prob=args.cutoff_prob, )
for i, beam_search_result in enumerate(beam_search_results):
wer_sum += wer(target_transcription[i],
beam_search_result[0][1])
wer_counter += 1
else:
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
print("Final WER = %f" % (wer_sum / wer_counter))
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
num_ins += 1
print("WER (%d/?) = %f" % (num_ins, wer_sum / num_ins))
print("Final WER (%d/%d) = %f" % (num_ins, num_ins, wer_sum / num_ins))
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
evaluate()
......
infer.py
浏览文件 @ a3807d9c
......@@ -4,14 +4,11 @@ from __future__ import division
from __future__ import print_function
import argparse
import gzip
import distutils.util
import multiprocessing
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from lm.lm_scorer import LmScorer
from model import DeepSpeech2Model
from error_rate import wer
import utils
......@@ -43,7 +40,7 @@ parser.add_argument(
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--num_threads_data",
default=multiprocessing.cpu_count(),
default=1,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
......@@ -57,6 +54,11 @@ parser.add_argument(
type=str,
help="Feature type of audio data: 'linear' (power spectrum)"
" or 'mfcc'. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--mean_std_filepath",
default='mean_std.npz',
......@@ -119,37 +121,12 @@ args = parser.parse_args()
def infer():
"""Inference for DeepSpeech2."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
# create network config
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
is_inference=True)
# load parameters
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(args.model_filepath))
# prepare infer data
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
......@@ -158,66 +135,36 @@ def infer():
shuffle_method=None)
infer_data = batch_reader().next()
# run inference
infer_results = paddle.infer(
output_layer=output_probs, parameters=parameters, input=infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(len(infer_data))
]
ds2_model = DeepSpeech2Model(
vocab_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
pretrained_model_path=args.model_filepath)
result_transcripts = ds2_model.infer_batch(
infer_data=infer_data,
decode_method=args.decode_method,
beam_alpha=args.alpha,
beam_beta=args.beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
# targe transcription
target_transcription = [
''.join(
[data_generator.vocab_list[index] for index in infer_data[i][1]])
for i, probs in enumerate(probs_split)
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in infer_data
]
## decode and print
# best path decode
wer_sum, wer_counter = 0, 0
if args.decode_method == "best_path":
for i, probs in enumerate(probs_split):
best_path_transcription = ctc_best_path_decoder(
probs_seq=probs, vocabulary=data_generator.vocab_list)
print("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target_transcription[i], best_path_transcription))
wer_cur = wer(target_transcription[i], best_path_transcription)
wer_sum += wer_cur
wer_counter += 1
print("cur wer = %f, average wer = %f" %
(wer_cur, wer_sum / wer_counter))
# beam search decode
elif args.decode_method == "beam_search":
ext_scorer = LmScorer(args.alpha, args.beta, args.language_model_path)
beam_search_batch_results = ctc_beam_search_decoder_batch(
probs_split=probs_split,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(data_generator.vocab_list),
num_processes=args.num_processes_beam_search,
cutoff_prob=args.cutoff_prob,
ext_scoring_func=ext_scorer, )
for i, beam_search_result in enumerate(beam_search_batch_results):
print("\nTarget Transcription:\t%s" % target_transcription[i])
for index in xrange(args.num_results_per_sample):
result = beam_search_result[index]
#output: index, log prob, beam result
print("Beam %d: %f \t%s" % (index, result[0], result[1]))
wer_cur = wer(target_transcription[i], beam_search_result[0][1])
wer_sum += wer_cur
wer_counter += 1
print("cur wer = %f , average wer = %f" %
(wer_cur, wer_sum / wer_counter))
else:
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
for target, result in zip(target_transcripts, result_transcripts):
print("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target, result))
print("Current wer = %f" % wer(target, result))
def main():
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
infer()
......
layer.py 0 → 100644
浏览文件 @ a3807d9c
"""Contains DeepSpeech2 layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
padding, act):
"""Convolution layer with batch normalization.
:param input: Input layer.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a tuple for
two image dimension.
