mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-24 15:30:11 +01:00
1867 lines
94 KiB
Python
1867 lines
94 KiB
Python
import argparse
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import os, glob
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import cv2
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import librosa
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import numpy as np
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import soundfile as sf
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import torch
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import time
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from tqdm import tqdm
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from lib import dataset
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from lib import spec_utils
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from lib.model_param_init import ModelParameters
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class VocalRemover(object):
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def __init__(self, model, device, window_size):
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self.model = model
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self.offset = model.offset
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self.device = device
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self.window_size = window_size
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def _execute(self, X_mag_pad, roi_size, n_window, aggressiveness):
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self.model.eval()
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with torch.no_grad():
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preds = []
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for i in tqdm(range(n_window)):
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start = i * roi_size
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X_mag_window = X_mag_pad[None, :, :, start:start + self.window_size]
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X_mag_window = torch.from_numpy(X_mag_window).to(self.device)
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pred = self.model.predict(X_mag_window, aggressiveness)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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pred = np.concatenate(preds, axis=2)
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return pred
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def preprocess(self, X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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def inference(self, X_spec, aggressiveness):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
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pred = pred[:, :, :n_frame]
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return pred * coef, X_mag, np.exp(1.j * X_phase)
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def inference_tta(self, X_spec, aggressiveness):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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X_mag_pre = X_mag / coef
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n_frame = X_mag_pre.shape[2]
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pad_l, pad_r, roi_size = dataset.make_padding(n_frame, self.window_size, self.offset)
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n_window = int(np.ceil(n_frame / roi_size))
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X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
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pred = pred[:, :, :n_frame]
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pad_l += roi_size // 2
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pad_r += roi_size // 2
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n_window += 1
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X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
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pred_tta = self._execute(X_mag_pad, roi_size, n_window, aggressiveness)
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pred_tta = pred_tta[:, :, roi_size // 2:]
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pred_tta = pred_tta[:, :, :n_frame]
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return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
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def main():
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p = argparse.ArgumentParser()
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p.add_argument('--gpu', '-g', type=int, default=-1)
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p.add_argument('--pretrained_modelA', '-Am', type=str, default='models/MGM-v5-4Band-44100-BETA1.pth') ##DON'T CHNGE ITERATION-1
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p.add_argument('--pretrained_modelB', '-B', type=str, default='models/MGM-v5-4Band-44100-BETA2.pth') ##DON'T CHNGE ITERATION-2
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p.add_argument('--pretrained_modelC', '-C', type=str, default='models/HighPrecison_4band_1.pth') ##DON'T CHNGE ITERATION-3
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p.add_argument('--pretrained_modelD', '-Da', type=str, default='models/HighPrecison_4band_2.pth') ##DON'T CHNGE ITERATION-4
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p.add_argument('--pretrained_modelE', '-E', type=str, default='models/NewLayer_4band_1.pth') ##DON'T CHNGE ITERATION-5
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p.add_argument('--pretrained_modelF', '-F', type=str, default='models/NewLayer_4band_2.pth') ##DON'T CHNGE ITERATION-6
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p.add_argument('--pretrained_modelG', '-G', type=str, default='models/NewLayer_4band_3.pth') ##DON'T CHNGE ITERATION-7
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p.add_argument('--pretrained_modelH', '-Hm', type=str, default='models/MGM-v5-MIDSIDE-44100-BETA1.pth') ##DON'T CHNGE ITERATION-8
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p.add_argument('--pretrained_modelI', '-Im', type=str, default='models/MGM-v5-MIDSIDE-44100-BETA2.pth') ##DON'T CHNGE ITERATION-9
