mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
optimize(rmvpe): move deepunet&e2e into rvc
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源文雨
217
rvc/f0/deepunet.py
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217
rvc/f0/deepunet.py
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from typing import List, Tuple, Union
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import torch
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import torch.nn as nn
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class ConvBlockRes(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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momentum: float = 0.01,
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):
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super(ConvBlockRes, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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nn.Conv2d(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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# self.shortcut:Optional[nn.Module] = None
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if in_channels != out_channels:
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self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
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def forward(self, x: torch.Tensor):
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if not hasattr(self, "shortcut"):
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return self.conv(x) + x
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else:
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return self.conv(x) + self.shortcut(x)
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels: int,
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in_size: int,
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n_encoders: int,
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kernel_size: Tuple[int, int],
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n_blocks: int,
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out_channels=16,
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momentum=0.01,
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):
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super(Encoder, self).__init__()
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self.n_encoders = n_encoders
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self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
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self.layers = nn.ModuleList()
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for _ in range(self.n_encoders):
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self.layers.append(
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ResEncoderBlock(
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in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
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)
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)
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in_channels = out_channels
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out_channels *= 2
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in_size //= 2
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self.out_size = in_size
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self.out_channel = out_channels
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def __call__(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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return super().__call__(x)
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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concat_tensors: List[torch.Tensor] = []
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x = self.bn(x)
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for layer in self.layers:
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t, x = layer(x)
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concat_tensors.append(t)
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return x, concat_tensors
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class ResEncoderBlock(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Tuple[int, int],
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n_blocks=1,
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momentum=0.01,
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):
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super(ResEncoderBlock, self).__init__()
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self.n_blocks = n_blocks
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self.kernel_size = kernel_size
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self.conv = nn.ModuleList()
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self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
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for _ in range(n_blocks - 1):
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self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
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if self.kernel_size is not None:
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self.pool = nn.AvgPool2d(kernel_size=kernel_size)
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def forward(
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self,
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x: torch.Tensor,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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for conv in self.conv:
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x = conv(x)
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if self.kernel_size is not None:
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return x, self.pool(x)
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return x
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class Intermediate(nn.Module):
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def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
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super(Intermediate, self).__init__()
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self.layers = nn.ModuleList()
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self.layers.append(
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ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
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)
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for _ in range(n_inters - 1):
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self.layers.append(
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ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
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)
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def forward(self, x):
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for layer in self.layers:
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x = layer(x)
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return x
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class ResDecoderBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
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super(ResDecoderBlock, self).__init__()
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out_padding = (0, 1) if stride == (1, 2) else (1, 1)
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self.conv1 = nn.Sequential(
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nn.ConvTranspose2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=stride,
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padding=(1, 1),
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output_padding=out_padding,
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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self.conv2 = nn.ModuleList()
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self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
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for _ in range(n_blocks - 1):
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self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
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def forward(self, x, concat_tensor):
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x = self.conv1(x)
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x = torch.cat((x, concat_tensor), dim=1)
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for conv2 in self.conv2:
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x = conv2(x)
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return x
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class Decoder(nn.Module):
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def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
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super(Decoder, self).__init__()
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self.layers = nn.ModuleList()
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self.n_decoders = n_decoders
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for _ in range(self.n_decoders):
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out_channels = in_channels // 2
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self.layers.append(
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ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
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)
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in_channels = out_channels
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def forward(self, x: torch.Tensor, concat_tensors: List[torch.Tensor]):
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for i, layer in enumerate(self.layers):
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x = layer(x, concat_tensors[-1 - i])
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return x
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class DeepUnet(nn.Module):
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def __init__(
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self,
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kernel_size: Tuple[int, int],
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n_blocks: int,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(DeepUnet, self).__init__()
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self.encoder = Encoder(
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in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
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)
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self.intermediate = Intermediate(
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self.encoder.out_channel // 2,
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self.encoder.out_channel,
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inter_layers,
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n_blocks,
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)
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self.decoder = Decoder(
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self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, concat_tensors = self.encoder(x)
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x = self.intermediate(x)
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x = self.decoder(x, concat_tensors)
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return x
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66
rvc/f0/e2e.py
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66
rvc/f0/e2e.py
Normal file
@@ -0,0 +1,66 @@
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from typing import Tuple
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import torch.nn as nn
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from .deepunet import DeepUnet
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class E2E(nn.Module):
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def __init__(
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self,
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n_blocks: int,
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n_gru: int,
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kernel_size: Tuple[int, int],
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(E2E, self).__init__()
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self.unet = DeepUnet(
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kernel_size,
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n_blocks,
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en_de_layers,
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inter_layers,
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in_channels,
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en_out_channels,
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)
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self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
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if n_gru:
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self.fc = nn.Sequential(
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self.BiGRU(3 * 128, 256, n_gru),
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nn.Linear(512, 360),
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nn.Dropout(0.25),
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nn.Sigmoid(),
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)
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else:
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self.fc = nn.Sequential(
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nn.Linear(3 * nn.N_MELS, nn.N_CLASS),
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nn.Dropout(0.25),
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nn.Sigmoid(),
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)
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def forward(self, mel):
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mel = mel.transpose(-1, -2).unsqueeze(1)
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x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
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x = self.fc(x)
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return x
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class BiGRU(nn.Module):
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def __init__(
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self,
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input_features: int,
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hidden_features: int,
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num_layers: int,
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):
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super().__init__()
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self.gru = nn.GRU(
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input_features,
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hidden_features,
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num_layers=num_layers,
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batch_first=True,
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bidirectional=True,
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)
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def forward(self, x):
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return self.gru(x)[0]
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