:type filter_size: int|tuple|list
:param num_channels_in: Number of input channels.
:type num_channels_in: int
:type num_channels_out: Number of output channels.
:type num_channels_in: out
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension.
:type padding: int|tuple|list
:param act: Activation type.
:type act: BaseActivation
:return: Batch norm layer after convolution layer.
:rtype: LayerOutput
"""
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 bidirectional_simple_rnn_bn_layer(name, input, size, act):
"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
:param name: Name of the layer.
:type name: string
:param input: Input layer.
:type input: LayerOutput
:param size: Number of RNN cells.
:type size: int
:param act: Activation type.
:type act: BaseActivation
:return: Bidirectional simple rnn layer.
:rtype: LayerOutput
"""
# input-hidden weights shared across bi-direcitonal rnn.
input_proj = paddle.layer.fc(
input=input, size=size, act=paddle.activation.Linear(), bias_attr=False)
# batch norm is only performed on input-state projection
input_proj_bn = paddle.layer.batch_norm(
input=input_proj, act=paddle.activation.Linear())
# forward and backward in time
forward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=False)
backward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=True)
return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn])
def conv_group(input, num_stacks):
"""Convolution group with stacked convolution layers.
:param input: Input layer.
:type input: LayerOutput
:param num_stacks: Number of stacked convolution layers.
:type num_stacks: int
:return: Output layer of the convolution group.
:rtype: LayerOutput
"""
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())
output_num_channels = 32
output_height = 160 // pow(2, num_stacks) + 1
return conv, output_num_channels, output_height
def rnn_group(input, size, num_stacks):
"""RNN group with stacked bidirectional simple RNN layers.
:param input: Input layer.
:type input: LayerOutput
:param size: Number of RNN cells in each layer.
:type size: int
:param num_stacks: Number of stacked rnn layers.
:type num_stacks: int
:return: Output layer of the RNN group.
:rtype: LayerOutput
"""
output = input
for i in xrange(num_stacks):
output = bidirectional_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):
"""
The whole DeepSpeech2 model structure (a simplified version).
:param audio_data: Audio spectrogram data layer.
:type audio_data: LayerOutput
:param text_data: Transcription text data layer.
:type text_data: LayerOutput
:param dict_size: Dictionary size for tokenized transcription.
:type dict_size: int
:param num_conv_layers: Number of stacking convolution layers.
:type num_conv_layers: int
:param num_rnn_layers: Number of stacking RNN layers.
:type num_rnn_layers: int
:param rnn_size: RNN layer size (number of RNN cells).
:type rnn_size: int
:return: A tuple of an output unnormalized log probability layer (
before softmax) and a ctc cost layer.
:rtype: tuple of LayerOutput
"""
# convolution group
conv_group_output, conv_group_num_channels, conv_group_height = conv_group(
input=audio_data, num_stacks=num_conv_layers)
# convert data form convolution feature map to sequence of vectors
conv2seq = paddle.layer.block_expand(
input=conv_group_output,
num_channels=conv_group_num_channels,
stride_x=1,
stride_y=1,
block_x=1,
block_y=conv_group_height)
# rnn group
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)
# probability distribution with softmax
log_probs = paddle.layer.mixed(
input=paddle.layer.identity_projection(input=fc),
act=paddle.activation.Softmax())
# ctc cost
ctc_loss = paddle.layer.warp_ctc(
input=fc,
label=text_data,
size=dict_size + 1,
blank=dict_size,
norm_by_times=True)
return log_probs, ctc_loss
model.py
浏览文件 @ a3807d9c
......@@ -3,141 +3,220 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import time
import gzip
from decoder import *
from lm.lm_scorer import LmScorer
import paddle.v2 as paddle
from layer import *
def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
padding, act):
"""
Convolution layer with batch normalization.
class DeepSpeech2Model(object):
"""DeepSpeech2Model class.
:param vocab_size: Decoding vocabulary size.
:type vocab_size: int
:param num_conv_layers: Number of stacking convolution layers.