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p.add_argument('--pretrained_modelJ', '-J', type=str, default='models/MGM-v5-3Band-44100-BETA.pth') ##DON'T CHNGE ITERATION-10
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##p.add_argument('--pretrained_modelK', '-K', type=str, default='models/MGM-v5-2Band-32000-BETA1.pth') ##DON'T CHNGE ITERATION-11
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##p.add_argument('--pretrained_modelL', '-L', type=str, default='models/MGM-v5-2Band-32000-BETA2.pth') ##DON'T CHNGE ITERATION-12
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p.add_argument('--pretrained_modelM', '-Mm', type=str, default='models/LOFI_2band-1_33966KB.pth') ##DON'T CHNGE ITERATION-13
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p.add_argument('--pretrained_modelN', '-Nm', type=str, default='models/LOFI_2band-2_33966KB.pth') ##DON'T CHNGE ITERATION-14
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p.add_argument('--deepextraction', '-D', action='store_true')
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p.add_argument('--saveindivsep', '-s', action='store_true')
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p.add_argument('--input', '-i', required=True)
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p.add_argument('--nn_architecture', '-n', type=str, default='default') ##DON'T CHNGE ITERATION-1, ITERATION-2, ITERATION-8, ITERATION-9, ITERATION-10, ITERATION-11, ITERATION-12
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p.add_argument('--nn_architectureA', '-aB', type=str, default='123821KB') ##DON'T CHNGE ITERATION-3, ITERATION-4
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p.add_argument('--nn_architectureB', '-bA', type=str, default='129605KB') ##DON'T CHNGE ITERATION-5, ITERATION-6, ITERATION-7
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p.add_argument('--nn_architectureC', '-bC', type=str, default='33966KB') ##DON'T CHNGE ITERATION-13, ITERATION-14
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p.add_argument('--model_params', '-m', type=str, default='modelparams/4band_44100.json') ##DON'T CHNGE ITERATION-1, ITERATION-2, ITERATION-3, ITERATION-4, ITERATION-5, ITERATION-6, ITERATION-7
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p.add_argument('--model_paramsB', '-mB', type=str, default='modelparams/3band_44100_mid.json') ##DON'T CHNGE ITERATION-8, ITERATION-9
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p.add_argument('--model_paramsC', '-mC', type=str, default='modelparams/3band_44100.json') ##DON'T CHNGE ITERATION-10
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p.add_argument('--model_paramsD', '-mD', type=str, default='modelparams/2band_32000.json') ##DON'T CHNGE ITERATION-11, ITERATION-12
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p.add_argument('--model_paramsE', '-mE', type=str, default='modelparams/2band_44100_lofi.json') ##DON'T CHNGE ITERATION-13, ITERATION-14
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p.add_argument('--window_size', '-w', type=int, default=512)
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p.add_argument('--output_image', '-I', action='store_true')
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p.add_argument('--postprocess', '-p', action='store_true')
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p.add_argument('--tta', '-t', action='store_true')
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p.add_argument('--high_end_process', '-H', type=str, choices=['none', 'bypass', 'correlation'], default='none')
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p.add_argument('--aggressiveness', '-A', type=float, default=0.05)
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args = p.parse_args()
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####################################################-ITERATION-1-####################################################
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if args.nn_architecture == 'default':
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from lib import nets
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dir = 'ensembled/temp'
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for file in os.scandir(dir):
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os.remove(file.path)
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#if '' == args.model_params:
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# mp = ModelParameters(args.pretrained_model)
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#else:
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mp = ModelParameters(args.model_params)
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start_time = time.time()
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print('loading MGM-v5-4Band-44100-BETA1...', end=' ')
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device = torch.device('cpu')
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model = nets.CascadedASPPNet(mp.param['bins'] * 2)
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model.load_state_dict(torch.load(args.pretrained_modelA, map_location=device))
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if torch.cuda.is_available() and args.gpu >= 0:
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device = torch.device('cuda:{}'.format(args.gpu))
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model.to(device)
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print('done')
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print('loading & stft of wave source...', end=' ')
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
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basename = os.path.splitext(os.path.basename(args.input))[0]
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bands_n = len(mp.param['band'])
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for d in range(bands_n, 0, -1):
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bp = mp.param['band'][d]
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if d == bands_n: # high-end band
