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PaddleSpeech/model_utils/model.py

566 lines
22 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
import copy
import inspect
import cPickle as pickle
import collections
import multiprocessing
import numpy as np
from distutils.dir_util import mkpath
import paddle.fluid as fluid
import paddle.fluid.compiler as compiler
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 use_gru: Use gru if set True. Use simple rnn if set False.
:type use_gru: bool
: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
:param place: Program running place.
:type place: CPUPlace or CUDAPlace
:param init_from_pretrained_model: Pretrained model path. If None, will train
from stratch.
:type init_from_pretrained_model: string|None
:param output_model_dir: Output model directory. If None, output to current directory.
:type output_model_dir: string|None
"""
def __init__(self,
vocab_size,
num_conv_layers,
num_rnn_layers,
rnn_layer_size,
use_gru=False,
share_rnn_weights=True,
place=fluid.CPUPlace(),
init_from_pretrained_model=None,
output_model_dir=None):
self._vocab_size = vocab_size
self._num_conv_layers = num_conv_layers
self._num_rnn_layers = num_rnn_layers
self._rnn_layer_size = rnn_layer_size
self._use_gru = use_gru
self._share_rnn_weights = share_rnn_weights
self._place = place
self._init_from_pretrained_model = init_from_pretrained_model
self._output_model_dir = output_model_dir
self._ext_scorer = None
self.logger = logging.getLogger("")
self.logger.setLevel(level=logging.INFO)
def create_network(self, is_infer=False):
"""Create data layers and model network.
:param is_training: Whether to create a network for training.
:type is_training: bool
:return reader: Reader for input.
:rtype reader: read generater
:return log_probs: An output unnormalized log probability layer.
:rtype lig_probs: Varable
:return loss: A ctc loss layer.
:rtype loss: Variable
"""
if not is_infer:
input_fields = {
'names': ['audio_data', 'text_data', 'seq_len_data', 'masks'],
'shapes':
[[None, 161, None], [None, 1], [None, 1], [None, 32, 81, None]],
'dtypes': ['float32', 'int32', 'int64', 'float32'],
'lod_levels': [0, 1, 0, 0]
}
inputs = [
fluid.data(
name=input_fields['names'][i],
shape=input_fields['shapes'][i],
dtype=input_fields['dtypes'][i],
lod_level=input_fields['lod_levels'][i])
for i in range(len(input_fields['names']))
]
reader = fluid.io.DataLoader.from_generator(
feed_list=inputs,
capacity=64,
iterable=False,
use_double_buffer=True)
(audio_data, text_data, seq_len_data, masks) = inputs
else:
audio_data = fluid.data(
name='audio_data',
shape=[None, 161, None],
dtype='float32',
lod_level=0)
seq_len_data = fluid.data(
name='seq_len_data',
shape=[None, 1],
dtype='int64',
lod_level=0)
masks = fluid.data(
name='masks',
shape=[None, 32, 81, None],
dtype='float32',
lod_level=0)
text_data = None
reader = fluid.DataFeeder([audio_data, seq_len_data, masks],
self._place)
log_probs, loss = deep_speech_v2_network(
audio_data=audio_data,
text_data=text_data,
seq_len_data=seq_len_data,
masks=masks,
dict_size=self._vocab_size,
num_conv_layers=self._num_conv_layers,
num_rnn_layers=self._num_rnn_layers,
rnn_size=self._rnn_layer_size,
use_gru=self._use_gru,
share_rnn_weights=self._share_rnn_weights)
return reader, log_probs, loss
def init_from_pretrained_model(self, exe, program):
'''Init params from pretrain model. '''
assert isinstance(self._init_from_pretrained_model, str)
if not os.path.exists(self._init_from_pretrained_model):
print(self._init_from_pretrained_model)
raise Warning("The pretrained params do not exist.")
return False
fluid.io.load_params(
exe,
self._init_from_pretrained_model,
main_program=program,
filename="params.pdparams")
print("finish initing model from pretrained params from %s" %
(self._init_from_pretrained_model))
pre_epoch = 0
dir_name = self._init_from_pretrained_model.split('_')
if len(dir_name) >= 2 and dir_name[-2].endswith('epoch') and dir_name[
-1].isdigit():
pre_epoch = int(dir_name[-1])
return pre_epoch + 1
def save_param(self, exe, program, dirname):
'''Save model params to dirname'''
assert isinstance(self._output_model_dir, str)
param_dir = os.path.join(self._output_model_dir)
if not os.path.exists(param_dir):
os.mkdir(param_dir)
fluid.io.save_params(
exe,
os.path.join(param_dir, dirname),
main_program=program,
filename="params.pdparams")
print("save parameters at %s" % (os.path.join(param_dir, dirname)))
return True
def test(self, exe, dev_batch_reader, test_program, test_reader,
fetch_list):
'''Test the model.
