mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-30 18:24:28 +01:00
254 lines
7.3 KiB
Python
254 lines
7.3 KiB
Python
import torch
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import torch.nn as nn
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from functools import partial
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class STFT:
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def __init__(self, n_fft, hop_length, dim_f, device):
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self.n_fft = n_fft
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self.hop_length = hop_length
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self.window = torch.hann_window(window_length=self.n_fft, periodic=True)
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self.dim_f = dim_f
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self.device = device
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def __call__(self, x):
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x_is_mps = not x.device.type in ["cuda", "cpu"]
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if x_is_mps:
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x = x.cpu()
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window = self.window.to(x.device)
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batch_dims = x.shape[:-2]
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c, t = x.shape[-2:]
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x = x.reshape([-1, t])
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x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True,return_complex=False)
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x = x.permute([0, 3, 1, 2])
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x = x.reshape([*batch_dims, c, 2, -1, x.shape[-1]]).reshape([*batch_dims, c * 2, -1, x.shape[-1]])
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if x_is_mps:
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x = x.to(self.device)
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return x[..., :self.dim_f, :]
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def inverse(self, x):
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x_is_mps = not x.device.type in ["cuda", "cpu"]
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if x_is_mps:
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x = x.cpu()
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window = self.window.to(x.device)
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batch_dims = x.shape[:-3]
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c, f, t = x.shape[-3:]
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n = self.n_fft // 2 + 1
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f_pad = torch.zeros([*batch_dims, c, n - f, t]).to(x.device)
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x = torch.cat([x, f_pad], -2)
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x = x.reshape([*batch_dims, c // 2, 2, n, t]).reshape([-1, 2, n, t])
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x = x.permute([0, 2, 3, 1])
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x = x[..., 0] + x[..., 1] * 1.j
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x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop_length, window=window, center=True)
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x = x.reshape([*batch_dims, 2, -1])
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if x_is_mps:
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x = x.to(self.device)
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return x
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def get_norm(norm_type):
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def norm(c, norm_type):
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if norm_type == 'BatchNorm':
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return nn.BatchNorm2d(c)
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elif norm_type == 'InstanceNorm':
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return nn.InstanceNorm2d(c, affine=True)
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elif 'GroupNorm' in norm_type:
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g = int(norm_type.replace('GroupNorm', ''))
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return nn.GroupNorm(num_groups=g, num_channels=c)
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else:
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return nn.Identity()
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return partial(norm, norm_type=norm_type)
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def get_act(act_type):
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if act_type == 'gelu':
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return nn.GELU()
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elif act_type == 'relu':
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return nn.ReLU()
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elif act_type[:3] == 'elu':
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alpha = float(act_type.replace('elu', ''))
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return nn.ELU(alpha)
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else:
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raise Exception
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class Upscale(nn.Module):
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def __init__(self, in_c, out_c, scale, norm, act):
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super().__init__()
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self.conv = nn.Sequential(
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norm(in_c),
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act,
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nn.ConvTranspose2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
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)
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def forward(self, x):
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return self.conv(x)
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class Downscale(nn.Module):
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def __init__(self, in_c, out_c, scale, norm, act):
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super().__init__()
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self.conv = nn.Sequential(
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norm(in_c),
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act,
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nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=scale, stride=scale, bias=False)
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)
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def forward(self, x):
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return self.conv(x)
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class TFC_TDF(nn.Module):
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def __init__(self, in_c, c, l, f, bn, norm, act):
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super().__init__()
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self.blocks = nn.ModuleList()
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for i in range(l):
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block = nn.Module()
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block.tfc1 = nn.Sequential(
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norm(in_c),
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act,
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nn.Conv2d(in_c, c, 3, 1, 1, bias=False),
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)
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block.tdf = nn.Sequential(
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norm(c),
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act,
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nn.Linear(f, f // bn, bias=False),