:type num_conv_layers: int
:param num_rnn_layers: Number of stacking RNN layers.
:type num_rnn_layers: int
:param rnn_layer_size: RNN layer size (number of RNN cells).
:type rnn_layer_size: int
:param pretrained_model_path: Pretrained model path. If None, will train
from stratch.
:type pretrained_model_path: basestring|None
"""
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 __init__(self, vocab_size, num_conv_layers, num_rnn_layers,
rnn_layer_size, pretrained_model_path):
self._create_network(vocab_size, num_conv_layers, num_rnn_layers,
rnn_layer_size)
self._create_parameters(pretrained_model_path)
self._inferer = None
self._ext_scorer = None
def bidirectional_simple_rnn_bn_layer(name, input, size, act):
"""
Bidirectonal simple rnn layer with sequence-wise batch normalization.
The batch normalization is only performed on input-state weights.
"""
# input-hidden weights shared across bi-direcitonal rnn.
input_proj = paddle.layer.fc(
input=input, size=size, act=paddle.activation.Linear(), bias_attr=False)
# batch norm is only performed on input-state projection
input_proj_bn = paddle.layer.batch_norm(
input=input_proj, act=paddle.activation.Linear())
# forward and backward in time
forward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=False)
backward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn, act=act, reverse=True)
return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn])
def train(self,
train_batch_reader,
dev_batch_reader,
feeding_dict,
learning_rate,
gradient_clipping,
num_passes,
output_model_dir,
num_iterations_print=100):
"""Train the model.
:param train_batch_reader: Train data reader.
:type train_batch_reader: callable
:param dev_batch_reader: Validation data reader.
:type dev_batch_reader: callable
:param feeding_dict: Feeding is a map of field name and tuple index
of the data that reader returns.
:type feeding_dict: dict|list
:param learning_rate: Learning rate for ADAM optimizer.
:type learning_rate: float
:param gradient_clipping: Gradient clipping threshold.
:type gradient_clipping: float
:param num_passes: Number of training epochs.
:type num_passes: int
:param num_iterations_print: Number of training iterations for printing
a training loss.
:type rnn_iteratons_print: int
:param output_model_dir: Directory for saving the model (every pass).
:type output_model_dir: basestring
"""
# prepare model output directory
if not os.path.exists(output_model_dir):
os.mkdir(output_model_dir)
def conv_group(input, num_stacks):
"""
Convolution group with several stacking convolution layers.
"""
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())
output_num_channels = 32
output_height = 160 // pow(2, num_stacks) + 1
return conv, output_num_channels, output_height
# prepare optimizer and trainer
optimizer = paddle.optimizer.Adam(
learning_rate=learning_rate,
gradient_clipping_threshold=gradient_clipping)
trainer = paddle.trainer.SGD(
cost=self._loss,
parameters=self._parameters,
update_equation=optimizer)
# 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 + 1) % num_iterations_print == 0:
output_model_path = os.path.join(output_model_dir,
"params.latest.tar.gz")
with gzip.open(output_model_path, 'w') as f:
self._parameters.to_tar(f)
print("\nPass: %d, Batch: %d, TrainCost: %f" %
(event.pass_id, event.batch_id + 1,
cost_sum / cost_counter))
cost_sum, cost_counter = 0.0, 0
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=dev_batch_reader, feeding=feeding_dict)
output_model_path = os.path.join(
output_model_dir, "params.pass-%d.tar.gz" % event.pass_id)
with gzip.open(output_model_path, 'w') as f:
self._parameters.to_tar(f)
print("\n------- Time: %d sec, Pass: %d, ValidationCost: %s" %
(time.time() - start_time, event.pass_id, result.cost))
def rnn_group(input, size, num_stacks):
"""
RNN group with several stacking RNN layers.