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X_wave[d], _ = librosa.load(
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args.input, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if X_wave[d].ndim == 1:
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X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
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else: # lower bands
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X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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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'])
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if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
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input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
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del X_wave, X_spec_s
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print('done')
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vr = VocalRemover(model, device, args.window_size)
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if args.tta:
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pred, X_mag, X_phase = vr.inference_tta(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
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else:
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pred, X_mag, X_phase = vr.inference(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
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if args.postprocess:
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print('post processing...', end=' ')
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pred_inv = np.clip(X_mag - pred, 0, np.inf)
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pred = spec_utils.mask_silence(pred, pred_inv)
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print('done')
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if 'is_vocal_model' in mp.param: # swap
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stems = {'inst': 'Vocals', 'vocals': 'Instruments'}
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else:
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stems = {'inst': 'Instruments', 'vocals': 'Vocals'}
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print('inverse stft of {}...'.format(stems['inst']), end=' ')
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y_spec_m = pred * X_phase
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v_spec_m = X_spec_m - y_spec_m
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if args.high_end_process == 'bypass':
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
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elif args.high_end_process == 'correlation':
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for i in range(input_high_end.shape[2]):
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for c in range(2):
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X_mag_max = np.amax(input_high_end[c, :, i])
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b1 = mp.param['pre_filter_start']-input_high_end_h//2
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b2 = mp.param['pre_filter_start']-1
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if X_mag_max > 0 and np.sum(np.abs(v_spec_m[c, b1:b2, i])) / (b2 - b1) > 0.07:
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y_mag = np.median(y_spec_m[c, b1:b2, i])
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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)))
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
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else:
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
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print('done')
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model_name = os.path.splitext(os.path.basename(args.pretrained_modelA))[0]
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sf.write(os.path.join('ensembled/temp', '1_{}_{}.wav'.format(model_name, stems['inst'])), wave, mp.param['sr'])
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if True:
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print('inverse stft of {}...'.format(stems['vocals']), end=' ')
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#v_spec_m = X_spec_m - y_spec_m
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wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
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print('done')
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sf.write(os.path.join('ensembled/temp', '1_{}_{}.wav'.format(model_name, stems['vocals'])), wave, mp.param['sr'])
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if args.output_image:
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with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f:
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image = spec_utils.spectrogram_to_image(y_spec_m)
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_, bin_image = cv2.imencode('.jpg', image)
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bin_image.tofile(f)
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with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f:
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image = spec_utils.spectrogram_to_image(v_spec_m)
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_, bin_image = cv2.imencode('.jpg', image)
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bin_image.tofile(f)
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print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
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####################################################-ITERATION-2-####################################################
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if args.nn_architecture == 'default':
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from lib import nets
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#if '' == args.model_params:
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# mp = ModelParameters(args.pretrained_model)
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#else:
