Hui Zhang
5 years ago
parent
054d795dc0
commit
6e412116b5
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import numpy as np
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle.nn import initializer as I
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def brelu(x, t_min=0.0, t_max=24.0, name=None):
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return paddle.min(paddle.max(x, t_min), t_max)
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def sequence_mask(x_len, max_len=None, dtype='float32'):
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max_len = (max_len or paddle.max(x))
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x_len = paddle.unsqueeze(x_len, -1)
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row_vector = paddle.arange(max_len)
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mask = row_vector < x_len
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mask = paddle.cast(mask, dtype)
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return mask
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class ConvBn(nn.Layer):
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"""Convolution layer with batch normalization.
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:param kernel_size: The x dimension of a filter kernel. Or input a tuple for
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two image dimension.
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:type kernel_size: int|tuple|list
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:param num_channels_in: Number of input channels.
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:type num_channels_in: int
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:param num_channels_out: Number of output channels.
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:type num_channels_out: int
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:param stride: The x dimension of the stride. Or input a tuple for two
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image dimension.
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:type stride: int|tuple|list
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:param padding: The x dimension of the padding. Or input a tuple for two
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image dimension.
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:type padding: int|tuple|list
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:param act: Activation type, relu|brelu
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:type act: string
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:param masks: Masks data layer to reset padding.
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:type masks: Variable
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:param name: Name of the layer.
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:param name: string
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:return: Batch norm layer after convolution layer.
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:rtype: Variable
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"""
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def __init__(self, num_channels_in, num_channels_out, kernel_size, stride,
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padding, act):
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super().__init__()
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.conv = nn.Conv2D(
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num_channels_in,
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num_channels_out,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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weight_attr=None,
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bias_attr=None,
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data_format='NCHW', )
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self.bn = nn.BatchNorm2D(
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num_channels=num_channels_out,
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param_attr=None,
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bias_attr=None,
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moving_mean_name=None,
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moving_variance_name=None,
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data_format='NCHW', )
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self.act = paddle.relu if act == 'relu' else brelu
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def forward(self, x, x_len):
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"""
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x(Tensor): audio, shape [B, C, D, T]
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"""
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x = self.conv(x)
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x = self.bn(x)
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x = self.act(x)
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# reset padding part to 0
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masks = sequence_mask(x_len) #[B, T]
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masks = masks.unsqueeze(1).unsqueeze(1) # [B, 1, 1, T]
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x = x.multiply(masks)
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x_len = (x_len - self.kernel_size[1] + 2 * self.padding[1]
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) // self.stride[1] + 1
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return x, x_len
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class ConvStack(nn.Layer):
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"""Convolution group with stacked convolution layers.
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:param feat_size: audio feature dim.
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:type feat_size: int
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:param num_stacks: Number of stacked convolution layers.
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:type num_stacks: int
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"""
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def __init__(self, feat_size, num_stacks):
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super().__init__()
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self.feat_size = feat_size # D
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self.num_stacks = num_stacks
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self.filter_size = (41, 11) # [D, T]
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self.stride = (2, 3)
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self.padding = (20, 5)
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self.conv_in = ConvBn(
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num_channels_in=1,
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num_channels_out=32,
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kernel_size=self.filter_size,
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stride=self.stride,
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padding=self.padding,
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act='brelu', )
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self.conv_stack = nn.LayerList([
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ConvBn(
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num_channels_in=32,
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num_channels_out=32,
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kernel_size=(21, 11),
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stride=(2, 1),
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padding=(10, 5),
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act='brelu') for i in range(num_stacks - 1)
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])
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# conv output feat_dim
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output_height = (feat_size - 1) // 2 + 1
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for i in range(self.num_stacks - 1):
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output_height = (output_height - 1) // 2 + 1
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self.output_height = output_height
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def forward(self, x, x_len):
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"""
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x: shape [B, C, D, T]
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x_len : shape [B]
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"""
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x, x_len = self.conv_in(x, x_len)
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for i, conv in enumerate(self.conv_stack):
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x, x_len = conv(x, x_len)
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return x, x_len
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class RNNCell(nn.RNNCellBase):
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r"""
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Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
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computes the outputs and updates states.
