mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-24 07:20:10 +01:00
386 lines
15 KiB
Python
386 lines
15 KiB
Python
import argparse
|
|
import os, glob
|
|
|
|
import cv2
|
|
import librosa
|
|
import numpy as np
|
|
import soundfile as sf
|
|
import torch
|
|
|
|
import time, re
|
|
|
|
from tqdm import tqdm
|
|
from lib import dataset
|
|
from lib import spec_utils
|
|
from lib.model_param_init import ModelParameters
|
|
|
|
|
|
class VocalRemover(object):
|
|
|
|
def __init__(self, model, device, window_size):
|
|
self.model = model
|
|
self.offset = model.offset
|
|
self.device = device
|
|
self.window_size = window_size
|
|
|
|
def _execute(self, X_mag_pad, roi_size, n_window, aggressiveness):
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
preds = []
|
|
for i in tqdm(range(n_window)):
|
|
start = i * roi_size
|
|
X_mag_window = X_mag_pad[None, :, :, start:start + self.window_size]
|
|
X_mag_window = torch.from_numpy(X_mag_window).to(self.device)
|
|
|
|
pred = self.model.predict(X_mag_window, aggressiveness)
|
|
|
|
pred = pred.detach().cpu().numpy()
|
|
preds.append(pred[0])
|
|
|
|
pred = np.concatenate(preds, axis=2)
|
|
|
|
return pred
|
|
|
|
def preprocess(self, X_spec):
|
|
X_mag = np.abs(X_spec)
|
|
X_phase = np.angle(X_spec)
|
|
|
|
return X_mag, X_phase
|
|
|
|
def inference(self, X_spec, aggressiveness):
|
|
X_mag, X_phase = self.preprocess(X_spec)
|
|
|
|
coef = X_mag.max()
|
|
X_mag_pre = X_mag / coef
|
|
|
|
n_frame = X_mag_pre.shape[2]
|
|
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
|
|
n_window = int(np.ceil(n_frame / roi_size))
|
|
|
|
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
|
|
pred = pred[:, :, :n_frame]
|
|
|
|
return pred * coef, X_mag, np.exp(1.j * X_phase)
|
|
|
|
def inference_tta(self, X_spec, aggressiveness):
|
|
X_mag, X_phase = self.preprocess(X_spec)
|
|
|
|
coef = X_mag.max()
|
|
X_mag_pre = X_mag / coef
|
|
|
|
n_frame = X_mag_pre.shape[2]
|
|
pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
|
|
n_window = int(np.ceil(n_frame / roi_size))
|
|
|
|
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
|
|
pred = pred[:, :, :n_frame]
|
|
|
|
pad_l += roi_size // 2
|
|
pad_r += roi_size // 2
|
|
n_window += 1
|
|
|
|
X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred_tta = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
|
|
pred_tta = pred_tta[:, :, roi_size // 2:]
|
|
pred_tta = pred_tta[:, :, :n_frame]
|
|
|
|
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
|
|
|
|
|
|
def main():
|
|
p = argparse.ArgumentParser()
|
|
p.add_argument('--gpu', '-g', type=int, default=-1)
|
|
#p.add_argument('--is_vocal_model', '-vm', action='store_true')
|
|
p.add_argument('--input', '-i', required=True)
|
|
p.add_argument('--nn_architecture', '-n', type=str, default='default')
|
|
p.add_argument('--window_size', '-w', type=int, default=512)
|
|
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')
|
|
#p.add_argument('--model_params', '-m', type=str, default='modelparams/4band_44100.json')
|
|
dm = [
|
|
'MGM-v5-4Band-44100-BETA1', 'MGM-v5-4Band-44100-BETA2', 'HighPrecison_4band_1',
|
|
'HighPrecison_4band_2', 'NewLayer_4band_1', 'NewLayer_4band_2', 'NewLayer_4band_3',
|
|
'MGM-v5-MIDSIDE-44100-BETA1', 'MGM-v5-MIDSIDE-44100-BETA2', 'MGM-v5-3Band-44100-BETA',
|
|
'MGM-v5-2Band-32000-BETA1', 'MGM-v5-2Band-32000-BETA2', 'LOFI_2band-1_33966KB', 'LOFI_2band-2_33966KB'
|
|
]
|
|
p.add_argument('-P','--pretrained_models', nargs='+', type=str, default=dm)
