c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
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Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
import torch
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import numpy as np
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from torch import nn
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import torch.nn.functional as F
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from uvr5_pack.lib_v5 import layers_537238KB as layers
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class BaseASPPNet(nn.Module):
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def __init__(self, nin, ch, dilations=(4, 8, 16)):
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super(BaseASPPNet, self).__init__()
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self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
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self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
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self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
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self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
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self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
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self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
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self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
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self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
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self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
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def __call__(self, x):
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h, e1 = self.enc1(x)
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h, e2 = self.enc2(h)
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h, e3 = self.enc3(h)
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h, e4 = self.enc4(h)
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h = self.aspp(h)
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h = self.dec4(h, e4)
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h = self.dec3(h, e3)
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h = self.dec2(h, e2)
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h = self.dec1(h, e1)
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return h
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class CascadedASPPNet(nn.Module):
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def __init__(self, n_fft):
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super(CascadedASPPNet, self).__init__()
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self.stg1_low_band_net = BaseASPPNet(2, 64)
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self.stg1_high_band_net = BaseASPPNet(2, 64)
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self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
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self.stg2_full_band_net = BaseASPPNet(32, 64)
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self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
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self.stg3_full_band_net = BaseASPPNet(64, 128)
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self.out = nn.Conv2d(128, 2, 1, bias=False)
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self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
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self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
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self.max_bin = n_fft // 2
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self.output_bin = n_fft // 2 + 1
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self.offset = 128
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def forward(self, x, aggressiveness=None):
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mix = x.detach()
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x = x.clone()
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x = x[:, :, : self.max_bin]
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bandw = x.size()[2] // 2
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aux1 = torch.cat(
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[
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self.stg1_low_band_net(x[:, :, :bandw]),
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self.stg1_high_band_net(x[:, :, bandw:]),
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],
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dim=2,
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)
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h = torch.cat([x, aux1], dim=1)
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aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
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h = torch.cat([x, aux1, aux2], dim=1)
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h = self.stg3_full_band_net(self.stg3_bridge(h))
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mask = torch.sigmoid(self.out(h))
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mask = F.pad(
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input=mask,
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pad=(0, 0, 0, self.output_bin - mask.size()[2]),
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mode="replicate",
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)
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if self.training:
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aux1 = torch.sigmoid(self.aux1_out(aux1))
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aux1 = F.pad(
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input=aux1,
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pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
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mode="replicate",
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)
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aux2 = torch.sigmoid(self.aux2_out(aux2))
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aux2 = F.pad(
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input=aux2,
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pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
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mode="replicate",
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)
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return mask * mix, aux1 * mix, aux2 * mix
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else:
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if aggressiveness:
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mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
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mask[:, :, : aggressiveness["split_bin"]],
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1 + aggressiveness["value"] / 3,
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)
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mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
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mask[:, :, aggressiveness["split_bin"] :],
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1 + aggressiveness["value"],
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)
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return mask * mix
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def predict(self, x_mag, aggressiveness=None):
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h = self.forward(x_mag, aggressiveness)
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if self.offset > 0:
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h = h[:, :, :, self.offset : -self.offset]
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assert h.size()[3] > 0
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return h
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