"""
output = input
for i in xrange(num_stacks):
output = bidirectional_simple_rnn_bn_layer(
name=str(i), input=output, size=size, act=paddle.activation.BRelu())
return output
# run train
trainer.train(
reader=train_batch_reader,
event_handler=event_handler,
num_passes=num_passes,
feeding=feeding_dict)
def infer_batch(self, infer_data, decode_method, beam_alpha, beam_beta,
beam_size, cutoff_prob, vocab_list, language_model_path,
num_processes):
"""Model inference. Infer the transcription for a batch of speech
utterances.
def deep_speech2(audio_data,
text_data,
dict_size,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=256,
is_inference=False):
"""
The whole DeepSpeech2 model structure (a simplified version).
:param infer_data: List of utterances to infer, with each utterance a
tuple of audio features and transcription text (empty
string).
:type infer_data: list
:param decode_method: Decoding method name, 'best_path' or
'beam search'.
:param decode_method: string
:param beam_alpha: Parameter associated with language model.
:type beam_alpha: float
:param beam_beta: Parameter associated with word count.
:type beam_beta: float
:param beam_size: Width for Beam search.
:type beam_size: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param vocab_list: List of tokens in the vocabulary, for decoding.
:type vocab_list: list
:param language_model_path: Filepath for language model.
:type language_model_path: basestring|None
:param num_processes: Number of processes (CPU) for decoder.
:type num_processes: int
:return: List of transcription texts.
:rtype: List of basestring
"""
# define inferer
if self._inferer == None:
self._inferer = paddle.inference.Inference(
output_layer=self._log_probs, parameters=self._parameters)
# run inference
infer_results = self._inferer.infer(input=infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
]
# run decoder
results = []
if decode_method == "best_path":
# best path decode
for i, probs in enumerate(probs_split):
output_transcription = ctc_best_path_decoder(
probs_seq=probs, vocabulary=data_generator.vocab_list)
results.append(output_transcription)
elif decode_method == "beam_search":
# initialize external scorer
if self._ext_scorer == None:
self._ext_scorer = LmScorer(beam_alpha, beam_beta,
language_model_path)
self._loaded_lm_path = language_model_path
else:
self._ext_scorer.reset_params(beam_alpha, beam_beta)
assert self._loaded_lm_path == language_model_path
:param audio_data: Audio spectrogram data layer.
:type audio_data: LayerOutput
:param text_data: Transcription text data layer.
:type text_data: LayerOutput
:param dict_size: Dictionary size for tokenized transcription.
:type dict_size: int
:param num_conv_layers: Number of stacking convolution layers.
:type num_conv_layers: int
:param num_rnn_layers: Number of stacking RNN layers.
:type num_rnn_layers: int
:param rnn_size: RNN layer size (number of RNN cells).
:type rnn_size: int
:param is_inference: False in the training mode, and True in the
inferene mode.
:type is_inference: bool
:return: If is_inference set False, return a ctc cost layer;
if is_inference set True, return a sequence layer of output
probability distribution.
:rtype: tuple of LayerOutput
"""
# convolution group
conv_group_output, conv_group_num_channels, conv_group_height = conv_group(
input=audio_data, num_stacks=num_conv_layers)
# convert data form convolution feature map to sequence of vectors
conv2seq = paddle.layer.block_expand(
input=conv_group_output,
num_channels=conv_group_num_channels,
stride_x=1,
stride_y=1,
block_x=1,
block_y=conv_group_height)
# rnn group
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)
if is_inference:
# probability distribution with softmax
return paddle.layer.mixed(
input=paddle.layer.identity_projection(input=fc),
act=paddle.activation.Softmax())
else:
# ctc cost
return paddle.layer.warp_ctc(
input=fc,
label=text_data,
size=dict_size + 1,
blank=dict_size,
norm_by_times=True)
# beam search decode
beam_search_results = ctc_beam_search_decoder_batch(
probs_split=probs_split,
vocabulary=vocab_list,
beam_size=beam_size,
blank_id=len(vocab_list),
num_processes=num_processes,
ext_scoring_func=self._ext_scorer,
cutoff_prob=cutoff_prob)
results = [result[0][1] for result in beam_search_results]
else:
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
return results
def _create_parameters(self, model_path=None):
"""Load or create model parameters."""
if model_path is None:
self._parameters = paddle.parameters.create(self._loss)
else:
self._parameters = paddle.parameters.Parameters.from_tar(
gzip.open(model_path))
def _create_network(self, vocab_size, num_conv_layers, num_rnn_layers,
rnn_layer_size):
"""Create data layers and model network."""
