Cool_Tools/ultimatevocalremovergui
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Anjok07 2021-06-01 03:21:36 -05:00 committed by GitHub
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1 changed files with 33 additions and 15 deletions
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ensemble_inference.py
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@ -102,6 +102,7 @@ def main():
p.add_argument('--output_image', '-I', action='store_true')
p.add_argument('--postprocess', '-p', action='store_true')
p.add_argument('--tta', '-t', action='store_true')
p.add_argument('--deepextraction', '-D', action='store_true')
p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation', 'mirroring', 'mirroring2'], default='bypass')
p.add_argument('--aggressiveness', '-A', type=float, default=0.05)
p.add_argument('--savein', '-s', action='store_true')
@ -116,19 +117,19 @@ def main():
args = p.parse_args()
####################################################-CLEAR-TEMP-FOLDER-########################################
#CLEAR-TEMP-FOLDER
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
####################################################-LOOPS-####################################################
#LOOPS
models = {
'MGM-v5-4Band-44100-BETA[12]':
{
'using_architecture': 'default',
'model_params': '4band_44100',
},
'HighPrecison_4band_[12]':
'HighPrecison_4band_[123456789]':
{
'using_architecture': '123821KB',
'model_params': '4band_44100',
@ -221,6 +222,7 @@ def main():
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
basename = '"{}"'.format(os.path.splitext(os.path.basename(args.input))[0])
basenameb = '{}'.format(os.path.splitext(os.path.basename(args.input))[0])
bands_n = len(mp.param['band'])
for d in range(bands_n, 0, -1):
@ -295,7 +297,7 @@ def main():
#model_name = os.path.splitext(os.path.basename(c['model_location']))[0]
sf.write(os.path.join('ensembled/temp', f"{ii+1}_{model_name}_{stems['inst']}.wav"), wave, mp.param['sr'])
if args.savein:
sf.write(os.path.join('separated', f"{basename}_{model_name}_{stems['inst']}.wav"), wave, mp.param['sr'])
sf.write(os.path.join('separated', f"{basenameb}_{model_name}_{stems['inst']}.wav"), wave, mp.param['sr'])
if True:
print('inverse stft of {}...'.format(stems['vocals']), end=' ')
@ -309,22 +311,22 @@ def main():
print('done')
sf.write(os.path.join('ensembled/temp', f"{ii+1}_{model_name}_{stems['vocals']}.wav"), wave, mp.param['sr'])
if args.savein:
sf.write(os.path.join('separated', f"{basename}_{model_name}_{stems['vocals']}.wav"), wave, mp.param['sr'])
sf.write(os.path.join('separated', f"{basenameb}_{model_name}_{stems['vocals']}.wav"), 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)
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('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
#############################################-ENSEMBLING-BEGIN-################################################
#ENSEMBLING-BEGIN
def get_files(folder="", suffix=""):
return [f"{folder}{i}" for i in os.listdir(folder) if i.endswith(suffix)]
@ -347,6 +349,22 @@ def main():
for i,e in tqdm(enumerate(ensambles), desc="Ensembling..."):
os.system(f"python lib/spec_utils.py -a {e['algorithm']} -m {e['model_params']} {' '.join(e['files'])} -o {e['output']}")
if args.deepextraction:
def get_files(folder="", files=""):
return [f"{folder}{i}" for i in os.listdir(folder) if i.endswith(suffix)]
deepext = [
{
'algorithm':'deep',
'model_params':'modelparams/1band_sr44100_hl512.json',
'file1':"ensembled/{}_Ensembled_Vocals.wav".format(basename),
'file2':"ensembled/{}_Ensembled_Instruments.wav".format(basename),
'output':'ensembled/{}_Ensembled_Deep_Extraction'.format(basename)
}
]
for i,e in tqdm(enumerate(deepext), desc="Performing Deep Extraction..."):
os.system(f"python lib/spec_utils.py -a {e['algorithm']} -m {e['model_params']} {e['file1']} {e['file2']} -o {e['output']}")
dir = 'ensembled/temp'
for file in os.scandir(dir):