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import torch
import torch.nn as nn
class Conv1dGLU(nn.Module):
"""
Conv1d + GLU(Gated Linear Unit) with residual connection.
For GLU refer to https://arxiv.org/abs/1612.08083 paper.
"""
def __init__(self, in_channels, out_channels, kernel_size, dropout):
super(Conv1dGLU, self).__init__()
self.out_channels = out_channels
self.conv1 = nn.Conv1d(in_channels, 2 * out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.conv1(x)
x1, x2 = torch.split(x, self.out_channels, dim=1)
x = x1 * torch.sigmoid(x2)
x = residual + self.dropout(x)
return x
# modified from https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/module/modules.py#L766
class MelStyleEncoder(nn.Module):
"""MelStyleEncoder"""
def __init__(
self,
n_mel_channels=80,
style_hidden=128,
style_vector_dim=256,
style_kernel_size=5,
style_head=2,
dropout=0.1,
):
super(MelStyleEncoder, self).__init__()
self.in_dim = n_mel_channels
self.hidden_dim = style_hidden
self.out_dim = style_vector_dim
self.kernel_size = style_kernel_size
self.n_head = style_head
self.dropout = dropout
self.spectral = nn.Sequential(
nn.Linear(self.in_dim, self.hidden_dim),
nn.Mish(inplace=True),
nn.Dropout(self.dropout),
nn.Linear(self.hidden_dim, self.hidden_dim),
nn.Mish(inplace=True),
nn.Dropout(self.dropout),
)
self.temporal = nn.Sequential(
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
Conv1dGLU(self.hidden_dim, self.hidden_dim, self.kernel_size, self.dropout),
)
self.slf_attn = nn.MultiheadAttention(
self.hidden_dim,
self.n_head,
self.dropout,
batch_first=True
)
self.fc = nn.Linear(self.hidden_dim, self.out_dim)
def temporal_avg_pool(self, x, mask=None):
if mask is None:
return torch.mean(x, dim=1)
else:
len_ = (~mask).sum(dim=1).unsqueeze(1).type_as(x)
return torch.sum(x * ~mask.unsqueeze(-1), dim=1) / len_
def forward(self, x, x_mask=None):
x = x.transpose(1, 2)
# spectral
x = self.spectral(x)
# temporal
x = x.transpose(1, 2)
x = self.temporal(x)
x = x.transpose(1, 2)
# self-attention
if x_mask is not None:
x_mask = ~x_mask.squeeze(1).to(torch.bool)
x, _ = self.slf_attn(x, x, x, key_padding_mask=x_mask)
# fc
x = self.fc(x)
# temoral average pooling
w = self.temporal_avg_pool(x, mask=x_mask)
return w |