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram",
type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(vocab_size))
self._log_probs, self._loss = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=vocab_size,
num_conv_layers=num_conv_layers,
num_rnn_layers=num_rnn_layers,
rnn_size=rnn_layer_size)
setup.sh
浏览文件 @ a3807d9c
......@@ -26,7 +26,4 @@ if [ $? != 0 ]; then
rm libsndfile-1.0.28.tar.gz
fi
# prepare ./checkpoints
mkdir checkpoints
echo "Install all dependencies successfully."
train.py
浏览文件 @ a3807d9c
......@@ -3,15 +3,11 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import argparse
import gzip
import time
import distutils.util
import multiprocessing
import paddle.v2 as paddle
from model import deep_speech2
from model import DeepSpeech2Model
from data_utils.data import DataGenerator
import utils
......@@ -23,6 +19,12 @@ parser.add_argument(
default=200,
type=int,
help="Training pass number. (default: %(default)s)")
parser.add_argument(
"--num_iterations_print",
default=100,
type=int,
help="Number of iterations for every train cost printing. "
"(default: %(default)s)")
parser.add_argument(
"--num_conv_layers",
default=2,
......@@ -114,6 +116,11 @@ parser.add_argument(
help="If set None, the training will start from scratch. "
"Otherwise, the training will resume from "
"the existing model of this path. (default: %(default)s)")
parser.add_argument(
"--output_model_dir",
default="./checkpoints",
type=str,
help="Directory for saving models. (default: %(default)s)")
parser.add_argument(
"--augmentation_config",
default='[{"type": "shift", '
......@@ -127,100 +134,48 @@ args = parser.parse_args()
def train():
"""DeepSpeech2 training."""
# initialize data generator
def data_generator():
return DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config=args.augmentation_config,
max_duration=args.max_duration,
min_duration=args.min_duration,
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
train_generator = data_generator()
test_generator = data_generator()
# create network config
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(
train_generator.vocab_size))
cost = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=train_generator.vocab_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/load parameters and optimizer
if args.init_model_path is None:
parameters = paddle.parameters.create(cost)
else:
if not os.path.isfile(args.init_model_path):
raise IOError("Invalid model!")
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(args.init_model_path))
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_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config=args.augmentation_config,
max_duration=args.max_duration,
min_duration=args.min_duration,
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
dev_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config="{}",
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
train_batch_reader = train_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
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)
test_batch_reader = test_generator.batch_reader_creator(
dev_batch_reader = dev_generator.batch_reader_creator(
manifest_path=args.dev_manifest_path,
batch_size=args.batch_size,
min_batch_size=1, # must be 1, but will have errors.
sortagrad=False,
shuffle_method=None)
# 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 + 1) % 100 == 0:
print("\nPass: %d, Batch: %d, TrainCost: %f" % (
event.pass_id, event.batch_id + 1, cost_sum / cost_counter))
cost_sum, cost_counter = 0.0, 0
with gzip.open("checkpoints/params.latest.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=test_generator.feeding)
print("\n------- Time: %d sec, Pass: %d, ValidationCost: %s" %
(time.time() - start_time, event.pass_id, result.cost))
with gzip.open("checkpoints/params.pass-%d.tar.gz" % event.pass_id,
'w') as f:
parameters.to_tar(f)
# run train
trainer.train(
reader=train_batch_reader,
event_handler=event_handler,
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,
pretrained_model_path=args.init_model_path)
ds2_model.train(
train_batch_reader=train_batch_reader,
dev_batch_reader=dev_batch_reader,
feeding_dict=train_generator.feeding,
learning_rate=args.adam_learning_rate,
gradient_clipping=400,
num_passes=args.num_passes,
feeding=train_generator.feeding)
num_iterations_print=args.num_iterations_print,
output_model_dir=args.output_model_dir)
def main():
......
