import torch from torch._C import has_mkl import torch.nn as nn import numpy as np import librosa dim_c = 4 k = 3 model_path = 'model' n_fft_scale = {'bass': 8, 'drums':2, 'other':4, 'vocals':3, '*':2} class Conv_TDF(nn.Module): def __init__(self, c, l, f, k, bn, bias=True): super(Conv_TDF, self).__init__() self.use_tdf = bn is not None self.H = nn.ModuleList() for i in range(l): self.H.append( nn.Sequential( nn.Conv2d(in_channels=c, out_channels=c, kernel_size=k, stride=1, padding=k//2), nn.BatchNorm2d(c), nn.ReLU(), ) ) if self.use_tdf: if bn==0: self.tdf = nn.Sequential( nn.Linear(f,f, bias=bias), nn.BatchNorm2d(c), nn.ReLU() ) else: self.tdf = nn.Sequential( nn.Linear(f,f//bn, bias=bias), nn.BatchNorm2d(c), nn.ReLU(), nn.Linear(f//bn,f, bias=bias), nn.BatchNorm2d(c), nn.ReLU() ) def forward(self, x): for h in self.H: x = h(x) return x + self.tdf(x) if self.use_tdf else x class Conv_TDF_net_trim(nn.Module): def __init__(self, device, load, model_name, target_name, lr, epoch, L, l, g, dim_f, dim_t, k=3, hop=1024, bn=None, bias=True): super(Conv_TDF_net_trim, self).__init__() self.dim_f, self.dim_t = 2**dim_f, 2**dim_t self.n_fft = self.dim_f * n_fft_scale[target_name] self.hop = hop self.n_bins = self.n_fft//2+1 self.chunk_size = hop * (self.dim_t-1) self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device) self.target_name = target_name self.blender = 'blender' in model_name out_c = dim_c*4 if target_name=='*' else dim_c in_c = dim_c*2 if self.blender else dim_c #out_c = dim_c*2 if self.blender else dim_c self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device) self.n = L//2 if load: self.first_conv = nn.Sequential( nn.Conv2d(in_channels=in_c, out_channels=g, kernel_size=1, stride=1), nn.BatchNorm2d(g), nn.ReLU(), ) f = self.dim_f c = g self.ds_dense = nn.ModuleList() self.ds = nn.ModuleList() for i in range(self.n): self.ds_dense.append(Conv_TDF(c, l, f, k, bn, bias=bias)) scale = (2,2) self.ds.append( nn.Sequential( nn.Conv2d(in_channels=c, out_channels=c+g, kernel_size=scale, stride=scale), nn.BatchNorm2d(c+g), nn.ReLU() ) ) f = f//2 c += g self.mid_dense = Conv_TDF(c, l, f, k, bn, bias=bias) #if bn is None and mid_tdf: # self.mid_dense = Conv_TDF(c, l, f, k, bn=0, bias=False) self.us_dense = nn.ModuleList() self.us = nn.ModuleList() for i in range(self.n): scale = (2,2) self.us.append( nn.Sequential( nn.ConvTranspose2d(in_channels=c, out_channels=c-g, kernel_size=scale, stride=scale), nn.BatchNorm2d(c-g), nn.ReLU() ) ) f = f*2 c -= g self.us_dense.append(Conv_TDF(c, l, f, k, bn, bias=bias)) self.final_conv = nn.Sequential( nn.Conv2d(in_channels=c, out_channels=out_c, kernel_size=1, stride=1), ) model_cfg = f'L{L}l{l}g{g}' model_cfg += ', ' if (bn is None or bn==0) else f'bn{bn}, ' stft_cfg = f'f{dim_f}t{dim_t}, ' model_name = model_name[:model_name.index('(')+1] + model_cfg + stft_cfg + model_name[model_name.index('(')+1:] try: self.load_state_dict( torch.load('{0}/{1}/{2}_lr{3}_e{4:05}.ckpt'.format(model_path, model_name, target_name, lr, epoch), map_location=device) ) print(f'Loading model ({target_name})') except FileNotFoundError: print(f'Random init ({target_name})') def stft(self, x): x = x.reshape([-1, self.chunk_size]) x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True) x = x.permute([0,3,1,2]) x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,dim_c,self.n_bins,self.dim_t]) return x[:,:,:self.dim_f] def istft(self, x, freq_pad=None): freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad x = torch.cat([x, freq_pad], -2) c = 4*2 if self.target_name=='*' else 2 x = x.reshape([-1,c,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t]) x = x.permute([0,2,3,1]) x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True) return x.reshape([-1,c,self.chunk_size]) def forward(self, x): x = self.first_conv(x) x = x.transpose(-1,-2) ds_outputs = [] for i in range(self.n): x = self.ds_dense[i](x) ds_outputs.append(x) x = self.ds[i](x) x = self.mid_dense(x) for i in range(self.n): x = self.us[i](x) x *= ds_outputs[-i-1] x = self.us_dense[i](x) x = x.transpose(-1,-2) x = self.final_conv(x) return x def stft(wave, nfft, hl): wave_left = np.asfortranarray(wave[0]) wave_right = np.asfortranarray(wave[1]) spec_left = librosa.stft(wave_left, nfft, hop_length=hl) spec_right = librosa.stft(wave_right, nfft, hop_length=hl) spec = np.asfortranarray([spec_left, spec_right]) return spec def istft(spec, hl): spec_left = np.asfortranarray(spec[0]) spec_right = np.asfortranarray(spec[1]) wave_left = librosa.istft(spec_left, hop_length=hl) wave_right = librosa.istft(spec_right, hop_length=hl) wave = np.asfortranarray([wave_left, wave_right]) return wave def spec_effects(wave, algorithm='default', value=None): spec = [stft(wave[0],2048,1024),stft(wave[1],2048,1024)] if algorithm == 'min_mag': v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0]) wave = istft(v_spec_m,1024) elif algorithm == 'max_mag': v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0]) wave = istft(v_spec_m,1024) elif algorithm == 'default': #wave = [istft(spec[0],1024),istft(spec[1],1024)] wave = (wave[1] * value) + (wave[0] * (1-value)) elif algorithm == 'invert_p': X_mag = np.abs(spec[0]) y_mag = np.abs(spec[1]) max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0])) wave = istft(v_spec,1024) return wave def get_models(name, device, load=True, stems='vocals'): if name=='tdf_extra': models = [] if 'vocals' in stems: models.append( Conv_TDF_net_trim( device=device, load=load, model_name='Conv-TDF', target_name='vocals', lr=0.0001, epoch=0, L=11, l=3, g=32, bn=8, bias=False, dim_f=11, dim_t=8 ) ) return models else: print('Model undefined') return None