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| | import torch |
| | from torch import nn |
| | from modules.base.base_module import LayerNorm |
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|
| | class DurationPredictor(nn.Module): |
| | def __init__( |
| | self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0 |
| | ): |
| | super().__init__() |
| |
|
| | self.in_channels = in_channels |
| | self.filter_channels = filter_channels |
| | self.kernel_size = kernel_size |
| | self.p_dropout = p_dropout |
| | self.gin_channels = gin_channels |
| |
|
| | self.drop = nn.Dropout(p_dropout) |
| | self.conv_1 = nn.Conv1d( |
| | in_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
| | ) |
| | self.norm_1 = LayerNorm(filter_channels) |
| | self.conv_2 = nn.Conv1d( |
| | filter_channels, filter_channels, kernel_size, padding=kernel_size // 2 |
| | ) |
| | self.norm_2 = LayerNorm(filter_channels) |
| | self.proj = nn.Conv1d(filter_channels, 1, 1) |
| |
|
| | if gin_channels != 0: |
| | self.cond = nn.Conv1d(gin_channels, in_channels, 1) |
| |
|
| | def forward(self, x, x_mask, g=None): |
| | x = torch.detach(x) |
| | if g is not None: |
| | g = torch.detach(g) |
| | x = x + self.cond(g) |
| | x = self.conv_1(x * x_mask) |
| | x = torch.relu(x) |
| | x = self.norm_1(x) |
| | x = self.drop(x) |
| | x = self.conv_2(x * x_mask) |
| | x = torch.relu(x) |
| | x = self.norm_2(x) |
| | x = self.drop(x) |
| | x = self.proj(x * x_mask) |
| | return x * x_mask |
| |
|
