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"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
from paddle import nn
[docs]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.
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().__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()
[docs] def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor):
Batch of input tensors (B, T, in_chans).
Returns:
Tensor: Batch of output tensors (B, T, in_chans).
"""
x = self.relu(self.w_1(x.transpose([0, 2, 1]))).transpose([0, 2, 1])
out = self.w_2(self.dropout(x).transpose([0, 2, 1])).transpose([0, 2, 1])
return out
[docs]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.
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().__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()
[docs] def forward(self, x):
"""Calculate forward propagation.
Args:
x (Tensor):
Batch of input tensors (B, T, in_chans).
Returns:
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))