:param exe:The executor of program.
:type exe: Executor
:param dev_batch_reader: The reader of test dataa.
:type dev_batch_reader: read generator
:param test_program: The program of test.
:type test_program: Program
:param test_reader: Reader of test.
:type test_reader: Reader
:param fetch_list: Fetch list.
:type fetch_list: list
:return: An output unnormalized log probability.
:rtype: array
'''
test_reader.start()
epoch_loss = []
while True:
try:
each_loss = exe.run(
program=test_program,
fetch_list=fetch_list,
return_numpy=False)
epoch_loss.extend(np.array(each_loss[0]))
except fluid.core.EOFException:
test_reader.reset()
break
return np.mean(np.array(epoch_loss))
def train(self,
train_batch_reader,
dev_batch_reader,
feeding_dict,
learning_rate,
gradient_clipping,
num_epoch,
batch_size,
num_samples,
save_epoch=100,
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_epoch: Number of training epochs.
:type num_epoch: int
:param batch_size: Number of batch size.
:type batch_size: int
:param num_samples: The num of train samples.
:type num_samples: int
:param save_epoch: Number of training iterations for save checkpoint and params.
:type save_epoch: int
:param num_iterations_print: Number of training iterations for printing
a training loss.
:type num_iteratons_print: int
:param test_off: Turn off testing.
:type test_off: bool
"""
# prepare model output directory
if not os.path.exists(self._output_model_dir):
mkpath(self._output_model_dir)
# adapt the feeding dict according to the network
adapted_feeding_dict = self._adapt_feeding_dict(feeding_dict)
if isinstance(self._place, fluid.CUDAPlace):
dev_count = fluid.core.get_cuda_device_count()
else:
dev_count = int(os.environ.get('CPU_NUM', 1))
# prepare the network
train_program = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_reader, log_probs, ctc_loss = self.create_network()
# prepare optimizer
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=fluid.layers.exponential_decay(
learning_rate=learning_rate,
decay_steps=num_samples / batch_size / dev_count,
decay_rate=0.83,
staircase=True))
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(
clip_norm=gradient_clipping))
optimizer.minimize(loss=ctc_loss)
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_reader, _, ctc_loss = self.create_network()
test_prog = test_prog.clone(for_test=True)
exe = fluid.Executor(self._place)
exe.run(startup_prog)
# init from some pretrain models, to better solve the current task
pre_epoch = 0
if self._init_from_pretrained_model:
pre_epoch = self.init_from_pretrained_model(exe, train_program)
build_strategy = compiler.BuildStrategy()
exec_strategy = fluid.ExecutionStrategy()
# pass the build_strategy to with_data_parallel API
compiled_prog = compiler.CompiledProgram(
train_program).with_data_parallel(
loss_name=ctc_loss.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
train_reader.set_batch_generator(train_batch_reader)
test_reader.set_batch_generator(dev_batch_reader)
# run train
for epoch_id in range(num_epoch):
train_reader.start()
epoch_loss = []
time_begin = time.time()
batch_id = 0
step = 0
while True:
try:
fetch_list = [ctc_loss.name]
if batch_id % num_iterations_print == 0:
fetch = exe.run(
program=compiled_prog,
fetch_list=fetch_list,
return_numpy=False)
each_loss = fetch[0]
epoch_loss.extend(np.array(each_loss[0]) / batch_size)
print("epoch: %d, batch: %d, train loss: %f\n" %
(epoch_id, batch_id,
np.mean(each_loss[0]) / batch_size))
else:
each_loss = exe.run(
program=compiled_prog,
fetch_list=[],
return_numpy=False)
batch_id = batch_id + 1
except fluid.core.EOFException:
train_reader.reset()
break
time_end = time.time()
used_time = time_end - time_begin
if test_off:
print("\n--------Time: %f sec, epoch: %d, train loss: %f\n" %
(used_time, epoch_id, np.mean(np.array(epoch_loss))))
else:
print('\n----------Begin test...')
test_loss = self.test(
exe,
dev_batch_reader=dev_batch_reader,
test_program=test_prog,
test_reader=test_reader,
fetch_list=[ctc_loss])
print(
"--------Time: %f sec, epoch: %d, train loss: %f, test loss: %f"
% (used_time, epoch_id + pre_epoch,
np.mean(np.array(epoch_loss)), test_loss / batch_size))
if (epoch_id + 1) % save_epoch == 0:
self.save_param(exe, train_program,
"epoch_" + str(epoch_id + pre_epoch))
self.save_param(exe, train_program, "step_final")
print("\n------------Training finished!!!-------------")
def infer_batch_probs(self, infer_data, feeding_dict):
"""Infer the prob matrices 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 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 2-D probability matrix, and each consists of prob
vectors for one speech utterancce.