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mp = ModelParameters(args.model_params)
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start_time = time.time()
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print('loading MGM-v5-4Band-44100-BETA2...', end=' ')
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device = torch.device('cpu')
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model = nets.CascadedASPPNet(mp.param['bins'] * 2)
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model.load_state_dict(torch.load(args.pretrained_modelB, map_location=device))
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if torch.cuda.is_available() and args.gpu >= 0:
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device = torch.device('cuda:{}'.format(args.gpu))
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model.to(device)
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print('done')
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print('loading & stft of wave source...', end=' ')
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
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basename = os.path.splitext(os.path.basename(args.input))[0]
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bands_n = len(mp.param['band'])
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for d in range(bands_n, 0, -1):
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bp = mp.param['band'][d]
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if d == bands_n: # high-end band
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X_wave[d], _ = librosa.load(
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args.input, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if X_wave[d].ndim == 1:
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X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
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else: # lower bands
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X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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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'])
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if d == bands_n and args.high_end_process in ['bypass', 'correlation']:
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input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
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input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
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del X_wave, X_spec_s
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print('done')
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vr = VocalRemover(model, device, args.window_size)
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if args.tta:
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pred, X_mag, X_phase = vr.inference_tta(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
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else:
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pred, X_mag, X_phase = vr.inference(X_spec_m, {'value': args.aggressiveness, 'split_bin': mp.param['band'][1]['crop_stop']})
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if args.postprocess:
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print('post processing...', end=' ')
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pred_inv = np.clip(X_mag - pred, 0, np.inf)
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pred = spec_utils.mask_silence(pred, pred_inv)
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print('done')
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if 'is_vocal_model' in mp.param: # swap
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stems = {'inst': 'Vocals', 'vocals': 'Instruments'}
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else:
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stems = {'inst': 'Instruments', 'vocals': 'Vocals'}
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print('inverse stft of {}...'.format(stems['inst']), end=' ')
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y_spec_m = pred * X_phase
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v_spec_m = X_spec_m - y_spec_m
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if args.high_end_process == 'bypass':
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
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elif args.high_end_process == 'correlation':
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for i in range(input_high_end.shape[2]):
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for c in range(2):
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X_mag_max = np.amax(input_high_end[c, :, i])
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b1 = mp.param['pre_filter_start']-input_high_end_h//2
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b2 = mp.param['pre_filter_start']-1
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if X_mag_max > 0 and np.sum(np.abs(v_spec_m[c, b1:b2, i])) / (b2 - b1) > 0.07:
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y_mag = np.median(y_spec_m[c, b1:b2, i])
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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)))
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end)
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else:
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wave = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
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print('done')
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model_name = os.path.splitext(os.path.basename(args.pretrained_modelB))[0]
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sf.write(os.path.join('ensembled/temp', '2_{}_{}.wav'.format(model_name, stems['inst'])), wave, mp.param['sr'])
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if True:
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print('inverse stft of {}...'.format(stems['vocals']), end=' ')
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#v_spec_m = X_spec_m - y_spec_m
