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| |
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|
| from abc import ABC |
|
|
| import torch |
|
|
| from Modules.GeneralLayers.ConditionalLayerNorm import AdaIN1d |
| from Modules.GeneralLayers.ConditionalLayerNorm import ConditionalLayerNorm |
| from Modules.GeneralLayers.LayerNorm import LayerNorm |
| from Utility.utils import integrate_with_utt_embed |
|
|
|
|
| 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, |
| utt_embed_dim=None, |
| embedding_integration="AdaIN"): |
| """ |
| Initialize 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() |
| self.dropouts = torch.nn.ModuleList() |
| self.norms = torch.nn.ModuleList() |
| self.embedding_projections = torch.nn.ModuleList() |
| self.utt_embed_dim = utt_embed_dim |
| self.use_conditional_layernorm_embedding_integration = embedding_integration in ["AdaIN", "ConditionalLayerNorm"] |
|
|
| for idx in range(n_layers): |
| if utt_embed_dim is not None: |
| if embedding_integration == "AdaIN": |
| self.embedding_projections += [AdaIN1d(style_dim=utt_embed_dim, num_features=idim)] |
| elif embedding_integration == "ConditionalLayerNorm": |
| self.embedding_projections += [ConditionalLayerNorm(speaker_embedding_dim=utt_embed_dim, hidden_dim=idim)] |
| else: |
| self.embedding_projections += [torch.nn.Linear(utt_embed_dim + idim, idim)] |
| else: |
| self.embedding_projections += [lambda x: x] |
| 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())] |
| self.norms += [LayerNorm(n_chans, dim=1)] |
| self.dropouts += [torch.nn.Dropout(dropout_rate)] |
|
|
| self.linear = torch.nn.Linear(n_chans, 1) |
|
|
| def forward(self, xs, padding_mask=None, utt_embed=None): |
| """ |
| Calculate forward propagation. |
| |
| Args: |
| xs (Tensor): Batch of input sequences (B, Tmax, idim). |
| padding_mask (ByteTensor, optional): |
| Batch of masks indicating padded part (B, Tmax). |
| |
| Returns: |
| Tensor: Batch of predicted sequences (B, Tmax, 1). |
| """ |
| xs = xs.transpose(1, -1) |
|
|
| for f, c, d, p in zip(self.conv, self.norms, self.dropouts, self.embedding_projections): |
| xs = f(xs) |
| if self.utt_embed_dim is not None: |
| xs = integrate_with_utt_embed(hs=xs.transpose(1, 2), utt_embeddings=utt_embed, projection=p, embedding_training=self.use_conditional_layernorm_embedding_integration).transpose(1, 2) |
| xs = c(xs) |
| xs = d(xs) |
|
|
| xs = self.linear(xs.transpose(1, 2)) |
|
|
| if padding_mask is not None: |
| xs = xs.masked_fill(padding_mask, 0.0) |
|
|
| return xs |
|
|
