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.
308 lines
13 KiB
308 lines
13 KiB
"""Contains DeepSpeech2 model."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import sys
|
|
import os
|
|
import time
|
|
import logging
|
|
import gzip
|
|
from distutils.dir_util import mkpath
|
|
import paddle.v2 as paddle
|
|
from decoders.swig_wrapper import Scorer
|
|
from decoders.swig_wrapper import ctc_greedy_decoder
|
|
from decoders.swig_wrapper import ctc_beam_search_decoder_batch
|
|
from model_utils.network import deep_speech_v2_network
|
|
|
|
logging.basicConfig(
|
|
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s')
|
|
|
|
|
|
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
|
|
:param share_rnn_weights: Whether to share input-hidden weights between
|
|
forward and backward directional RNNs.Notice that
|
|
for GRU, weight sharing is not supported.
|
|
:type share_rnn_weights: bool
|
|
"""
|
|
|
|
def __init__(self, vocab_size, num_conv_layers, num_rnn_layers,
|
|
rnn_layer_size, use_gru, pretrained_model_path,
|
|
share_rnn_weights):
|
|
self._create_network(vocab_size, num_conv_layers, num_rnn_layers,
|
|
rnn_layer_size, use_gru, share_rnn_weights)
|
|
self._create_parameters(pretrained_model_path)
|
|
self._inferer = None
|
|
self._loss_inferer = None
|
|
self._ext_scorer = None
|
|
self.logger = logging.getLogger("")
|
|
self.logger.setLevel(level=logging.INFO)
|
|
|
|
def train(self,
|
|
train_batch_reader,
|
|
dev_batch_reader,
|
|
feeding_dict,
|
|
learning_rate,
|
|
gradient_clipping,
|
|
num_passes,
|
|
output_model_dir,
|
|
is_local=True,
|
|
num_iterations_print=100,
|
|
test_off=False):
|
|
"""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 is_local: Set to False if running with pserver with multi-nodes.
|
|
:type is_local: bool
|
|
:param output_model_dir: Directory for saving the model (every pass).
|
|
:type output_model_dir: basestring
|
|
:param test_off: Turn off testing.
|
|
:type test_off: bool
|
|
"""
|
|
# prepare model output directory
|
|
if not os.path.exists(output_model_dir):
|
|
mkpath(output_model_dir)
|
|
|
|
# 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,
|
|
is_local=is_local)
|
|
|
|
# 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:
|
|
trainer.save_parameter_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):
|
|
if test_off:
|
|
print("\n------- Time: %d sec, Pass: %d" %
|
|
(time.time() - start_time, event.pass_id))
|
|
else:
|
|
result = trainer.test(
|
|
reader=dev_batch_reader, feeding=feeding_dict)
|
|
print(
|
|
"\n------- Time: %d sec, Pass: %d, "
|
|
"ValidationCost: %s" %
|
|
(time.time() - start_time, event.pass_id, result.cost))
|
|
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:
|
|
trainer.save_parameter_to_tar(f)
|
|
|
|
# run train
|
|
trainer.train(
|
|
reader=train_batch_reader,
|
|
event_handler=event_handler,
|
|
num_passes=num_passes,
|
|
feeding=feeding_dict)
|
|
|
|
def infer_loss_batch(self, infer_data):
|
|
"""Model inference. Infer the ctc loss for a batch of speech
|
|
utterances.
|
|
|
|
: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
|
|
:return: List of ctc loss.
|
|
:rtype: List of float
|
|
"""
|
|
# define inferer
|
|
if self._loss_inferer == None:
|
|
self._loss_inferer = paddle.inference.Inference(
|
|
output_layer=self._loss, parameters=self._parameters)
|
|
# run inference
|
|
return self._loss_inferer.infer(input=infer_data)
|
|
|
|
def infer_batch(self, infer_data, decoding_method, beam_alpha, beam_beta,
|
|
beam_size, cutoff_prob, cutoff_top_n, vocab_list,
|
|
language_model_path, num_processes, feeding_dict):
|
|
"""Model inference. Infer the transcription for a batch of speech
|
|
utterances.
|
|
|
|
:param infer_data: List of utterances to infer, with each utterance
|
|
consisting of a tuple of audio features and
|
|
transcription text (empty string).
