Cool_Tools/ultimatevocalremovergui
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Anjok07 2021-05-04 19:45:16 -05:00 committed by GitHub
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3 changed files with 2940 additions and 20 deletions
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4Band_ens_inference.py Normal file
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allmodels_ens_inference.py Normal file
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inference.py
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@ -100,10 +100,11 @@ def main():
p.add_argument('--model_params', '-m', type=str, default='') p.add_argument('--model_params', '-m', type=str, default='')
p.add_argument('--window_size', '-w', type=int, default=512) p.add_argument('--window_size', '-w', type=int, default=512)
p.add_argument('--output_image', '-I', action='store_true') p.add_argument('--output_image', '-I', action='store_true')
p.add_argument('--deepextraction', '-D', action='store_true')
p.add_argument('--postprocess', '-p', action='store_true') p.add_argument('--postprocess', '-p', action='store_true')
p.add_argument('--tta', '-t', action='store_true') p.add_argument('--tta', '-t', action='store_true')
p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation'], default='none') p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation'], default='none')
p.add_argument('--aggressiveness', '-A', type=float, default=0.09) p.add_argument('--aggressiveness', '-A', type=float, default=0.07)
args = p.parse_args() args = p.parse_args()
if args.nn_architecture == 'default': if args.nn_architecture == 'default':
@ -114,6 +115,10 @@ def main():
from lib import nets_123821KB as nets from lib import nets_123821KB as nets
if args.nn_architecture == '129605KB': if args.nn_architecture == '129605KB':
from lib import nets_129605KB as nets from lib import nets_129605KB as nets
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
#if '' == args.model_params: #if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model) # mp = ModelParameters(args.pretrained_model)
@ -201,28 +206,69 @@ def main():
else: else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp) wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done') if args.deepextraction:
model_name = os.path.splitext(os.path.basename(args.pretrained_model))[0]
sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
if True:
print('inverse stft of {}...'.format(stems['vocals']), end=' ')
#v_spec_m = X_spec_m - y_spec_m
wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
print('done') print('done')
sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr']) model_name = os.path.splitext(os.path.basename(args.pretrained_model))[0]
sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
sf.write(os.path.join('ensembled/temp', 'tempI.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
if args.output_image: if True:
with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f: print('inverse stft of {}...'.format(stems['vocals']), end=' ')
image = spec_utils.spectrogram_to_image(y_spec_m) #v_spec_m = X_spec_m - y_spec_m
_, bin_image = cv2.imencode('.jpg', image) wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
bin_image.tofile(f) print('done')
with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f: sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
image = spec_utils.spectrogram_to_image(v_spec_m) sf.write(os.path.join('ensembled/temp', 'tempV.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f) if args.output_image:
with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
print('Performing Deep Extraction...')
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/tempI.wav ensembled/temp/tempV.wav -o ensembled/temp/difftemp")
os.system("python lib/diffext.py ensembled/temp/tempI.wav ensembled/temp/difftemp_v.wav ensembled/temp/aligned-difftemp_v.wav ensembled/temp/subtracted-difftemp_v.wav")
os.rename('ensembled/temp/subtracted-difftemp_v.wav', 'separated/{}_{}_DeepExtraction_Instruments.wav'.format(basename, model_name))
print('Complete!')
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
else:
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_model))[0]
sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
if True:
print('inverse stft of {}...'.format(stems['vocals']), end=' ')
#v_spec_m = X_spec_m - y_spec_m
wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
print('done')
sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
if args.output_image:
with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec_m)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
if __name__ == '__main__': if __name__ == '__main__':
main() main()