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wave = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
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print('done')
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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))
|
|
|
|
####################################################-ITERATION-3-####################################################
|
|
|
|
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))
|
|
|
|
####################################################-ITERATION-4-####################################################
|
|
|
|
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))
|
|
|
|
####################################################-ITERATION-5-####################################################
|
|
|
|
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))
|
|
|
|
####################################################-ITERATION-6-####################################################
|
|
|
|
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))
|
|
|
|
####################################################-ITERATION-7-####################################################
|
|
|
|
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))
|
|
|
|
####################################################-ITERATION-8-####################################################
|
|
|
|
if args.nn_architecture == 'default':
|
|
from lib import nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
mp = ModelParameters(args.model_paramsB)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading MGM-v5-MIDSIDE-44100-BETA1...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_modelH, 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_modelH))[0]
|
|
sf.write(os.path.join('ensembled/temp', '8_{}_{}.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', '8_{}_{}.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))
|
|
|
|
####################################################-ITERATION-9-####################################################
|
|
|
|
if args.nn_architecture == 'default':
|
|
from lib import nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
mp = ModelParameters(args.model_paramsB)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading MGM-v5-MIDSIDE-44100-BETA2...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_modelI, 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_modelI))[0]
|
|
sf.write(os.path.join('ensembled/temp', '9_{}_{}.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', '9_{}_{}.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))
|
|
|
|
####################################################-ITERATION-10-####################################################
|
|
|
|
if args.nn_architecture == 'default':
|
|
from lib import nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
mp = ModelParameters(args.model_paramsC)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading MGM-v5-3Band-44100-BETA...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_modelJ, 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_modelJ))[0]
|
|
sf.write(os.path.join('ensembled/temp', '10_{}_{}.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', '10_{}_{}.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))
|
|
|
|
####################################################-ITERATION-11-####################################################
|
|
|
|
## if args.nn_architecture == 'default':
|
|
## from lib import nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
## mp = ModelParameters(args.model_paramsD)
|
|
|
|
## start_time = time.time()
|
|
|
|
## print('loading MGM-v5-2Band-32000-BETA1...', end=' ')
|
|
|
|
## device = torch.device('cpu')
|
|
## model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
## model.load_state_dict(torch.load(args.pretrained_modelK, 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_modelK))[0]
|
|
## sf.write(os.path.join('ensembled/temp', '11_{}_{}.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', '11_{}_{}.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))
|
|
|
|
####################################################-ITERATION-12-####################################################
|
|
|
|
## if args.nn_architecture == 'default':
|
|
## from lib import nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
## mp = ModelParameters(args.model_paramsD)
|
|
|
|
## start_time = time.time()
|
|
|
|
## print('loading MGM-v5-2Band-32000-BETA...', end=' ')
|
|
|
|
## device = torch.device('cpu')
|
|
## model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
## model.load_state_dict(torch.load(args.pretrained_modelK, 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_modelK))[0]
|
|
## sf.write(os.path.join('ensembled/temp', '11_{}_{}.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', '11_{}_{}.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))
|
|
|
|
####################################################-ITERATION-13-####################################################
|
|
|
|
if args.nn_architectureC == '33966KB':
|
|
from lib import nets_33966KB as nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
mp = ModelParameters(args.model_paramsE)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading LOFI_2band-1_33966KB...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_modelM, 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_modelM))[0]
|
|
sf.write(os.path.join('ensembled/temp', '13_{}_{}.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', '13_{}_{}.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))
|
|
|
|
####################################################-ITERATION-14-####################################################
|
|
|
|
if args.nn_architectureC == '33966KB':
|
|
from lib import nets_33966KB as nets
|
|
|
|
|
|
#if '' == args.model_params:
|
|
# mp = ModelParameters(args.pretrained_model)
|
|
#else:
|
|
mp = ModelParameters(args.model_paramsE)
|
|
|
|
start_time = time.time()
|
|
|
|
print('loading LOFI_2band-2_33966KB...', end=' ')
|
|
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(args.pretrained_modelN, 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_modelN))[0]
|
|
