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Runtime error
| import torch | |
| from torch import nn | |
| from .attentions_mechanism import LayerNorm | |
| 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 |