Cool_Tools/ultimatevocalremovergui
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ultimatevocalremovergui/4Band_ens_inference.py
Nerdy314159265 cb076dfc80
Alter 4Band_ens_inference.py to prevent crashing 
This change should prevent 4Band_ens_inference.py from crashing or only partially completing all the inference rounds when the window size is set below 320.
The NewLayer models fail when the size is below 320, so this change simply sets a lower limit of 320 for those rounds.
2021-05-16 04:30:37 -05:00

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import argparse
import os, glob
import cv2
import librosa
import numpy as np
import soundfile as sf
import torch
import time
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('--pretrained_modelA', '-a', type=str, default='models/MGM-v5-4Band-44100-BETA1.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelB', '-B', type=str, default='models/MGM-v5-4Band-44100-BETA2.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelC', '-C', type=str, default='models/HighPrecison_4band_1.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelD', '-Da', type=str, default='models/HighPrecison_4band_2.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelE', '-E', type=str, default='models/NewLayer_4band_1.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelF', '-F', type=str, default='models/NewLayer_4band_2.pth') ##DON'T CHNGE
p.add_argument('--pretrained_modelG', '-G', type=str, default='models/NewLayer_4band_3.pth') ##DON'T CHNGE
p.add_argument('--deepextraction', '-D', action='store_true')
p.add_argument('--saveindivsep', '-s', action='store_true')
p.add_argument('--input', '-i', required=True)
p.add_argument('--nn_architecture', '-n', type=str, default='default') ##DON'T CHNGE
p.add_argument('--nn_architectureA', '-aB', type=str, default='123821KB') ##DON'T CHNGE
p.add_argument('--nn_architectureB', '-bA', type=str, default='129605KB') ##DON'T CHNGE
p.add_argument('--model_params', '-m', type=str, default='modelparams/4band_44100.json') ##DON'T CHNGE
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('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation'], default='none')
p.add_argument('--aggressiveness', '-A', type=float, default=0.09)
args = p.parse_args()
####################################################-INERATION1-####################################################
if args.nn_architecture == 'default':
from lib import nets
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading MGM-v5-4Band-44100-BETA1...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelA, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelA))[0]
sf.write(os.path.join('ensembled/temp', '1_{}_{}.wav'.format(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('ensembled/temp', '1_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION2-####################################################
if args.nn_architecture == 'default':
from lib import nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading MGM-v5-4Band-44100-BETA2...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelB, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelB))[0]
sf.write(os.path.join('ensembled/temp', '2_{}_{}.wav'.format(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('ensembled/temp', '2_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION3-####################################################
if args.nn_architectureA == '123821KB':
from lib import nets_123821KB as nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading HighPrecison_4band_1...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelC, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelC))[0]
sf.write(os.path.join('ensembled/temp', '3_{}_{}.wav'.format(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('ensembled/temp', '3_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION4-####################################################
if args.nn_architectureA == '123821KB':
from lib import nets_123821KB as nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading HighPrecison_4band_2...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelD, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelD))[0]
sf.write(os.path.join('ensembled/temp', '4_{}_{}.wav'.format(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('ensembled/temp', '4_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION5-####################################################
if args.nn_architectureB == '129605KB':
from lib import nets_129605KB as nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading NewLayer_4band_1...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelE, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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, max(args.window_size,320))
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelE))[0]
sf.write(os.path.join('ensembled/temp', '5_{}_{}.wav'.format(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('ensembled/temp', '5_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION6-####################################################
if args.nn_architectureB == '129605KB':
from lib import nets_129605KB as nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading NewLayer_4band_2...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelF, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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, max(args.window_size,320))
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelF))[0]
sf.write(os.path.join('ensembled/temp', '6_{}_{}.wav'.format(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('ensembled/temp', '6_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################-INERATION7-####################################################
if args.nn_architectureB == '129605KB':
from lib import nets_129605KB as nets
#if '' == args.model_params:
# mp = ModelParameters(args.pretrained_model)
#else:
mp = ModelParameters(args.model_params)
start_time = time.time()
print('loading NewLayer_4band_3...', end=' ')
device = torch.device('cpu')
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
model.load_state_dict(torch.load(args.pretrained_modelG, 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 = 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_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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, max(args.window_size,320))
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:
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: # swap
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':
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)
else:
wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
print('done')
model_name = os.path.splitext(os.path.basename(args.pretrained_modelG))[0]
sf.write(os.path.join('ensembled/temp', '7_{}_{}.wav'.format(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('ensembled/temp', '7_{}_{}.wav'.format(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)
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
####################################################^INERATIONS-COMPLETE^####################################################
####################################################-ENSEMBLING-BEGIN-#######################################################
if args.deepextraction:
print('Ensembling Instrumentals...')
