| import torch | |
| from torch import nn | |
| from torch.nn.utils import remove_weight_norm, weight_norm | |
| from fish_speech.models.vits_decoder.modules.attentions import MultiHeadAttention | |
| class MRTE(nn.Module): | |
| def __init__( | |
| self, | |
| content_enc_channels=192, | |
| hidden_size=512, | |
| out_channels=192, | |
| n_heads=4, | |
| ): | |
| 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 | |