Cool_Tools/ultimatevocalremovergui
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Update spec_utils.py

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aufr33 2021-06-04 18:04:51 +03:00 committed by GitHub
parent 88d48c3d0e
commit 2fd50e11f8
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1 changed files with 40 additions and 29 deletions
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lib/spec_utils.py
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@ -327,8 +327,8 @@ def fft_hp_filter(spec, bin_start, bin_stop):
spec[:, 0:bin_stop+1, :] *= 0 spec[:, 0:bin_stop+1, :] *= 0
return spec return spec
def mirroring(a, spec_m, input_high_end, mp): def mirroring(a, spec_m, input_high_end, mp):
if 'mirroring' == a: if 'mirroring' == a:
mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1) mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
@ -343,22 +343,22 @@ def mirroring(a, spec_m, input_high_end, mp):
return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi) return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
def ensembling(a, specs): def ensembling(a, specs):
for i in range(1, len(specs)): for i in range(1, len(specs)):
if i == 1: if i == 1:
spec = specs[i-1] spec = specs[0]
ln = min([spec.shape[2], specs[i].shape[2]]) ln = min([spec.shape[2], specs[i].shape[2]])
spec = spec[:,:,:ln] spec = spec[:,:,:ln]
specs[i] = specs[i][:,:,:ln] specs[i] = specs[i][:,:,:ln]
if 'min_mag' == a: if 'min_mag' == a:
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec) spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
if 'max_mag' == a: if 'max_mag' == a:
spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec) spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
return spec return spec
if __name__ == "__main__": if __name__ == "__main__":
import cv2 import cv2
@ -368,7 +368,7 @@ if __name__ == "__main__":
from model_param_init import ModelParameters from model_param_init import ModelParameters
p = argparse.ArgumentParser() p = argparse.ArgumentParser()
p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'min_mag', 'max_mag', 'deep'], default='min_mag') p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'invert_p', 'min_mag', 'max_mag', 'deep'], default='min_mag')
p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json')) p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json'))
p.add_argument('--output_name', '-o', type=str, default='output') p.add_argument('--output_name', '-o', type=str, default='output')
p.add_argument('--vocals_only', '-v', action='store_true') p.add_argument('--vocals_only', '-v', action='store_true')
@ -377,19 +377,20 @@ if __name__ == "__main__":
start_time = time.time() start_time = time.time()
if args.algorithm == 'invert' and len(args.input) != 2: if args.algorithm.startswith('invert') and len(args.input) != 2:
raise ValueError('There should be two input files.') raise ValueError('There should be two input files.')
if args.algorithm != 'invert' and len(args.input) < 2: if not args.algorithm.startswith('invert') and len(args.input) < 2:
raise ValueError('There must be at least two input files.') raise ValueError('There must be at least two input files.')
wave, specs = {}, {} wave, specs = {}, {}
mp = ModelParameters(args.model_params) mp = ModelParameters(args.model_params)
for i in range(len(args.input)): for i in range(len(args.input)):
spec = {}
for d in range(len(mp.param['band']), 0, -1): for d in range(len(mp.param['band']), 0, -1):
bp = mp.param['band'][d] bp = mp.param['band'][d]
spec = {}
if d == len(mp.param['band']): # high-end band if d == len(mp.param['band']): # high-end band
wave[d], _ = librosa.load( wave[d], _ = librosa.load(
@ -411,25 +412,35 @@ if __name__ == "__main__":
v_spec = d_spec - specs[1] v_spec = d_spec - specs[1]
sf.write(os.path.join('{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr']) sf.write(os.path.join('{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])
if args.algorithm == 'invert': if args.algorithm.startswith('invert'):
specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2) ln = min([specs[0].shape[2], specs[1].shape[2]])
v_spec = specs[0] - specs[1] specs[0] = specs[0][:,:,:ln]
specs[1] = specs[1][:,:,:ln]
if not args.vocals_only:
if 'invert_p' == args.algorithm:
X_mag = np.abs(specs[0]) X_mag = np.abs(specs[0])
y_mag = np.abs(specs[1]) y_mag = np.abs(specs[1])
v_mag = np.abs(v_spec) max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
else:
specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
v_spec = specs[0] - specs[1]
X_image = spectrogram_to_image(X_mag) if not args.vocals_only:
y_image = spectrogram_to_image(y_mag) X_mag = np.abs(specs[0])
v_image = spectrogram_to_image(v_mag) y_mag = np.abs(specs[1])
v_mag = np.abs(v_spec)
cv2.imwrite('{}_X.png'.format(args.output_name), X_image) X_image = spectrogram_to_image(X_mag)
cv2.imwrite('{}_y.png'.format(args.output_name), y_image) y_image = spectrogram_to_image(y_mag)
cv2.imwrite('{}_v.png'.format(args.output_name), v_image) v_image = spectrogram_to_image(v_mag)
sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[0], mp), mp.param['sr']) cv2.imwrite('{}_X.png'.format(args.output_name), X_image)
sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[1], mp), mp.param['sr']) cv2.imwrite('{}_y.png'.format(args.output_name), y_image)
cv2.imwrite('{}_v.png'.format(args.output_name), v_image)
sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[0], mp), mp.param['sr'])
sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[1], mp), mp.param['sr'])
sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr']) sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])
else: else: