mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 01:10:56 +01:00
278 lines
12 KiB
Python
278 lines
12 KiB
Python
import argparse
|
|
import os
|
|
import importlib
|
|
|
|
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_model', '-P', type=str, default='models/baseline.pth')
|
|
p.add_argument('--input', '-i', required=True)
|
|
p.add_argument('--nn_architecture', '-n', type=str, choices=['default', '33966KB', '123821KB', '129605KB'], default='default')
|
|
p.add_argument('--model_params', '-m', type=str, default='')
|
|
p.add_argument('--window_size', '-w', type=int, default=512)
|
|
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('--is_vocal_model', '-vm', action='store_true')
|
|
p.add_argument('--tta', '-t', action='store_true')
|
|
p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation', 'mirroring', 'mirroring2'], default='none')
|
|
p.add_argument('--aggressiveness', '-A', type=float, default=0.07)
|
|
args = p.parse_args()
|
|
|
|
nets = importlib.import_module('lib.nets' + f'_{args.nn_architecture}'.replace('_default', ''), package=None)
|
|
|
|
dir = 'ensembled/temp'
|
|
for file in os.scandir(dir):
|
|
os.remove(file.path)
|
|
|
|
mp = ModelParameters(args.model_params)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading model...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_model, 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 != '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:
|
|
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: # 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':
|
|
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)
|
|
|
|
if args.deepextraction:
|
|
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'])
|
|
sf.write(os.path.join('ensembled/temp', 'tempI.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'])
|
|
sf.write(os.path.join('ensembled/temp', 'tempV.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)
|
|
|
|
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/spec_utils.py -a invert -v -m modelparams/1band_sr44100_hl512.json ensembled/temp/tempI.wav ensembled/temp/difftemp_v.wav -o ensembled/temp/difftemp")
|
|
os.rename('ensembled/temp/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=' ')
|
|
|
|
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('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))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|
|
|