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| import torch | |
| from torch import nn | |
| from ..generic.normalization import LayerNorm | |
| class DurationPredictor(nn.Module): | |
| """Glow-TTS duration prediction model. | |
| :: | |
| [2 x (conv1d_kxk -> relu -> layer_norm -> dropout)] -> conv1d_1x1 -> durs | |
| Args: | |
| in_channels (int): Number of channels of the input tensor. | |
| hidden_channels (int): Number of hidden channels of the network. | |
| kernel_size (int): Kernel size for the conv layers. | |
| dropout_p (float): Dropout rate used after each conv layer. | |
| """ | |
| def __init__(self, in_channels, hidden_channels, kernel_size, dropout_p, cond_channels=None, language_emb_dim=None): | |
| super().__init__() | |
| # add language embedding dim in the input | |
| if language_emb_dim: | |
| in_channels += language_emb_dim | |
| # class arguments | |
| self.in_channels = in_channels | |
| self.filter_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dropout_p = dropout_p | |
| # layers | |
| self.drop = nn.Dropout(dropout_p) | |
| self.conv_1 = nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2) | |
| self.norm_1 = LayerNorm(hidden_channels) | |
| self.conv_2 = nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2) | |
| self.norm_2 = LayerNorm(hidden_channels) | |
| # output layer | |
| self.proj = nn.Conv1d(hidden_channels, 1, 1) | |
| if cond_channels is not None and cond_channels != 0: | |
| self.cond = nn.Conv1d(cond_channels, in_channels, 1) | |
| if language_emb_dim != 0 and language_emb_dim is not None: | |
| self.cond_lang = nn.Conv1d(language_emb_dim, in_channels, 1) | |
| def forward(self, x, x_mask, g=None, lang_emb=None): | |
| """ | |
| Shapes: | |
| - x: :math:`[B, C, T]` | |
| - x_mask: :math:`[B, 1, T]` | |
| - g: :math:`[B, C, 1]` | |
| """ | |
| if g is not None: | |
| x = x + self.cond(g) | |
| if lang_emb is not None: | |
| x = x + self.cond_lang(lang_emb) | |
| 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 | |