GPT-SoVITS/GPT_SoVITS/module/mrte_model.py

# 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)


@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)