| |
|
|
| 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): |
| |
| 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) |
|
|
| |
| 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) |
|
|
|
|
| @torch.jit.script |
| 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) |
|
|
