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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| class LayerNorm(nn.Module): | |
| def __init__(self, channels, eps=1e-5): | |
| super().__init__() | |
| self.channels = channels | |
| self.eps = eps | |
| self.gamma = nn.Parameter(torch.ones(channels)) | |
| self.beta = nn.Parameter(torch.zeros(channels)) | |
| def forward(self, x): | |
| x = x.transpose(1, -1) | |
| x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) | |
| return x.transpose(1, -1) | |
| class ConvReluNorm(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| hidden_channels, | |
| out_channels, | |
| kernel_size, | |
| n_layers, | |
| p_dropout, | |
| ): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.hidden_channels = hidden_channels | |
| self.out_channels = out_channels | |
| self.kernel_size = kernel_size | |
| self.n_layers = n_layers | |
| self.p_dropout = p_dropout | |
| assert n_layers > 1, "Number of layers should be larger than 0." | |
| self.conv_layers = nn.ModuleList() | |
| self.norm_layers = nn.ModuleList() | |
| self.conv_layers.append( | |
| nn.Conv1d( | |
| in_channels, hidden_channels, kernel_size, padding=kernel_size // 2 | |
| ) | |
| ) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout)) | |
| for _ in range(n_layers - 1): | |
| self.conv_layers.append( | |
| nn.Conv1d( | |
| hidden_channels, | |
| hidden_channels, | |
| kernel_size, | |
| padding=kernel_size // 2, | |
| ) | |
| ) | |
| self.norm_layers.append(LayerNorm(hidden_channels)) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) | |
| self.proj.weight.data.zero_() | |
| self.proj.bias.data.zero_() | |
| def forward(self, x, x_mask): | |
| x_org = x | |
| for i in range(self.n_layers): | |
| x = self.conv_layers[i](x * x_mask) | |
| x = self.norm_layers[i](x) | |
| x = self.relu_drop(x) | |
| x = x_org + self.proj(x) | |
| return x * x_mask | |