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