|
|
|
|
args = p.parse_args()
|
|
|
|
#CLEAR-TEMP-FOLDER
|
|
dir = 'ensembled/temp'
|
|
for file in os.scandir(dir):
|
|
os.remove(file.path)
|
|
|
|
#LOOPS
|
|
models = {
|
|
'MGM-v5-4Band-44100-BETA[12]':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '4band_44100',
|
|
},
|
|
'HighPrecison_4band_[1-9]':
|
|
{
|
|
'using_architecture': '123821KB',
|
|
'model_params': '4band_44100',
|
|
},
|
|
'NewLayer_4band_[123]':
|
|
{
|
|
'using_architecture': '129605KB',
|
|
'model_params': '4band_44100',
|
|
},
|
|
'MGM-v5-MIDSIDE-44100-BETA[12]':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '3band_44100_mid',
|
|
},
|
|
'MGM-v5-3Band-44100-BETA':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '3band_44100',
|
|
},
|
|
'MGM-v5-2Band-32000-BETA[12]':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '2band_48000',
|
|
},
|
|
'LOFI_2band-[12]_33966KB':
|
|
{
|
|
'using_architecture': '33966KB',
|
|
'model_params': '2band_44100_lofi',
|
|
},
|
|
'MGM-v5-KAROKEE-32000-BETA1':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '2band_48000',
|
|
},
|
|
'MGM-v5-KAROKEE-32000-BETA2-AGR':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '2band_48000',
|
|
},
|
|
'MGM-v5-Vocal_2Band-32000-BETA[12]':
|
|
{
|
|
'using_architecture': 'default',
|
|
'model_params': '2band_48000',
|
|
'is_vocal_model': 'true'
|
|
},
|
|
'HP2-4BAND-3090_4band_[1-9]':
|
|
{
|
|
'using_architecture': '537238KB',
|
|
'model_params': '4band_44100'
|
|
},
|
|
'HP_4BAND_3090':
|
|
{
|
|
'using_architecture': '123821KB',
|
|
'model_params': '4band_44100'
|
|
}
|
|
}
|
|
|
|
from tqdm.auto import tqdm
|
|
|
|
for ii, model_name in tqdm(enumerate(args.pretrained_models), disable=True, desc='Iterations..'):
|
|
c = {}
|
|
|
|
for p in models:
|
|
if re.match(p, model_name):
|
|
c = models[p]
|
|
break
|
|
|
|
arch_now = c['using_architecture']
|
|
|
|
if arch_now == 'default':
|
|
from lib import nets
|
|
elif arch_now == '33966KB':
|
|
from lib import nets_33966KB as nets
|
|
elif arch_now == '123821KB':
|
|
from lib import nets_123821KB as nets
|
|
elif arch_now == '129605KB':
|
|
from lib import nets_129605KB as nets
|
|
elif arch_now == '537238KB':
|
|
from lib import nets_537238KB as nets
|
|
elif arch_now == '2064829KB':
|
|
from lib import nets_2064829KB as nets
|
|
|
|
|
|
mp = ModelParameters(os.path.join('modelparams', c['model_params']) + '.json')
|
|
|
|
start_time = time.time()
|
|
|
|
print(f'loading {model_name}...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(os.path.join('models', model_name) + '.pth', map_location=device))
|
|
if torch.cuda.is_available() and args.gpu >= 0:
|
|
device = torch.device('cuda:{}'.format(args.gpu))
|
|
model.to(device)
|
|
|
|
print('done')
|
|
|
|
print('loading & stft of wave source...', end=' ')
|
|
|
|
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):
|
|
bp = mp.param['band'][d]
|
|
|
|
if d == bands_n: # high-end band
|
|
X_wave[d], _ = librosa.load(
|
|
args.input, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
|
|
|
|
if X_wave[d].ndim == 1:
|
|
X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
|
|
else: # lower bands
|
|
X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
|
|
|
|
X_spec_s[d] = spec_utils.wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp, True)
|
|
|
|
if d == bands_n and args.high_end_process != 'none':
|
|
input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
|
|
input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
|
|
|
|