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Instruments.wav ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Instruments.wav -o ensembled/temp/1E2E_ensam1")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/3_HighPrecison_4band_1_Instruments.wav ensembled/temp/4_HighPrecison_4band_2_Instruments.wav -o ensembled/temp/3E4E_ensam1")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/5_NewLayer_4band_1_Instruments.wav ensembled/temp/6_NewLayer_4band_2_Instruments.wav -o ensembled/temp/5E6E_ensam3")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E_ensam1_v.wav ensembled/temp/3E4E_ensam1_v.wav -o ensembled/temp/1E2E3E4E_ensam4")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E3E4E_ensam4_v.wav ensembled/temp/5E6E_ensam3_v.wav -o ensembled/temp/A6_ensam5")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/7_NewLayer_4band_3_Instruments.wav ensembled/temp/A6_ensam5_v.wav -o ensembled/temp/Complete")
print('Ensembling Vocals...')
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Vocals.wav ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Vocals.wav -o ensembled/temp/1E2EV_ensam1")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/3_HighPrecison_4band_1_Vocals.wav ensembled/temp/4_HighPrecison_4band_2_Vocals.wav -o ensembled/temp/3E4EV_ensam1")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/5_NewLayer_4band_1_Vocals.wav ensembled/temp/6_NewLayer_4band_2_Vocals.wav -o ensembled/temp/5E6EV_ensam3")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2EV_ensam1_v.wav ensembled/temp/3E4EV_ensam1_v.wav -o ensembled/temp/1E2E3E4EV_ensam4")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E3E4EV_ensam4_v.wav ensembled/temp/5E6EV_ensam3_v.wav -o ensembled/temp/A6V_ensam5")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/7_NewLayer_4band_3_Vocals.wav ensembled/temp/A6V_ensam5_v.wav -o ensembled/temp/CompleteV")
print('Performing Deep Extraction...')
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/Complete_v.wav ensembled/temp/CompleteV_v.wav -o ensembled/temp/difftemp")
os.system("python lib/spec_utils.py -a invertB -m modelparams/1band_sr44100_hl512.json ensembled/temp/Complete_v.wav ensembled/temp/difftemp_v.wav -o ensembled/temp/difftempC")
os.rename('ensembled/temp/difftempC_v.wav', 'ensembled/{}_4BAND_Ensembled_DeepExtraction_Instrumental.wav'.format(basename))
os.rename('ensembled/temp/Complete_v.wav', 'ensembled/{}_4BAND_Ensembled_Instrumental.wav'.format(basename))
os.rename('ensembled/temp/CompleteV_v.wav', 'ensembled/{}_4BAND_Ensembled_Vocals.wav'.format(basename))
print('Deep Extraction Complete!')
else:
print('Ensembling Instrumentals...')
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Instruments.wav ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Instruments.wav -o ensembled/temp/1E2E_ensam1")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/3_HighPrecison_4band_1_Instruments.wav ensembled/temp/4_HighPrecison_4band_2_Instruments.wav -o ensembled/temp/3E4E_ensam1")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/5_NewLayer_4band_1_Instruments.wav ensembled/temp/6_NewLayer_4band_2_Instruments.wav -o ensembled/temp/5E6E_ensam3")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E_ensam1_v.wav ensembled/temp/3E4E_ensam1_v.wav -o ensembled/temp/1E2E3E4E_ensam4")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E3E4E_ensam4_v.wav ensembled/temp/5E6E_ensam3_v.wav -o ensembled/temp/A6_ensam5")
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/7_NewLayer_4band_3_Instruments.wav ensembled/temp/A6_ensam5_v.wav -o ensembled/temp/Complete")
os.rename('ensembled/temp/Complete_v.wav', 'ensembled/{}_4BAND_Ensembled_Instrumental.wav'.format(basename))
print('Ensembling Vocals...')