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The formula used is as follows:
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.. math::
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h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
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y_{t} & = h_{t}
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where :math:`act` is for :attr:`activation`.
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"""
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def __init__(self,
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hidden_size,
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activation="tanh",
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weight_ih_attr=None,
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weight_hh_attr=None,
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bias_ih_attr=None,
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bias_hh_attr=None,
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name=None):
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super().__init__()
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std = 1.0 / math.sqrt(hidden_size)
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self.weight_hh = self.create_parameter(
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(hidden_size, hidden_size),
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weight_hh_attr,
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default_initializer=I.Uniform(-std, std))
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self.bias_ih = self.create_parameter(
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(hidden_size, ),
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bias_ih_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.bias_hh = self.create_parameter(
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(hidden_size, ),
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bias_hh_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.hidden_size = hidden_size
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if activation not in ["tanh", "relu", "brelu"]:
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raise ValueError(
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"activation for SimpleRNNCell should be tanh or relu, "
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"but get {}".format(activation))
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self.activation = activation
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self._activation_fn = paddle.tanh \
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if activation == "tanh" \
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else F.relu
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if activation == 'brelu':
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self._activation_fn = brelu
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def forward(self, inputs, states=None):
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if states is None:
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states = self.get_initial_states(inputs, self.state_shape)
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pre_h = states
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i2h = inputs
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if self.bias_ih is not None:
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i2h += self.bias_ih
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h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
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if self.bias_hh is not None:
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h2h += self.bias_hh
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h = self._activation_fn(i2h + h2h)
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return h, h
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@property
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def state_shape(self):
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return (self.hidden_size, )
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class GRUCellShare(nn.RNNCellBase):
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r"""
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Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
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it computes the outputs and updates states.
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The formula for GRU used is as follows:
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.. math::
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r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
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z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
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\widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
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h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
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y_{t} & = h_{t}
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where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
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multiplication operator.
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"""
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def __init__(self,
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hidden_size,
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weight_ih_attr=None,
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weight_hh_attr=None,
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bias_ih_attr=None,
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bias_hh_attr=None,
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name=None):
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super(GRUCell, self).__init__()
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std = 1.0 / math.sqrt(hidden_size)
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self.weight_hh = self.create_parameter(
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(3 * hidden_size, hidden_size),
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weight_hh_attr,
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default_initializer=I.Uniform(-std, std))
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self.bias_ih = self.create_parameter(
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(3 * hidden_size, ),
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bias_ih_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.bias_hh = self.create_parameter(
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(3 * hidden_size, ),
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bias_hh_attr,
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is_bias=True,
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default_initializer=I.Uniform(-std, std))
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self.hidden_size = hidden_size
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self.input_size = input_size
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self._gate_activation = F.sigmoid
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self._activation = paddle.tanh
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def forward(self, inputs, states=None):
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if states is None:
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states = self.get_initial_states(inputs, self.state_shape)
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pre_hidden = states
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x_gates = inputs
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if self.bias_ih is not None:
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x_gates = x_gates + self.bias_ih
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h_gates = paddle.matmul(pre_hidden, self.weight_hh, transpose_y=True)
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if self.bias_hh is not None:
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h_gates = h_gates + self.bias_hh
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x_r, x_z, x_c = paddle.split(x_gates, num_or_sections=3, axis=1)
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h_r, h_z, h_c = paddle.split(h_gates, num_or_sections=3, axis=1)
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r = self._gate_activation(x_r + h_r)
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z = self._gate_activation(x_z + h_z)
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c = self._activation(x_c + r * h_c) # apply reset gate after mm
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h = (pre_hidden - c) * z + c
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return h, h
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@property
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def state_shape(self):
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r"""
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The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
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size would be automatically inserted into shape). The shape corresponds
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to the shape of :math:`h_{t-1}`.
