mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-24 14:05:00 +03:00
187 lines
7.4 KiB
Python
187 lines
7.4 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.nn import Conv1d
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from torch.nn.utils import weight_norm, remove_weight_norm
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from .commons import init_weights, get_padding, fused_add_tanh_sigmoid_multiply
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LRELU_SLOPE = 0.1
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class WN(torch.nn.Module):
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def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
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super(WN, self).__init__()
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assert kernel_size % 2 == 1
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self.hidden_channels = hidden_channels
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self.kernel_size = (kernel_size,)
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.p_dropout = p_dropout
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self.in_layers = torch.nn.ModuleList()
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self.res_skip_layers = torch.nn.ModuleList()
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self.drop = nn.Dropout(p_dropout)
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if gin_channels != 0:
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cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
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self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
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for i in range(n_layers):
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dilation = dilation_rate**i
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padding = int((kernel_size * dilation - dilation) / 2)
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in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilation, padding=padding)
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in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
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self.in_layers.append(in_layer)
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# last one is not necessary
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if i < n_layers - 1:
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res_skip_channels = 2 * hidden_channels
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else:
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res_skip_channels = hidden_channels
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res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
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self.res_skip_layers.append(res_skip_layer)
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def forward(self, x, x_mask, g=None, **kwargs):
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output = torch.zeros_like(x)
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n_channels_tensor = torch.IntTensor([self.hidden_channels])
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if g is not None:
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g = self.cond_layer(g)
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for i in range(self.n_layers):
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x_in = self.in_layers[i](x)
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if g is not None:
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cond_offset = i * 2 * self.hidden_channels
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g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
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else:
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g_l = torch.zeros_like(x_in)
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acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
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acts = self.drop(acts)
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res_skip_acts = self.res_skip_layers[i](acts)
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if i < self.n_layers - 1:
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res_acts = res_skip_acts[:, : self.hidden_channels, :]
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x = (x + res_acts) * x_mask
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output = output + res_skip_acts[:, self.hidden_channels :, :]
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else:
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output = output + res_skip_acts
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return output * x_mask
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def remove_weight_norm(self):
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if self.gin_channels != 0:
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torch.nn.utils.remove_weight_norm(self.cond_layer)
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for l in self.in_layers:
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torch.nn.utils.remove_weight_norm(l)
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for l in self.res_skip_layers:
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torch.nn.utils.remove_weight_norm(l)
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class ResBlock1(torch.nn.Module):
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
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super(ResBlock1, self).__init__()
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self.convs1 = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], padding=get_padding(kernel_size, dilation[2])))])
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1)))])
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self.convs2.apply(init_weights)
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def forward(self, x, x_mask=None):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c2(xt)
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x = xt + x
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if x_mask is not None:
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x = x * x_mask
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class ResBlock2(torch.nn.Module):
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def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
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super(ResBlock2, self).__init__()
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self.convs = nn.ModuleList([weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], padding=get_padding(kernel_size, dilation[0]))), weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], padding=get_padding(kernel_size, dilation[1])))])
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self.convs.apply(init_weights)
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def forward(self, x, x_mask=None):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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if x_mask is not None:
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xt = xt * x_mask
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xt = c(xt)
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x = xt + x
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if x_mask is not None:
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x = x * x_mask
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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class Flip(nn.Module):
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def forward(self, x, *args, reverse=False, **kwargs):
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x = torch.flip(x, [1])
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if not reverse:
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logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
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return x, logdet
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else:
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return x
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class ResidualCouplingLayer(nn.Module):
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def __init__(self, channels, hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=0, mean_only=False):
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assert channels % 2 == 0, "channels should be divisible by 2"
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.half_channels = channels // 2
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self.mean_only = mean_only
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self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
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self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
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self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
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self.post.weight.data.zero_()
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self.post.bias.data.zero_()
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def forward(self, x, x_mask, g=None, reverse=False):
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x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
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h = self.pre(x0) * x_mask
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h = self.enc(h, x_mask, g=g)
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stats = self.post(h) * x_mask
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if not self.mean_only:
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m, logs = torch.split(stats, [self.half_channels] * 2, 1)
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else:
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m = stats
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logs = torch.zeros_like(m)
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if not reverse:
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x1 = m + x1 * torch.exp(logs) * x_mask
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x = torch.cat([x0, x1], 1)
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logdet = torch.sum(logs, [1, 2])
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return x, logdet
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else:
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x1 = (x1 - m) * torch.exp(-logs) * x_mask
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x = torch.cat([x0, x1], 1)
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return x
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