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 # reduce artifacts 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