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| # This is Multi-reference timbre encoder | |
| import torch | |
| from torch import nn | |
| from torch.nn.utils import remove_weight_norm, weight_norm | |
| from module.attentions import MultiHeadAttention | |
| class MRTE(nn.Module): | |
| def __init__(self, | |
| content_enc_channels=192, | |
| hidden_size=512, | |
| out_channels=192, | |
| kernel_size=5, | |
| n_heads=4, | |
| ge_layer = 2 | |
| ): | |
| super(MRTE, self).__init__() | |
| self.cross_attention = MultiHeadAttention(hidden_size,hidden_size,n_heads) | |
| self.c_pre = nn.Conv1d(content_enc_channels,hidden_size, 1) | |
| self.text_pre = nn.Conv1d(content_enc_channels,hidden_size, 1) | |
| self.c_post = nn.Conv1d(hidden_size,out_channels, 1) | |
| def forward(self, ssl_enc, ssl_mask, text, text_mask, ge, test=None): | |
| if(ge==None):ge=0 | |
| attn_mask = text_mask.unsqueeze(2) * ssl_mask.unsqueeze(-1) | |
| ssl_enc = self.c_pre(ssl_enc * ssl_mask) | |
| text_enc = self.text_pre(text * text_mask) | |
| if test != None: | |
| if test == 0: | |
| x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge | |
| elif test == 1: | |
| x = ssl_enc + ge | |
| elif test ==2: | |
| x = self.cross_attention(ssl_enc*0 * ssl_mask, text_enc * text_mask, attn_mask) + ge | |
| else: | |
| raise ValueError("test should be 0,1,2") | |
| else: | |
| x = self.cross_attention(ssl_enc * ssl_mask, text_enc * text_mask, attn_mask) + ssl_enc + ge | |
| x = self.c_post(x * ssl_mask) | |
| return x | |
| class SpeakerEncoder(torch.nn.Module): | |
| def __init__(self, mel_n_channels=80, model_num_layers=2, model_hidden_size=256, model_embedding_size=256): | |
| super(SpeakerEncoder, self).__init__() | |
| self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) | |
| self.linear = nn.Linear(model_hidden_size, model_embedding_size) | |
| self.relu = nn.ReLU() | |
| def forward(self, mels): | |
| self.lstm.flatten_parameters() | |
| _, (hidden, _) = self.lstm(mels.transpose(-1, -2)) | |
| embeds_raw = self.relu(self.linear(hidden[-1])) | |
| return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) | |
| class MELEncoder(nn.Module): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
| self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels, 1) | |
| def forward(self, x): | |
| # print(x.shape,x_lengths.shape) | |
| x = self.pre(x) | |
| x = self.enc(x) | |
| x = self.proj(x) | |
| return x | |
| class WN(torch.nn.Module): | |
| def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers): | |
| super(WN, self).__init__() | |
| assert(kernel_size % 2 == 1) | |
| self.hidden_channels =hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.in_layers = torch.nn.ModuleList() | |
| self.res_skip_layers = torch.nn.ModuleList() | |
| for i in range(n_layers): | |
| dilation = dilation_rate ** i | |
| padding = int((kernel_size * dilation - dilation) / 2) | |
| in_layer = nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, | |
| dilation=dilation, padding=padding) | |
| in_layer = weight_norm(in_layer) | |
| self.in_layers.append(in_layer) | |
| # last one is not necessary | |
| if i < n_layers - 1: | |
| res_skip_channels = 2 * hidden_channels | |
| else: | |
| res_skip_channels = hidden_channels | |
| res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) | |
| res_skip_layer = weight_norm(res_skip_layer, name='weight') | |
| self.res_skip_layers.append(res_skip_layer) | |
| def forward(self, x): | |
| output = torch.zeros_like(x) | |
| n_channels_tensor = torch.IntTensor([self.hidden_channels]) | |
| for i in range(self.n_layers): | |
| x_in = self.in_layers[i](x) | |
| acts = fused_add_tanh_sigmoid_multiply( | |
| x_in, | |
| n_channels_tensor) | |
| res_skip_acts = self.res_skip_layers[i](acts) | |
| if i < self.n_layers - 1: | |
| res_acts = res_skip_acts[:,:self.hidden_channels,:] | |
| x = (x + res_acts) | |
| output = output + res_skip_acts[:,self.hidden_channels:,:] | |
| else: | |
| output = output + res_skip_acts | |
| return output | |
| def remove_weight_norm(self): | |
| for l in self.in_layers: | |
| remove_weight_norm(l) | |
| for l in self.res_skip_layers: | |
| remove_weight_norm(l) | |
| def fused_add_tanh_sigmoid_multiply(input, n_channels): | |
| n_channels_int = n_channels[0] | |
| t_act = torch.tanh(input[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(input[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| if __name__ == '__main__': | |
| content_enc = torch.randn(3,192,100) | |
| content_mask = torch.ones(3,1,100) | |
| ref_mel = torch.randn(3,128,30) | |
| ref_mask = torch.ones(3,1,30) | |
| model = MRTE() | |
| out = model(content_enc,content_mask,ref_mel,ref_mask) | |
| print(out.shape) |