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Zero
| """ | |
| Code taken from https://github.com/tuanh123789/AdaSpeech/blob/main/model/adaspeech_modules.py | |
| By https://github.com/tuanh123789 | |
| No license specified | |
| Implemented as outlined in AdaSpeech https://arxiv.org/pdf/2103.00993.pdf | |
| Used in this toolkit similar to how it is done in AdaSpeech 4 https://arxiv.org/pdf/2204.00436.pdf | |
| """ | |
| import torch | |
| from torch import nn | |
| class ConditionalLayerNorm(nn.Module): | |
| def __init__(self, | |
| hidden_dim, | |
| speaker_embedding_dim, | |
| dim=-1): | |
| super(ConditionalLayerNorm, self).__init__() | |
| self.dim = dim | |
| if isinstance(hidden_dim, int): | |
| self.normal_shape = hidden_dim | |
| self.speaker_embedding_dim = speaker_embedding_dim | |
| self.W_scale = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape), | |
| nn.Tanh(), | |
| nn.Linear(self.normal_shape, self.normal_shape)) | |
| self.W_bias = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape), | |
| nn.Tanh(), | |
| nn.Linear(self.normal_shape, self.normal_shape)) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| torch.nn.init.constant_(self.W_scale[0].weight, 0.0) | |
| torch.nn.init.constant_(self.W_scale[2].weight, 0.0) | |
| torch.nn.init.constant_(self.W_scale[0].bias, 1.0) | |
| torch.nn.init.constant_(self.W_scale[2].bias, 1.0) | |
| torch.nn.init.constant_(self.W_bias[0].weight, 0.0) | |
| torch.nn.init.constant_(self.W_bias[2].weight, 0.0) | |
| torch.nn.init.constant_(self.W_bias[0].bias, 0.0) | |
| torch.nn.init.constant_(self.W_bias[2].bias, 0.0) | |
| def forward(self, x, speaker_embedding): | |
| if self.dim != -1: | |
| x = x.transpose(-1, self.dim) | |
| mean = x.mean(dim=-1, keepdim=True) | |
| var = ((x - mean) ** 2).mean(dim=-1, keepdim=True) | |
| scale = self.W_scale(speaker_embedding) | |
| bias = self.W_bias(speaker_embedding) | |
| y = scale.unsqueeze(1) * ((x - mean) / var) + bias.unsqueeze(1) | |
| if self.dim != -1: | |
| y = y.transpose(-1, self.dim) | |
| return y | |
| class SequentialWrappableConditionalLayerNorm(nn.Module): | |
| def __init__(self, | |
| hidden_dim, | |
| speaker_embedding_dim): | |
| super(SequentialWrappableConditionalLayerNorm, self).__init__() | |
| if isinstance(hidden_dim, int): | |
| self.normal_shape = hidden_dim | |
| self.speaker_embedding_dim = speaker_embedding_dim | |
| self.W_scale = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape), | |
| nn.Tanh(), | |
| nn.Linear(self.normal_shape, self.normal_shape)) | |
| self.W_bias = nn.Sequential(nn.Linear(self.speaker_embedding_dim, self.normal_shape), | |
| nn.Tanh(), | |
| nn.Linear(self.normal_shape, self.normal_shape)) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| torch.nn.init.constant_(self.W_scale[0].weight, 0.0) | |
| torch.nn.init.constant_(self.W_scale[2].weight, 0.0) | |
| torch.nn.init.constant_(self.W_scale[0].bias, 1.0) | |
| torch.nn.init.constant_(self.W_scale[2].bias, 1.0) | |
| torch.nn.init.constant_(self.W_bias[0].weight, 0.0) | |
| torch.nn.init.constant_(self.W_bias[2].weight, 0.0) | |
| torch.nn.init.constant_(self.W_bias[0].bias, 0.0) | |
| torch.nn.init.constant_(self.W_bias[2].bias, 0.0) | |
| def forward(self, packed_input): | |
| x, speaker_embedding = packed_input | |
| mean = x.mean(dim=-1, keepdim=True) | |
| var = ((x - mean) ** 2).mean(dim=-1, keepdim=True) | |
| scale = self.W_scale(speaker_embedding) | |
| bias = self.W_bias(speaker_embedding) | |
| y = scale.unsqueeze(1) * ((x - mean) / var) + bias.unsqueeze(1) | |
| return y | |
| class AdaIN1d(nn.Module): | |
| """ | |
| MIT Licensed | |
| Copyright (c) 2022 Aaron (Yinghao) Li | |
| https://github.com/yl4579/StyleTTS/blob/main/models.py | |
| """ | |
| def __init__(self, style_dim, num_features): | |
| super().__init__() | |
| self.norm = nn.InstanceNorm1d(num_features, affine=False) | |
| self.fc = nn.Linear(style_dim, num_features * 2) | |
| def forward(self, x, s): | |
| h = self.fc(s) | |
| h = h.view(h.size(0), h.size(1), 1) | |
| gamma, beta = torch.chunk(h, chunks=2, dim=1) | |
| return (1 + gamma.transpose(1, 2)) * self.norm(x.transpose(1, 2)).transpose(1, 2) + beta.transpose(1, 2) | |