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.
129 lines
3.8 KiB
129 lines
3.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
|
|
from paddle import nn
|
|
|
|
|
|
class MultiLayeredConv1d(nn.Layer):
|
|
"""Multi-layered conv1d for Transformer block.
|
|
|
|
This is a module of multi-leyered conv1d designed
|
|
to replace positionwise feed-forward network
|
|
in Transforner block, which is introduced in
|
|
`FastSpeech: Fast, Robust and Controllable Text to Speech`_.
|
|
|
|
.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
|
|
https://arxiv.org/pdf/1905.09263.pdf
|
|
|
|
"""
|
|
|
|
def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
|
|
"""Initialize MultiLayeredConv1d module.
|
|
|
|
Parameters
|
|
----------
|
|
in_chans : int
|
|
Number of input channels.
|
|
hidden_chans : int
|
|
Number of hidden channels.
|
|
kernel_size : int
|
|
Kernel size of conv1d.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
|
|
"""
|
|
super().__init__()
|
|
self.w_1 = nn.Conv1D(
|
|
in_chans,
|
|
hidden_chans,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=(kernel_size - 1) // 2, )
|
|
self.w_2 = nn.Conv1D(
|
|
hidden_chans,
|
|
in_chans,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=(kernel_size - 1) // 2, )
|
|
self.dropout = nn.Dropout(dropout_rate)
|
|
self.relu = nn.ReLU()
|
|
|
|
def forward(self, x):
|
|
"""Calculate forward propagation.
|
|
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Batch of input tensors (B, T, in_chans).
|
|
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
Batch of output tensors (B, T, in_chans).
|
|
"""
|
|
x = self.relu(self.w_1(x.transpose([0, 2, 1]))).transpose([0, 2, 1])
|
|
return self.w_2(self.dropout(x).transpose([0, 2, 1])).transpose(
|
|
[0, 2, 1])
|
|
|
|
|
|
class Conv1dLinear(nn.Layer):
|
|
"""Conv1D + Linear for Transformer block.
|
|
|
|
A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
|
|
|
|
"""
|
|
|
|
def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
|
|
"""Initialize Conv1dLinear module.
|
|
|
|
Parameters
|
|
----------
|
|
in_chans : int
|
|
Number of input channels.
|
|
hidden_chans : int
|
|
Number of hidden channels.
|
|
kernel_size : int
|
|
Kernel size of conv1d.
|
|
dropout_rate : float
|
|
Dropout rate.
|
|
"""
|
|
super().__init__()
|
|
self.w_1 = nn.Conv1D(
|
|
in_chans,
|
|
hidden_chans,
|
|
kernel_size,
|
|
stride=1,
|
|
padding=(kernel_size - 1) // 2, )
|
|
self.w_2 = nn.Linear(hidden_chans, in_chans, bias_attr=True)
|
|
self.dropout = nn.Dropout(dropout_rate)
|
|
self.relu = nn.ReLU()
|
|
|
|
def forward(self, x):
|
|
"""Calculate forward propagation.
|
|
|
|
Parameters
|
|
----------
|
|
x : paddle.Tensor
|
|
Batch of input tensors (B, T, in_chans).
|
|
|
|
Returns
|
|
----------
|
|
paddle.Tensor
|
|
Batch of output tensors (B, T, in_chans).
|
|
|
|
"""
|
|
x = self.relu(self.w_1(x.transpose([0, 2, 1]))).transpose([0, 2, 1])
|
|
|
|
return self.w_2(self.dropout(x))
|