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Vocals.wav ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Vocals.wav -o ensembled/temp/1E2EV_ensam1")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/3_HighPrecison_4band_1_Vocals.wav ensembled/temp/4_HighPrecison_4band_2_Vocals.wav -o ensembled/temp/3E4EV_ensam1")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/5_NewLayer_4band_1_Vocals.wav ensembled/temp/6_NewLayer_4band_2_Vocals.wav -o ensembled/temp/5E6EV_ensam3")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2EV_ensam1_v.wav ensembled/temp/3E4EV_ensam1_v.wav -o ensembled/temp/1E2E3E4EV_ensam4")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1E2E3E4EV_ensam4_v.wav ensembled/temp/5E6EV_ensam3_v.wav -o ensembled/temp/A6V_ensam5")
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/7_NewLayer_4band_3_Vocals.wav ensembled/temp/A6V_ensam5_v.wav -o ensembled/temp/CompleteV")
os.rename('ensembled/temp/CompleteV_v.wav', 'ensembled/{}_4BAND_Ensembled_Vocals.wav'.format(basename))
if args.saveindivsep:
print('Saving Individual Separations...')
os.rename('ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Instruments.wav', 'separated/{}_MGM-v5-4Band-44100-BETA1_Instruments.wav'.format(basename))
os.rename('ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Instruments.wav', 'separated/{}_MGM-v5-4Band-44100-BETA2_Instruments.wav'.format(basename))
os.rename('ensembled/temp/3_HighPrecison_4band_1_Instruments.wav', 'separated/{}_HighPrecison_4band_1_Instruments.wav'.format(basename))
os.rename('ensembled/temp/4_HighPrecison_4band_2_Instruments.wav', 'separated/{}_HighPrecison_4band_2_Instruments.wav'.format(basename))
os.rename('ensembled/temp/5_NewLayer_4band_1_Instruments.wav', 'separated/{}_NewLayer_4band_1_Instruments.wav'.format(basename))
os.rename('ensembled/temp/6_NewLayer_4band_2_Instruments.wav', 'separated/{}_NewLayer_4band_2_Instruments.wav'.format(basename))
os.rename('ensembled/temp/7_NewLayer_4band_3_Instruments.wav', 'separated/{}_NewLayer_4band_3_Instruments.wav'.format(basename))
os.rename('ensembled/temp/1_MGM-v5-4Band-44100-BETA1_Vocals.wav', 'separated/{}_MGM-v5-4Band-44100-BETA1_Vocals.wav'.format(basename))
os.rename('ensembled/temp/2_MGM-v5-4Band-44100-BETA2_Vocals.wav', 'separated/{}_MGM-v5-4Band-44100-BETA2_Vocals.wav'.format(basename))
os.rename('ensembled/temp/3_HighPrecison_4band_1_Vocals.wav', 'separated/{}_HighPrecison_4band_1_Vocals.wav'.format(basename))
os.rename('ensembled/temp/4_HighPrecison_4band_2_Vocals.wav', 'separated/{}_HighPrecison_4band_2_Vocals.wav'.format(basename))
os.rename('ensembled/temp/5_NewLayer_4band_1_Vocals.wav', 'separated/{}_NewLayer_4band_1_Vocals.wav'.format(basename))
os.rename('ensembled/temp/6_NewLayer_4band_2_Vocals.wav', 'separated/{}_NewLayer_4band_2_Vocals.wav'.format(basename))
os.rename('ensembled/temp/7_NewLayer_4band_3_Vocals.wav', 'separated/{}_NewLayer_4band_3_Vocals.wav'.format(basename))
os.remove("ensembled/temp/A6V_ensam5_v.wav")
os.remove("ensembled/temp/1E2E3E4EV_ensam4_v.wav")
os.remove("ensembled/temp/5E6EV_ensam3_v.wav")
os.remove("ensembled/temp/3E4EV_ensam1_v.wav")
os.remove("ensembled/temp/1E2EV_ensam1_v.wav")
os.remove("ensembled/temp/A6_ensam5_v.wav")
os.remove("ensembled/temp/1E2E3E4E_ensam4_v.wav")
os.remove("ensembled/temp/5E6E_ensam3_v.wav")
os.remove("ensembled/temp/3E4E_ensam1_v.wav")
os.remove("ensembled/temp/1E2E_ensam1_v.wav")
os.remove("ensembled/temp/difftemp_v.wav")
print('All Separations Saved!')
else:
print('Cleaning Up...')
dir = 'ensembled/temp'
for file in os.scandir(dir):
os.remove(file.path)
print('Complete!')
if __name__ == '__main__':
main()
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