|
|
:type infer_data: list
|
|
:param decoding_method: Decoding method name, 'ctc_greedy' or
|
|
'ctc_beam_search'.
|
|
:param decoding_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 cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
|
|
characters with highest probs in vocabulary will be
|
|
used in beam search, default 40.
|
|
:type cutoff_top_n: int
|
|
: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
|
|
:param feeding_dict: Feeding is a map of field name and tuple index
|
|
of the data that reader returns.
|
|
:type feeding_dict: dict|list
|
|
: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, feeding=feeding_dict)
|
|
start_pos = [0] * (len(infer_data) + 1)
|
|
for i in xrange(len(infer_data)):
|
|
start_pos[i + 1] = start_pos[i] + infer_data[i][3][0]
|
|
probs_split = [
|
|
infer_results[start_pos[i]:start_pos[i + 1]]
|
|
for i in xrange(0, len(infer_data))
|
|
]
|
|
# run decoder
|
|
results = []
|
|
if decoding_method == "ctc_greedy":
|
|
# best path decode
|
|
for i, probs in enumerate(probs_split):
|
|
output_transcription = ctc_greedy_decoder(
|
|
probs_seq=probs, vocabulary=vocab_list)
|
|
results.append(output_transcription)
|
|
elif decoding_method == "ctc_beam_search":
|
|
# initialize external scorer
|
|
if self._ext_scorer == None:
|
|
self._loaded_lm_path = language_model_path
|
|
self.logger.info("begin to initialize the external scorer "
|
|
"for decoding")
|
|
self._ext_scorer = Scorer(beam_alpha, beam_beta,
|
|
language_model_path, vocab_list)
|
|
|
|
lm_char_based = self._ext_scorer.is_character_based()
|
|
lm_max_order = self._ext_scorer.get_max_order()
|
|
lm_dict_size = self._ext_scorer.get_dict_size()
|
|
self.logger.info("language model: "
|
|
"is_character_based = %d," % lm_char_based +
|
|
" max_order = %d," % lm_max_order +
|
|
" dict_size = %d" % lm_dict_size)
|
|
self.logger.info("end initializing scorer. Start decoding ...")
|
|
else:
|
|
self._ext_scorer.reset_params(beam_alpha, beam_beta)
|
|
assert self._loaded_lm_path == language_model_path
|
|
# beam search decode
|
|
num_processes = min(num_processes, len(probs_split))
|
|
beam_search_results = ctc_beam_search_decoder_batch(
|
|
probs_split=probs_split,
|
|
vocabulary=vocab_list,
|
|
beam_size=beam_size,
|
|
num_processes=num_processes,
|
|
ext_scoring_func=self._ext_scorer,
|
|
cutoff_prob=cutoff_prob,
|
|
cutoff_top_n=cutoff_top_n)
|
|
|
|
results = [result[0][1] for result in beam_search_results]
|
|
else:
|
|
raise ValueError("Decoding method [%s] is not supported." %
|
|
decoding_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, use_gru, share_rnn_weights):
|
|
"""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))
|
|
seq_offset_data = paddle.layer.data(
|
|
name='sequence_offset',
|
|
type=paddle.data_type.integer_value_sequence(1))
|
|
seq_len_data = paddle.layer.data(
|
|
name='sequence_length',
|
|
type=paddle.data_type.integer_value_sequence(1))
|
|
index_range_datas = []
|
|
for i in xrange(num_rnn_layers):
|
|
index_range_datas.append(
|
|
paddle.layer.data(
|
|
name='conv%d_index_range' % i,
|
|
type=paddle.data_type.dense_vector(6)))
|
|
|
|
self._log_probs, self._loss = deep_speech_v2_network(
|
|
audio_data=audio_data,
|
|
text_data=text_data,
|
|
seq_offset_data=seq_offset_data,
|
|
seq_len_data=seq_len_data,
|
|
index_range_datas=index_range_datas,
|
|
dict_size=vocab_size,
|
|
num_conv_layers=num_conv_layers,
|
|
num_rnn_layers=num_rnn_layers,
|
|
rnn_size=rnn_layer_size,
|
|
use_gru=use_gru,
|
|
share_rnn_weights=share_rnn_weights)
|