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PaddleSpeech/layer.py

276 lines
9.9 KiB

"""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, share_weights):
"""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
:param share_weights: Whether to share input-hidden weights between
forward and backward directional RNNs.
:type share_weights: bool
:return: Bidirectional simple rnn layer.
:rtype: LayerOutput
"""
if share_weights:
# input-hidden weights shared between 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)
else:
input_proj_forward = paddle.layer.fc(
input=input,
size=size,
act=paddle.activation.Linear(),
bias_attr=False)
input_proj_backward = 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_forward = paddle.layer.batch_norm(
input=input_proj_forward, act=paddle.activation.Linear())
input_proj_bn_backward = paddle.layer.batch_norm(
input=input_proj_backward, act=paddle.activation.Linear())
# forward and backward in time
forward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn_forward, act=act, reverse=False)
backward_simple_rnn = paddle.layer.recurrent(
input=input_proj_bn_backward, act=act, reverse=True)
return paddle.layer.concat(input=[forward_simple_rnn, backward_simple_rnn])
def bidirectional_gru_bn_layer(name, input, size, act):
"""Bidirectonal gru 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_proj_forward = paddle.layer.fc(
input=input,
size=size * 3,
act=paddle.activation.Linear(),
bias_attr=False)
input_proj_backward = paddle.layer.fc(
input=input,
size=size * 3,
act=paddle.activation.Linear(),
bias_attr=False)
# batch norm is only performed on input-related projections
input_proj_bn_forward = paddle.layer.batch_norm(
input=input_proj_forward, act=paddle.activation.Linear())
input_proj_bn_backward = paddle.layer.batch_norm(
input=input_proj_backward, act=paddle.activation.Linear())
# forward and backward in time
forward_gru = paddle.layer.grumemory(
input=input_proj_bn_forward, act=act, reverse=False)
backward_gru = paddle.layer.grumemory(
input=input_proj_bn_backward, act=act, reverse=True)
return paddle.layer.concat(input=[forward_gru, backward_gru])
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, use_gru, share_rnn_weights):
"""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
: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.
It is only available when use_gru=False.
:type share_weights: bool
:return: Output layer of the RNN group.
:rtype: LayerOutput
"""
output = input
for i in xrange(num_stacks):
if use_gru:
output = bidirectional_gru_bn_layer(
name=str(i),
input=output,
size=size,
act=paddle.activation.Relu())
# BRelu does not support hppl, need to add later. Use Relu instead.
else:
output = bidirectional_simple_rnn_bn_layer(
name=str(i),
input=output,
size=size,
act=paddle.activation.BRelu(),
share_weights=share_rnn_weights)
return output
def deep_speech2(audio_data,
text_data,
dict_size,
num_conv_layers=2,
num_rnn_layers=3,
rnn_size=256,
use_gru=False,
share_rnn_weights=True):
"""
The whole DeepSpeech2 model structure.
: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 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 direction RNNs.
It is only available when use_gru=False.
:type share_weights: bool
: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,
use_gru=use_gru,
share_rnn_weights=share_rnn_weights)
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