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PaddleSpeech/paddlespeech/t2s/modules/conformer/convolution.py

82 lines
2.5 KiB

3 years ago
# 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.
# Modified from espnet(https://github.com/espnet/espnet)
"""ConvolutionModule definition."""
from paddle import nn
class ConvolutionModule(nn.Layer):
"""ConvolutionModule in Conformer model.
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernerl size of conv layers.
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"""
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
"""Construct an ConvolutionModule object."""
super().__init__()
# kernerl_size should be a odd number for 'SAME' padding
assert (kernel_size - 1) % 2 == 0
self.pointwise_conv1 = nn.Conv1D(
channels,
2 * channels,
kernel_size=1,
stride=1,
padding=0,
bias_attr=bias, )
self.depthwise_conv = nn.Conv1D(
channels,
channels,
kernel_size,
stride=1,
padding=(kernel_size - 1) // 2,
groups=channels,
bias_attr=bias, )
self.norm = nn.BatchNorm1D(channels)
self.pointwise_conv2 = nn.Conv1D(
channels,
channels,
kernel_size=1,
stride=1,
padding=0,
bias_attr=bias, )
self.activation = activation
def forward(self, x):
"""Compute convolution module.
Args:
x (Tensor): Input tensor (#batch, time, channels).
Returns:
Tensor: Output tensor (#batch, time, channels).
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"""
# exchange the temporal dimension and the feature dimension
x = x.transpose([0, 2, 1])
# GLU mechanism
# (batch, 2*channel, time)
x = self.pointwise_conv1(x)
# (batch, channel, time)
x = nn.functional.glu(x, axis=1)
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# 1D Depthwise Conv
x = self.depthwise_conv(x)
x = self.activation(self.norm(x))
x = self.pointwise_conv2(x)
return x.transpose([0, 2, 1])