tune.py
浏览文件 @ a3807d9c
......@@ -3,14 +3,13 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import distutils.util
import argparse
import gzip
import multiprocessing
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from lm.lm_scorer import LmScorer
from model import DeepSpeech2Model
from error_rate import wer
import utils
......@@ -40,9 +39,14 @@ parser.add_argument(
default=True,
type=distutils.util.strtobool,
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=8,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--num_threads_data",
default=multiprocessing.cpu_count(),
default=1,
type=int,
help="Number of cpu threads for preprocessing data. (default: %(default)s)")
parser.add_argument(
......@@ -62,10 +66,10 @@ parser.add_argument(
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--decode_manifest_path",
default='datasets/manifest.test',
"--tune_manifest_path",
default='datasets/manifest.dev',
type=str,
help="Manifest path for decoding. (default: %(default)s)")
help="Manifest path for tuning. (default: %(default)s)")
parser.add_argument(
"--model_filepath",
default='checkpoints/params.latest.tar.gz',
......@@ -127,96 +131,64 @@ args = parser.parse_args()
def tune():
"""Tune parameters alpha and beta on one minibatch."""
if not args.num_alphas >= 0:
raise ValueError("num_alphas must be non-negative!")
if not args.num_betas >= 0:
raise ValueError("num_betas must be non-negative!")
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}',
specgram_type=args.specgram_type,
num_threads=args.num_threads_data)
# create network config
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
is_inference=True)
# load parameters
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(args.model_filepath))
# prepare infer data
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
manifest_path=args.tune_manifest_path,
batch_size=args.num_samples,
sortagrad=False,
shuffle_method=None)
# get one batch data for tuning
infer_data = batch_reader().next()
# run inference
infer_results = paddle.infer(
output_layer=output_probs, parameters=parameters, input=infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
tune_data = batch_reader().next()
target_transcripts = [
''.join([data_generator.vocab_list[token] for token in transcript])
for _, transcript in tune_data
]
ds2_model = DeepSpeech2Model(
vocab_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_layer_size=args.rnn_layer_size,
pretrained_model_path=args.model_filepath)
# create grid for search
cand_alphas = np.linspace(args.alpha_from, args.alpha_to, args.num_alphas)
cand_betas = np.linspace(args.beta_from, args.beta_to, args.num_betas)
params_grid = [(alpha, beta) for alpha in cand_alphas
for beta in cand_betas]
ext_scorer = LmScorer(args.alpha_from, args.beta_from,
args.language_model_path)
## tune parameters in loop
for alpha, beta in params_grid:
wer_sum, wer_counter = 0, 0
# reset scorer
ext_scorer.reset_params(alpha, beta)
# beam search using multiple processes
beam_search_results = ctc_beam_search_decoder_batch(
probs_split=probs_split,
vocabulary=data_generator.vocab_list,
result_transcripts = ds2_model.infer_batch(
infer_data=tune_data,
decode_method='beam_search',
beam_alpha=alpha,
beam_beta=beta,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
blank_id=len(data_generator.vocab_list),
num_processes=args.num_processes_beam_search,
ext_scoring_func=ext_scorer, )
for i, beam_search_result in enumerate(beam_search_results):
target_transcription = ''.join([
data_generator.vocab_list[index] for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, beam_search_result[0][1])
wer_counter += 1
vocab_list=data_generator.vocab_list,
language_model_path=args.language_model_path,
num_processes=args.num_processes_beam_search)
wer_sum, num_ins = 0.0, 0
for target, result in zip(target_transcripts, result_transcripts):
wer_sum += wer(target, result)
num_ins += 1
print("alpha = %f\tbeta = %f\tWER = %f" %
(alpha, beta, wer_sum / wer_counter))
(alpha, beta, wer_sum / num_ins))
def main():
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
tune()
......
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