:rtype: List of matrix
"""
# define inferer
infer_program = fluid.Program()
startup_prog = fluid.Program()
# adapt the feeding dict according to the network
adapted_feeding_dict = self._adapt_feeding_dict(feeding_dict)
# prepare the network
with fluid.program_guard(infer_program, startup_prog):
with fluid.unique_name.guard():
feeder, log_probs, _ = self.create_network(is_infer=True)
infer_program = infer_program.clone(for_test=True)
exe = fluid.Executor(self._place)
exe.run(startup_prog)
# init param from pretrained_model
if not self._init_from_pretrained_model:
exit("No pretrain model file path!")
self.init_from_pretrained_model(exe, infer_program)
infer_results = []
time_begin = time.time()
# run inference
for i in range(infer_data[0].shape[0]):
each_log_probs = exe.run(
program=infer_program,
feed=feeder.feed(
[[infer_data[0][i], infer_data[2][i], infer_data[3][i]]]),
fetch_list=[log_probs],
return_numpy=False)
infer_results.extend(np.array(each_log_probs[0]))
# slice result
infer_results = np.array(infer_results)
seq_len = (infer_data[2] - 1) // 3 + 1
start_pos = [0] * (infer_data[0].shape[0] + 1)
for i in range(infer_data[0].shape[0]):
start_pos[i + 1] = start_pos[i] + seq_len[i][0]
probs_split = [
infer_results[start_pos[i]:start_pos[i + 1]]
for i in range(0, infer_data[0].shape[0])
]
return probs_split
def decode_batch_greedy(self, probs_split, vocab_list):
"""Decode by best path for a batch of probs matrix input.
:param probs_split: List of 2-D probability matrix, and each consists
of prob vectors for one speech utterancce.
:param probs_split: List of matrix
:param vocab_list: List of tokens in the vocabulary, for decoding.
:type vocab_list: list
:return: List of transcription texts.
:rtype: List of basestring
"""
results = []
for i, probs in enumerate(probs_split):
output_transcription = ctc_greedy_decoder(
probs_seq=probs, vocabulary=vocab_list)
results.append(output_transcription)
print(results)
return results
def init_ext_scorer(self, beam_alpha, beam_beta, language_model_path,
vocab_list):
"""Initialize the external scorer.
: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 language_model_path: Filepath for language model. If it is
empty, the external scorer will be set to
None, and the decoding method will be pure
beam search without scorer.
:type language_model_path: basestring|None
:param vocab_list: List of tokens in the vocabulary, for decoding.
:type vocab_list: list
"""
if 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")
else:
self._ext_scorer = None
self.logger.info("no language model provided, "
"decoding by pure beam search without scorer.")
def decode_batch_beam_search(self, probs_split, beam_alpha, beam_beta,
beam_size, cutoff_prob, cutoff_top_n,
vocab_list, num_processes):
"""Decode by beam search for a batch of probs matrix input.
:param probs_split: List of 2-D probability matrix, and each consists
of prob vectors for one speech utterancce.
:param probs_split: List of matrix
: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 num_processes: Number of processes (CPU) for decoder.
:type num_processes: int
:return: List of transcription texts.
:rtype: List of basestring
"""
if self._ext_scorer != None:
self._ext_scorer.reset_params(beam_alpha, beam_beta)
# 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]
return results
def _adapt_feeding_dict(self, feeding_dict):
"""Adapt feeding dict according to network struct.
To remove impacts from padding part, we add scale_sub_region layer and
sub_seq layer. For sub_seq layer, 'sequence_offset' and
'sequence_length' fields are appended. For each scale_sub_region layer
'convN_index_range' field is appended.
: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: Adapted feeding dict.
:rtype: dict|list
"""
adapted_feeding_dict = copy.deepcopy(feeding_dict)
if isinstance(feeding_dict, dict):
adapted_feeding_dict["sequence_offset"] = len(adapted_feeding_dict)
adapted_feeding_dict["sequence_length"] = len(adapted_feeding_dict)
for i in range(self._num_conv_layers):
adapted_feeding_dict["conv%d_index_range" %i] = \
len(adapted_feeding_dict)
elif isinstance(feeding_dict, list):
adapted_feeding_dict.append("sequence_offset")
adapted_feeding_dict.append("sequence_length")
for i in range(self._num_conv_layers):
adapted_feeding_dict.append("conv%d_index_range" % i)
else:
raise ValueError("Type of feeding_dict is %s, not supported." %
type(feeding_dict))
return adapted_feeding_dict