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import torch |
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import torch.nn as nn |
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import segmentation_models_pytorch as smp |
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from utils import prefer_target_instrument |
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class STFT: |
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def __init__(self, config): |
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self.n_fft = config.n_fft |
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self.hop_length = config.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 = config.dim_f |
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def __call__(self, x): |
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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( |
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x, |
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n_fft=self.n_fft, |
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hop_length=self.hop_length, |
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window=window, |
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center=True, |
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return_complex=True |
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) |
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x = torch.view_as_real(x) |
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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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return x[..., :self.dim_f, :] |
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def inverse(self, x): |
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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( |
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x, |
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n_fft=self.n_fft, |
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hop_length=self.hop_length, |
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window=window, |
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center=True |
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) |
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x = x.reshape([*batch_dims, 2, -1]) |
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return x |
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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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def get_decoder(config, c): |
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decoder = None |
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decoder_options = dict() |
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if config.model.decoder_type == 'unet': |
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try: |
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decoder_options = dict(config.decoder_unet) |
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except: |
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pass |
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decoder = smp.Unet( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'fpn': |
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try: |
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decoder_options = dict(config.decoder_fpn) |
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except: |
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pass |
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decoder = smp.FPN( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'unet++': |
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try: |
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decoder_options = dict(config.decoder_unet_plus_plus) |
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except: |
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pass |
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decoder = smp.UnetPlusPlus( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'manet': |
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try: |
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decoder_options = dict(config.decoder_manet) |
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except: |
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pass |
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decoder = smp.MAnet( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'linknet': |
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try: |
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decoder_options = dict(config.decoder_linknet) |
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except: |
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pass |
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decoder = smp.Linknet( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'pspnet': |
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try: |
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decoder_options = dict(config.decoder_pspnet) |
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except: |
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pass |
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decoder = smp.PSPNet( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'pspnet': |
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try: |
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decoder_options = dict(config.decoder_pspnet) |
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except: |
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pass |
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decoder = smp.PSPNet( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'pan': |
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try: |
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decoder_options = dict(config.decoder_pan) |
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except: |
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pass |
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decoder = smp.PAN( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'deeplabv3': |
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try: |
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decoder_options = dict(config.decoder_deeplabv3) |
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except: |
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pass |
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decoder = smp.DeepLabV3( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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elif config.model.decoder_type == 'deeplabv3plus': |
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try: |
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decoder_options = dict(config.decoder_deeplabv3plus) |
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except: |
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pass |
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decoder = smp.DeepLabV3Plus( |
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encoder_name=config.model.encoder_name, |
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encoder_weights="imagenet", |
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in_channels=c, |
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classes=c, |
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**decoder_options, |
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) |
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return decoder |
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class Segm_Models_Net(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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act = get_act(act_type=config.model.act) |
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self.num_target_instruments = len(prefer_target_instrument(config)) |
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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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c = config.model.num_channels |
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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.unet_model = get_decoder(config, c) |
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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) |
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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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x = self.unet_model(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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