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Delete models.py

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Anjok07
2022-11-02 03:24:31 -05:00
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commit 6a1b989119
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models.py
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import torch
from torch._C import has_mkl
import torch.nn as nn
import numpy as np
import librosa
dim_c = 4
model_path = 'model'
class Conv_TDF_net_trim(nn.Module):
def __init__(self, device, n_fft_scale, dim_f, load, model_name, target_name,
L, dim_t, hop=1024):
super(Conv_TDF_net_trim, self).__init__()
self.dim_f, self.dim_t = dim_f, 2**dim_t
self.n_fft = n_fft_scale
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=False).to(device)
self.target_name = target_name
#print(n_fft_scale)
out_c = dim_c*4 if target_name=='*' else dim_c
self.freq_pad = torch.zeros([1, out_c, self.n_bins-self.dim_f, self.dim_t]).to(device)
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 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):
doubleout = spec = [stft(wave[0],2048,1024),stft(wave[1],2048,1024)]
if algorithm == 'Min_Mag':
doubleout
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':
doubleout
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':
doubleout
#wave = [istft(spec[0],1024),istft(spec[1],1024)]
wave = (wave[1] * value) + (wave[0] * (1-value))
elif algorithm == 'Invert_p':
doubleout
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, n_fft_scale, dim_f, load=True, stems='bdov'):
if name=='tdf_extra':
models = []
if 'b' in stems:
models.append(
Conv_TDF_net_trim(
device=device, load=load, n_fft_scale=n_fft_scale,
model_name='Conv-TDF', target_name='bass',
L=11, dim_f=dim_f, dim_t=8
)
)
if 'd' in stems:
models.append(
Conv_TDF_net_trim(
device=device, load=load, n_fft_scale=n_fft_scale,
model_name='Conv-TDF', target_name='drums',
L=9, dim_f=dim_f, dim_t=7
)
)
if 'o' in stems:
models.append(
Conv_TDF_net_trim(
device=device, load=load, n_fft_scale=n_fft_scale,
model_name='Conv-TDF', target_name='other',
L=11, dim_f=dim_f, dim_t=8
)
)
if 'v' in stems:
models.append(
Conv_TDF_net_trim(
device=device, load=load, n_fft_scale=n_fft_scale,
model_name='Conv-TDF', target_name='vocals',
L=11, dim_f=dim_f, dim_t=8
)
)
return models
else:
print('Model undefined')
return None