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# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
# Northwestern Polytechnical University (Pengcheng Guo)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Adapted by Florian Lux 2021
from torch import nn
class ConvolutionModule(nn.Module):
"""
ConvolutionModule in Conformer model.
Args:
channels (int): The number of channels of conv layers.
kernel_size (int): Kernel size of conv layers.
"""
def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
super(ConvolutionModule, self).__init__()
# kernel_size should be an 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=bias, )
self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=bias, )
self.norm = nn.GroupNorm(num_groups=32, num_channels=channels)
self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=bias, )
self.activation = activation
def forward(self, x):
"""
Compute convolution module.
Args:
x (torch.Tensor): Input tensor (#batch, time, channels).
Returns:
torch.Tensor: Output tensor (#batch, time, channels).
"""
# exchange the temporal dimension and the feature dimension
x = x.transpose(1, 2)
# GLU mechanism
x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
# 1D Depthwise Conv
x = self.depthwise_conv(x)
x = self.activation(self.norm(x))
x = self.pointwise_conv2(x)
return x.transpose(1, 2)
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