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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 |
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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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