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https://github.com/Anjok07/ultimatevocalremovergui.git
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130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
import torch
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from torch._C import has_mkl
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import torch.nn as nn
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import numpy as np
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import librosa
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dim_c = 4
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model_path = 'model'
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class Conv_TDF_net_trim(nn.Module):
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def __init__(self, device, n_fft_scale, dim_f, load, model_name, target_name,
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L, dim_t, hop=1024):
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super(Conv_TDF_net_trim, self).__init__()
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self.dim_f, self.dim_t = dim_f, 2**dim_t
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self.n_fft = n_fft_scale
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self.hop = hop
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self.n_bins = self.n_fft//2+1
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self.chunk_size = hop * (self.dim_t-1)
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self.window = torch.hann_window(window_length=self.n_fft, periodic=False).to(device)
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self.target_name = target_name
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#print(n_fft_scale)
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out_c = dim_c*4 if target_name=='*' else dim_c
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self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device)
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def stft(self, x):
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x = x.reshape([-1, self.chunk_size])
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x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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x = x.permute([0,3,1,2])
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x = x.reshape([-1,2,2,self.n_bins,self.dim_t]).reshape([-1,dim_c,self.n_bins,self.dim_t])
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return x[:,:,:self.dim_f]
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def istft(self, x, freq_pad=None):
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freq_pad = self.freq_pad.repeat([x.shape[0],1,1,1]) if freq_pad is None else freq_pad
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x = torch.cat([x, freq_pad], -2)
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c = 4*2 if self.target_name=='*' else 2
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x = x.reshape([-1,c,2,self.n_bins,self.dim_t]).reshape([-1,2,self.n_bins,self.dim_t])
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x = x.permute([0,2,3,1])
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x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
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return x.reshape([-1,c,self.chunk_size])
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def stft(wave, nfft, hl):
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
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spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def istft(spec, hl):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hl)
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wave_right = librosa.istft(spec_right, hop_length=hl)
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wave = np.asfortranarray([wave_left, wave_right])
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return wave
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def spec_effects(wave, algorithm='Default', value=None):
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doubleout = spec = [stft(wave[0],2048,1024),stft(wave[1],2048,1024)]
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if algorithm == 'Min_Mag':
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doubleout
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v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
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wave = istft(v_spec_m,1024)
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elif algorithm == 'Max_Mag':
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doubleout
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v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
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wave = istft(v_spec_m,1024)
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elif algorithm == 'Default':
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doubleout
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#wave = [istft(spec[0],1024),istft(spec[1],1024)]
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wave = (wave[1] * value) + (wave[0] * (1-value))
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elif algorithm == 'Invert_p':
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doubleout
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X_mag = np.abs(spec[0])
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y_mag = np.abs(spec[1])
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max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
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v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0]))
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wave = istft(v_spec,1024)
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return wave
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def get_models(name, device, n_fft_scale, dim_f, load=True, stems='bdov'):
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if name=='tdf_extra':
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models = []
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if 'b' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load, n_fft_scale=n_fft_scale,
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model_name='Conv-TDF', target_name='bass',
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L=11, dim_f=dim_f, dim_t=8
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)
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)
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if 'd' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load, n_fft_scale=n_fft_scale,
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model_name='Conv-TDF', target_name='drums',
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L=9, dim_f=dim_f, dim_t=7
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)
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)
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if 'o' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load, n_fft_scale=n_fft_scale,
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model_name='Conv-TDF', target_name='other',
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L=11, dim_f=dim_f, dim_t=8
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)
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)
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if 'v' in stems:
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models.append(
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Conv_TDF_net_trim(
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device=device, load=load, n_fft_scale=n_fft_scale,
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model_name='Conv-TDF', target_name='vocals',
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L=11, dim_f=dim_f, dim_t=8
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)
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)
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return models
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else:
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print('Model undefined')
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return None
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