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# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
# Adapted by Florian Lux 2021
"""
Layer modules for FFT block in FastSpeech (Feed-forward Transformer).
"""
import torch
class MultiLayeredConv1d(torch.nn.Module):
"""
Multi-layered conv1d for Transformer block.
This is a module of multi-layered conv1d designed
to replace positionwise feed-forward network
in Transformer 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.
Args:
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(MultiLayeredConv1d, self).__init__()
self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
self.w_2 = torch.nn.Conv1d(hidden_chans, in_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
self.dropout = torch.nn.Dropout(dropout_rate)
def forward(self, x):
"""
Calculate forward propagation.
Args:
x (torch.Tensor): Batch of input tensors (B, T, in_chans).
Returns:
torch.Tensor: Batch of output tensors (B, T, hidden_chans).
"""
x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
class Conv1dLinear(torch.nn.Module):
"""
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.
Args:
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(Conv1dLinear, self).__init__()
self.w_1 = torch.nn.Conv1d(in_chans, hidden_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, )
self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
self.dropout = torch.nn.Dropout(dropout_rate)
def forward(self, x):
"""
Calculate forward propagation.
Args:
x (torch.Tensor): Batch of input tensors (B, T, in_chans).
Returns:
torch.Tensor: Batch of output tensors (B, T, hidden_chans).
"""
x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
return self.w_2(self.dropout(x))
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