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norm(c),
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act,
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nn.Linear(f // bn, f, bias=False),
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)
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block.tfc2 = nn.Sequential(
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norm(c),
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act,
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nn.Conv2d(c, c, 3, 1, 1, bias=False),
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)
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block.shortcut = nn.Conv2d(in_c, c, 1, 1, 0, bias=False)
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self.blocks.append(block)
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in_c = c
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def forward(self, x):
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for block in self.blocks:
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s = block.shortcut(x)
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x = block.tfc1(x)
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x = x + block.tdf(x)
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x = block.tfc2(x)
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x = x + s
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return x
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class TFC_TDF_net(nn.Module):
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def __init__(self, config, device):
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super().__init__()
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self.config = config
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self.device = device
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norm = get_norm(norm_type=config.model.norm)
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act = get_act(act_type=config.model.act)
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self.num_target_instruments = 1 if config.training.target_instrument else len(config.training.instruments)
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self.num_subbands = config.model.num_subbands
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dim_c = self.num_subbands * config.audio.num_channels * 2
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n = config.model.num_scales
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scale = config.model.scale
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l = config.model.num_blocks_per_scale
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c = config.model.num_channels
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g = config.model.growth
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bn = config.model.bottleneck_factor
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f = config.audio.dim_f // self.num_subbands
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self.first_conv = nn.Conv2d(dim_c, c, 1, 1, 0, bias=False)
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self.encoder_blocks = nn.ModuleList()
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for i in range(n):
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block = nn.Module()
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block.tfc_tdf = TFC_TDF(c, c, l, f, bn, norm, act)
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block.downscale = Downscale(c, c + g, scale, norm, act)
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f = f // scale[1]
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c += g
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self.encoder_blocks.append(block)
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self.bottleneck_block = TFC_TDF(c, c, l, f, bn, norm, act)
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self.decoder_blocks = nn.ModuleList()
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for i in range(n):
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block = nn.Module()
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block.upscale = Upscale(c, c - g, scale, norm, act)
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f = f * scale[1]
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c -= g
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block.tfc_tdf = TFC_TDF(2 * c, c, l, f, bn, norm, act)
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self.decoder_blocks.append(block)
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self.final_conv = nn.Sequential(
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nn.Conv2d(c + dim_c, c, 1, 1, 0, bias=False),
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act,
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nn.Conv2d(c, self.num_target_instruments * dim_c, 1, 1, 0, bias=False)
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)
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self.stft = STFT(config.audio.n_fft, config.audio.hop_length, config.audio.dim_f, self.device)
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def cac2cws(self, x):
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k = self.num_subbands
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b, c, f, t = x.shape
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x = x.reshape(b, c, k, f // k, t)
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x = x.reshape(b, c * k, f // k, t)
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return x
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def cws2cac(self, x):
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k = self.num_subbands
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b, c, f, t = x.shape
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x = x.reshape(b, c // k, k, f, t)
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x = x.reshape(b, c // k, f * k, t)
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return x
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def forward(self, x):
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x = self.stft(x)
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mix = x = self.cac2cws(x)
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first_conv_out = x = self.first_conv(x)
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x = x.transpose(-1, -2)
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encoder_outputs = []
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for block in self.encoder_blocks:
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x = block.tfc_tdf(x)
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encoder_outputs.append(x)
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x = block.downscale(x)
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x = self.bottleneck_block(x)
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for block in self.decoder_blocks:
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x = block.upscale(x)
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x = torch.cat([x, encoder_outputs.pop()], 1)
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x = block.tfc_tdf(x)
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x = x.transpose(-1, -2)
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x = x * first_conv_out # reduce artifacts
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x = self.final_conv(torch.cat([mix, x], 1))
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x = self.cws2cac(x)
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if self.num_target_instruments > 1:
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b, c, f, t = x.shape
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x = x.reshape(b, self.num_target_instruments, -1, f, t)
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x = self.stft.inverse(x)
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return x
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