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| # Copyright 2019 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| # Adapted by Florian Lux 2021 | |
| from abc import ABC | |
| import torch | |
| from Layers.LayerNorm import LayerNorm | |
| class VariancePredictor(torch.nn.Module, ABC): | |
| """ | |
| Variance predictor module. | |
| This is a module of variance predictor described in `FastSpeech 2: | |
| Fast and High-Quality End-to-End Text to Speech`_. | |
| .. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`: | |
| https://arxiv.org/abs/2006.04558 | |
| """ | |
| def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, bias=True, dropout_rate=0.5, ): | |
| """ | |
| Initilize duration predictor module. | |
| Args: | |
| idim (int): Input dimension. | |
| n_layers (int, optional): Number of convolutional layers. | |
| n_chans (int, optional): Number of channels of convolutional layers. | |
| kernel_size (int, optional): Kernel size of convolutional layers. | |
| dropout_rate (float, optional): Dropout rate. | |
| """ | |
| super().__init__() | |
| self.conv = torch.nn.ModuleList() | |
| for idx in range(n_layers): | |
| in_chans = idim if idx == 0 else n_chans | |
| self.conv += [ | |
| torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias, ), torch.nn.ReLU(), | |
| LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate), )] | |
| self.linear = torch.nn.Linear(n_chans, 1) | |
| def forward(self, xs, x_masks=None): | |
| """ | |
| Calculate forward propagation. | |
| Args: | |
| xs (Tensor): Batch of input sequences (B, Tmax, idim). | |
| x_masks (ByteTensor, optional): | |
| Batch of masks indicating padded part (B, Tmax). | |
| Returns: | |
| Tensor: Batch of predicted sequences (B, Tmax, 1). | |
| """ | |
| xs = xs.transpose(1, -1) # (B, idim, Tmax) | |
| for f in self.conv: | |
| xs = f(xs) # (B, C, Tmax) | |
| xs = self.linear(xs.transpose(1, 2)) # (B, Tmax, 1) | |
| if x_masks is not None: | |
| xs = xs.masked_fill(x_masks, 0.0) | |
| return xs | |