sf.write(os.path.join('ensembled/temp', '14_{}_{}.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', '14_{}_{}.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))
|
|
|
|
####################################################ITERATIONS-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/1STHALF")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Instruments.wav ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Instruments.wav -o ensembled/temp/8E9E_ensam1")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/10_MGM-v5-3Band-44100-BETA_Instruments.wav ensembled/temp/13_LOFI_2band-1_33966KB_Instruments.wav -o ensembled/temp/10E13E_ensam3")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E_ensam1_v.wav ensembled/temp/10E13E_ensam3_v.wav -o ensembled/temp/8E9E10E13E_ensam4")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E10E13E_ensam4_v.wav ensembled/temp/14_LOFI_2band-2_33966KB_Instruments.wav -o ensembled/temp/2NDHALF")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1STHALF_v.wav ensembled/temp/2NDHALF_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/1STHALFV")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Vocals.wav ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Vocals.wav -o ensembled/temp/8E9EV_ensam1")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/10_MGM-v5-3Band-44100-BETA_Vocals.wav ensembled/temp/13_LOFI_2band-1_33966KB_Vocals.wav -o ensembled/temp/10E13EV_ensam3")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9EV_ensam1_v.wav ensembled/temp/10E13EV_ensam3_v.wav -o ensembled/temp/8E9E10E13EV_ensam4")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E10E13EV_ensam4_v.wav ensembled/temp/14_LOFI_2band-2_33966KB_Vocals.wav -o ensembled/temp/2NDHALFV")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1STHALFV_v.wav ensembled/temp/2NDHALFV_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/{}_ALLMODELS_Ensembled_DeepExtraction_Instrumental.wav'.format(basename))
|
|
os.rename('ensembled/temp/Complete_v.wav', 'ensembled/{}_ALLMODELS_Ensembled_Instrumental.wav'.format(basename))
|
|
os.rename('ensembled/temp/CompleteV_v.wav', 'ensembled/{}_ALLMODELS_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/1STHALF")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Instruments.wav ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Instruments.wav -o ensembled/temp/8E9E_ensam1")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/10_MGM-v5-3Band-44100-BETA_Instruments.wav ensembled/temp/13_LOFI_2band-1_33966KB_Instruments.wav -o ensembled/temp/10E13E_ensam3")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E_ensam1_v.wav ensembled/temp/10E13E_ensam3_v.wav -o ensembled/temp/8E9E10E13E_ensam4")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E10E13E_ensam4_v.wav ensembled/temp/14_LOFI_2band-2_33966KB_Instruments.wav -o ensembled/temp/2NDHALF")
|
|
os.system("python lib/spec_utils.py -a min_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1STHALF_v.wav ensembled/temp/2NDHALF_v.wav -o ensembled/temp/Complete")
|
|
os.rename('ensembled/temp/Complete_v.wav', 'ensembled/{}_ALLMODELS_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/1STHALFV")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Vocals.wav ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Vocals.wav -o ensembled/temp/8E9EV_ensam1")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/10_MGM-v5-3Band-44100-BETA_Vocals.wav ensembled/temp/13_LOFI_2band-1_33966KB_Vocals.wav -o ensembled/temp/10E13EV_ensam3")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9EV_ensam1_v.wav ensembled/temp/10E13EV_ensam3_v.wav -o ensembled/temp/8E9E10E13EV_ensam4")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/8E9E10E13EV_ensam4_v.wav ensembled/temp/14_LOFI_2band-2_33966KB_Vocals.wav -o ensembled/temp/2NDHALFV")
|
|
os.system("python lib/spec_utils.py -a max_mag -m modelparams/1band_sr44100_hl512.json ensembled/temp/1STHALFV_v.wav ensembled/temp/2NDHALFV_v.wav -o ensembled/temp/CompleteV")
|
|
os.rename('ensembled/temp/CompleteV_v.wav', 'ensembled/{}_ALLMODELS_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.rename('ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Instruments.wav', 'separated/{}_MGM-v5-MIDSIDE-44100-BETA1_Instruments.wav'.format(basename))
|
|
os.rename('ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Instruments.wav', 'separated/{}_MGM-v5-MIDSIDE-44100-BETA2_Instruments.wav'.format(basename))
|
|
os.rename('ensembled/temp/10_MGM-v5-3Band-44100-BETA_Instruments.wav', 'separated/{}_MGM-v5-3Band-44100-BETA_Instruments.wav'.format(basename))
|
|
os.rename('ensembled/temp/13_LOFI_2band-1_33966KB_Instruments.wav', 'separated/{}_LOFI_2band-_33966KB_Instruments.wav'.format(basename))
|
|
os.rename('ensembled/temp/14_LOFI_2band-2_33966KB_Instruments.wav', 'separated/{}_LOFI_2band-2_33966KB_Instruments.wav'.format(basename))
|
|
os.rename('ensembled/temp/8_MGM-v5-MIDSIDE-44100-BETA1_Vocals.wav', 'separated/{}_MGM-v5-MIDSIDE-44100-BETA1_Vocals.wav'.format(basename))
|
|
os.rename('ensembled/temp/9_MGM-v5-MIDSIDE-44100-BETA2_Vocals.wav', 'separated/{}_MGM-v5-MIDSIDE-44100-BETA2_Vocals.wav'.format(basename))
|
|
os.rename('ensembled/temp/10_MGM-v5-3Band-44100-BETA_Vocals.wav', 'separated/{}_MGM-v5-3Band-44100-BETA_Vocals.wav'.format(basename))
|
|
os.rename('ensembled/temp/13_LOFI_2band-1_33966KB_Vocals.wav', 'separated/{}_LOFI_2band-1_33966KB_Vocals.wav'.format(basename))
|
|
os.rename('ensembled/temp/14_LOFI_2band-2_33966KB_Vocals.wav', 'separated/{}_LOFI_2band-2_33966KB_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/1STHALF_v.wav")
|
|
os.remove("ensembled/temp/1STHALFV_v.wav")
|
|
os.remove("ensembled/temp/2NDHALF_v.wav")
|
|
os.remove("ensembled/temp/2NDHALFV_v.wav")
|
|
os.remove("ensembled/temp/8E9E_ensam1_v.wav")
|
|
os.remove("ensembled/temp/8E9E10E13E_ensam4_v.wav")
|
|
os.remove("ensembled/temp/8E9E10E13EV_ensam4_v.wav")
|
|
os.remove("ensembled/temp/8E9EV_ensam1_v.wav")
|
|
os.remove("ensembled/temp/10E13E_ensam3_v.wav")
|
|
os.remove("ensembled/temp/10E13EV_ensam3_v.wav")
|
|
print('Complete!')
|
|
else:
|
|
print('Cleaning Up...')
|
|
dir = 'ensembled/temp'
|
|
for file in os.scandir(dir):
|
|
os.remove(file.path)
|
|
print('Complete!')
|
|
|
|
if __name__ == '__main__':
|
|
main()
|