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"""
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return (self.hidden_size, )
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class BiRNNWithBN(nn.Layer):
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"""Bidirectonal simple rnn layer with sequence-wise batch normalization.
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The batch normalization is only performed on input-state weights.
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:param name: Name of the layer parameters.
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:type name: string
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:param size: Dimension of RNN cells.
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:type size: int
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:param share_weights: Whether to share input-hidden weights between
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forward and backward directional RNNs.
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:type share_weights: bool
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:return: Bidirectional simple rnn layer.
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:rtype: Variable
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"""
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def __init__(self, i_size, h_size, share_weights):
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super().__init__()
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self.share_weights = share_weights
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self.pad_value = paddle.to_tensor(np.array([0.0], dtype=np.float32))
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if self.share_weights:
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#input-hidden weights shared between bi-directional rnn.
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self.fw_fc = nn.Linear(i_size, h_size)
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# batch norm is only performed on input-state projection
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self.fw_bn = nn.BatchNorm1D(h_size, data_format='NLC')
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self.bw_fc = self.fw_fc
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self.bw_bn = self.fw_bn
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else:
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self.fw_fc = nn.Linear(i_size, h_size)
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self.fw_bn = nn.BatchNorm1D(h_size, data_format='NLC')
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self.bw_fc = nn.Linear(i_size, h_size)
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self.bw_bn = nn.BatchNorm1D(h_size, data_format='NLC')
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self.fw_cell = RNNCell(hidden_size=h_size, activation='relu')
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self.bw_cell = RNNCell(
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hidden_size=h_size,
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activation='relu', )
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self.fw_rnn = nn.RNN(
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self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
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self.bw_rnn = nn.RNN(
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self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
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def forward(self, x, x_len):
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# x, shape [B, T, D]
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fw_x = self.fw_bn(self.fw_fc(x))
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bw_x = self.bw_bn(self.bw_bn(x))
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fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
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bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
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x = paddle.concat([fw_x, bw_x], axis=-1)
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return x, x_len
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class BiGRUWithBN(nn.Layer):
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"""Bidirectonal gru layer with sequence-wise batch normalization.
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The batch normalization is only performed on input-state weights.
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:param name: Name of the layer.
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:type name: string
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:param input: Input layer.
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:type input: Variable
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:param size: Dimension of GRU cells.
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:type size: int
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:param act: Activation type.
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:type act: string
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:return: Bidirectional GRU layer.
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:rtype: Variable
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"""
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def __init__(self, i_size, act):
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super().__init__()
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hidden_size = i_size * 3
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self.fw_fc = nn.Linear(i_size, hidden_size)
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self.fw_bn = nn.BatchNorm1D(hidden_size, data_format='NLC')
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self.bw_fc = nn.Linear(i_size, hidden_size)
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self.bw_bn = nn.BatchNorm1D(hidden_size, data_format='NLC')
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self.fw_cell = GRUCellShare(hidden_size)
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self.bw_cell = GRUCellShare(hidden_size)
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self.fw_rnn = nn.RNN(
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self.fw_cell, is_reverse=False, time_major=False) #[B, T, D]
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self.bw_rnn = nn.RNN(
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self.fw_cell, is_reverse=True, time_major=False) #[B, T, D]
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def forward(self, x, x_len):
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# x, shape [B, T, D]
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fw_x = self.fw_bn(self.fw_fc(x))
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bw_x = self.bw_bn(self.bw_bn(x))
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fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
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bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
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x = paddle.concat([fw_x, bw_x], axis=-1)
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return x, x_len
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class RNNStack(nn.Layer):
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"""RNN group with stacked bidirectional simple RNN or GRU layers.
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:param input: Input layer.
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:type input: Variable
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:param size: Dimension of RNN cells in each layer.
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:type size: int
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:param num_stacks: Number of stacked rnn layers.
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:type num_stacks: int
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:param use_gru: Use gru if set True. Use simple rnn if set False.
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:type use_gru: bool
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:param share_rnn_weights: Whether to share input-hidden weights between
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forward and backward directional RNNs.
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It is only available when use_gru=False.