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
|
|
|
|
del X_wave, X_spec_s
|
|
|
|
print('done')
|
|
|
|
vr = VocalRemover(model, device, args.window_size)
|
|
|
|
if args.tta:
|
|
pred, X_mag, X_phase = vr.inference_tta(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
|
|
else:
|
|
pred, X_mag, X_phase = vr.inference(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
|
|
|
|
if args.postprocess: # if c['do_postprocess']:
|
|
print('post processing...', end=' ')
|
|
pred_inv = np.clip(X_mag - pred, 0, np.inf)
|
|
pred = spec_utils.mask_silence(pred, pred_inv)
|
|
print('done')
|
|
|
|
if 'is_vocal_model' in mp.param: # or args.is_vocal_model:
|
|
stems = {'inst': 'Vocals', 'vocals': 'Instruments'}
|
|
else:
|
|
stems = {'inst': 'Instruments', 'vocals': 'Vocals'}
|
|
|
|
print('inverse stft of {}...'.format(stems['inst']), end=' ')
|
|
y_spec_m = pred * X_phase
|
|
v_spec_m = X_spec_m - y_spec_m
|
|
|
|
if args.high_end_process == 'bypass':
|
|
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
|
|
elif args.high_end_process == 'correlation':
|
|
print('Deprecated: correlation will be removed in the final release. Please use the mirroring instead.')
|
|
|
|
for i in range(input_high_end.shape[2]):
|
|
for c in range(2):
|
|
X_mag_max = np.amax(input_high_end[c, :, i])
|
|
b1 = mp.param['pre_filter_start']-input_high_end_h//2
|
|
b2 = mp.param['pre_filter_start']-1
|
|
if X_mag_max > 0 and np.sum(np.abs(v_spec_m[c, b1:b2, i])) / (b2 - b1) > 0.07:
|
|
y_mag = np.median(y_spec_m[c, b1:b2, i])
|
|
input_high_end[c, :, i] = np.true_divide(input_high_end[c, :, i], abs(X_mag_max) / min(abs(y_mag * 4), abs(X_mag_max)))
|
|
|
|
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
|
|
elif args.high_end_process.startswith('mirroring'):
|
|
input_high_end_ = spec_utils.mirroring(args.high_end_process, y_spec_m, input_high_end, mp)
|
|
|
|
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_)
|
|
else:
|
|
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
|
|
|
|
print('done')
|
|
#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"{basenameb}_{model_name}_{stems['inst']}.wav"), wave, mp.param['sr'])
|
|
|
|
if True:
|
|
print('inverse stft of {}...'.format(stems['vocals']), end=' ')
|
|
|
|
if args.high_end_process.startswith('mirroring'):
|
|
input_high_end_ = spec_utils.mirroring(args.high_end_process, v_spec_m, input_high_end, mp)
|
|
|
|
wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_)
|
|
else:
|
|
wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
|
|
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"{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)
|
|
|
|
# print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
|
|
|
|
#ENSEMBLING-BEGIN
|
|
|
|
def get_files(folder="", suffix=""):
|
|
return [f"{folder}{i}" for i in os.listdir(folder) if i.endswith(suffix)]
|
|
|
|
ensambles = [
|
|
{
|
|
'algorithm':'min_mag',
|
|
'model_params':'modelparams/1band_sr44100_hl512.json',
|
|
'files':get_files(folder="ensembled/temp/", suffix="_Instruments.wav"),
|
|
'output':'{}_Ensembled_Instruments'.format(basename)
|
|
},
|
|
{
|
|
'algorithm':'max_mag',
|
|
'model_params':'modelparams/1band_sr44100_hl512.json',
|
|
'files':get_files(folder="ensembled/temp/", suffix="_Vocals.wav"),
|
|
'output': '{}_Ensembled_Vocals'.format(basename)
|
|
}
|
|
]
|
|
|
|
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):
|
|
os.remove(file.path)
|
|
print('Complete!')
|
|
|
|
if __name__ == '__main__':
|
|
main()
|