(
self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0,
)
| 372 | |
| 373 | class WNEncoder(nn.Module): |
| 374 | def __init__( |
| 375 | self, |
| 376 | in_channels, |
| 377 | out_channels, |
| 378 | hidden_channels, |
| 379 | kernel_size, |
| 380 | dilation_rate, |
| 381 | n_layers, |
| 382 | gin_channels=0, |
| 383 | ): |
| 384 | super().__init__() |
| 385 | self.in_channels = in_channels |
| 386 | self.out_channels = out_channels |
| 387 | self.hidden_channels = hidden_channels |
| 388 | self.kernel_size = kernel_size |
| 389 | self.dilation_rate = dilation_rate |
| 390 | self.n_layers = n_layers |
| 391 | self.gin_channels = gin_channels |
| 392 | |
| 393 | self.pre = nn.Conv1d(in_channels, hidden_channels, 1) |
| 394 | self.enc = modules.WN( |
| 395 | hidden_channels, |
| 396 | kernel_size, |
| 397 | dilation_rate, |
| 398 | n_layers, |
| 399 | gin_channels=gin_channels, |
| 400 | ) |
| 401 | self.proj = nn.Conv1d(hidden_channels, out_channels, 1) |
| 402 | self.norm = modules.LayerNorm(out_channels) |
| 403 | |
| 404 | def forward(self, x, x_lengths, g=None): |
| 405 | x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( |
no outgoing calls
no test coverage detected