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:type share_weights: bool
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:return: Output layer of the RNN group.
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:rtype: Variable
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"""
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def __init__(self, i_size, h_size, num_stacks, use_gru, share_rnn_weights):
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self.rnn_stacks = nn.LayerList()
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for i in range(num_stacks):
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if use_gru:
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#default:GRU using tanh
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self.rnn_stacks.append(BiGRUWithBN(size=i_size, act="relu"))
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else:
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self.rnn_stacks.append(
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BiRNNWithBN(
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i_size=i_size,
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size=h_size,
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share_weights=share_rnn_weights, ))
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def forward(self, x, x_len):
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"""
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x: shape [B, T, D]
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x_len: shpae [B]
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"""
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for i, rnn in enumerate(self.rnn_stacks):
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x, x_len = rnn(x, x_len)
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return x, x_len
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class DeepSpeech2(nn.Layer):
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"""The DeepSpeech2 network structure.
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:param audio_data: Audio spectrogram data layer.
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:type audio_data: Variable
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:param text_data: Transcription text data layer.
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:type text_data: Variable
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:param audio_len: Valid sequence length data layer.
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:type audio_len: Variable
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:param masks: Masks data layer to reset padding.
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:type masks: Variable
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:param dict_size: Dictionary size for tokenized transcription.
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:type dict_size: int
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:param num_conv_layers: Number of stacking convolution layers.
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||||
: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 (dimension 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
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
feat_size,
|
||||
dict_size,
|
||||
num_conv_layers=2,
|
||||
num_rnn_layers=3,
|
||||
rnn_size=256,
|
||||
use_gru=False,
|
||||
share_rnn_weight=True):
|
||||
super().__init__()
|
||||
self.feat_size = feat_size # 161 for linear
|
||||
self.dict_size = dict_size
|
||||
|
||||
self.conv = ConvStack(num_conv_layers)
|
||||
|
||||
i_size = self.conv.output_height(feat_size) # H after conv stack
|
||||
self.rnn = RNNStack(
|
||||
i_size=i_size,
|
||||
h_size=rnn_size,
|
||||
num_stacks=num_rnn_layers,
|
||||
use_gru=use_gru,
|
||||
share_rnn_weights=share_rnn_weights, )
|
||||
self.fc = nn.Linaer(rnn_size * 2, dict_size + 1)
|
||||
self.loss = nn.CTCLoss(blank=dict_size, reduction='none')
|
||||
|
||||
def forward(self, audio, text, audio_len, text_len):
|
||||
"""
|
||||
audio: shape [B, D, T]
|
||||
text: shape [B, T]
|
||||
audio_len: shape [B]
|
||||
text_len: shape [B]
|
||||
"""
|
||||
# [B, D, T] -> [B, C=1, D, T]
|
||||
audio = audio.unsqueeze(1)
|
||||
|
||||
# convolution group
|
||||
x, audio_len = self.conv(audio, audio_len)
|
||||
|
||||
# convert data from convolution feature map to sequence of vectors
|
||||
B, C, D, T = paddle.shape(x)
|
||||
x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
|
||||
x = x.reshape([0, -1, C * D]) #[B, T, C*D]
|
||||
|
||||
# remove padding part
|
||||
x, audio_len = self.rnn(x, audio_len) #[B, T, D]
|
||||
|
||||
logits = self.fc(x) #[B, T, V + 1]
|
||||
|
||||
#ctcdecoder need probs, not log_probs
|
||||
probs = F.log_softmax(logits)
|
||||
|
||||
if not text:
|
||||
return probs, None
|
||||
else:
|
||||
# warp-ctc do softmax on activations
|
||||
# warp-ctc need activation with shape [T, B, V + 1]
|
||||
logits = logits.transpose([1, 0, 2])
|
||||
ctc_loss = self.loss(logits, text, audio_len, text_len)
|
||||
ctc_loss = paddle.reduce_sum(ctc_loss)
|
||||
return probs, ctc_loss
|
||||
Loading…
Reference in new issue