mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-09 20:40:48 +08:00
optimize(infer): move modules into rvc
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源文雨
260
rvc/residuals.py
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260
rvc/residuals.py
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from typing import Optional
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import torch
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from torch import nn
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from torch.nn import Conv1d
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from torch.nn import functional as F
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from torch.nn.utils import remove_weight_norm, weight_norm
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from .norms import WN
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from .utils import (
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get_padding,
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call_weight_data_normal_if_Conv,
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)
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LRELU_SLOPE = 0.1
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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(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2]),
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)
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),
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]
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)
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self.convs1.apply(call_weight_data_normal_if_Conv)
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self.convs2 = nn.ModuleList(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=1,
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padding=get_padding(kernel_size, 1),
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)
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),
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]
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)
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self.convs2.apply(call_weight_data_normal_if_Conv)
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self.lrelu_slope = LRELU_SLOPE
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def forward(self, x: torch.Tensor, x_mask: Optional[torch.Tensor] = None):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, self.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, self.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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def __prepare_scriptable__(self):
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for l in self.convs1:
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for hook in l._forward_pre_hooks.values():
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if (
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hook.__module__ == "torch.nn.utils.weight_norm"
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and hook.__class__.__name__ == "WeightNorm"
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):
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torch.nn.utils.remove_weight_norm(l)
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for l in self.convs2:
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for hook in l._forward_pre_hooks.values():
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if (
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hook.__module__ == "torch.nn.utils.weight_norm"
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and hook.__class__.__name__ == "WeightNorm"
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):
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torch.nn.utils.remove_weight_norm(l)
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return self
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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(
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[
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]),
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)
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),
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weight_norm(
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Conv1d(
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channels,
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channels,
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kernel_size,
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1,
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dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]),
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)
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),
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]
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)
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self.convs.apply(call_weight_data_normal_if_Conv)
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self.lrelu_slope = LRELU_SLOPE
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def forward(self, x, x_mask: Optional[torch.Tensor] = None):
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for c in self.convs:
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xt = F.leaky_relu(x, self.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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def __prepare_scriptable__(self):
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for l in self.convs:
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for hook in l._forward_pre_hooks.values():
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if (
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hook.__module__ == "torch.nn.utils.weight_norm"
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and hook.__class__.__name__ == "WeightNorm"
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):
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torch.nn.utils.remove_weight_norm(l)
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return self
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class ResidualCouplingLayer(nn.Module):
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def __init__(
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self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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p_dropout=0,
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gin_channels=0,
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mean_only=False,
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):
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assert channels % 2 == 0, "channels should be divisible by 2"
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super(ResidualCouplingLayer, self).__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(
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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p_dropout=float(p_dropout),
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gin_channels=gin_channels,
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)
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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(
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self,
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x: torch.Tensor,
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x_mask: torch.Tensor,
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g: Optional[torch.Tensor] = None,
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reverse: bool = False,
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):
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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, torch.zeros([1])
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def remove_weight_norm(self):
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self.enc.remove_weight_norm()
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def __prepare_scriptable__(self):
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for hook in self.enc._forward_pre_hooks.values():
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if (
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hook.__module__ == "torch.nn.utils.weight_norm"
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and hook.__class__.__name__ == "WeightNorm"
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):
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torch.nn.utils.remove_weight_norm(self.enc)
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return self
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