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