mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
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Update inference.py
This commit is contained in:
aufr33
GitHub
parent
33b7900ae2
commit
d9468a1edd
290
inference.py
290
inference.py
@ -8,6 +8,7 @@ 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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import math
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from tqdm import tqdm
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from lib import dataset
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@ -23,7 +24,7 @@ class VocalRemover(object):
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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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def _execute(self, X_mag_pad, roi_size, n_window, params):
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self.model.eval()
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with torch.no_grad():
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preds = []
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@ -32,7 +33,7 @@ class VocalRemover(object):
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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 = self.model.predict(X_mag_window, params)
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pred = pred.detach().cpu().numpy()
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preds.append(pred[0])
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@ -47,7 +48,7 @@ class VocalRemover(object):
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return X_mag, X_phase
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def inference(self, X_spec, aggressiveness):
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def inference(self, X_spec, params):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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@ -59,12 +60,12 @@ class VocalRemover(object):
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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 = self._execute(X_mag_pad, roi_size, n_window, params)
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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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def inference_tta(self, X_spec, params):
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X_mag, X_phase = self.preprocess(X_spec)
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coef = X_mag.max()
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@ -76,7 +77,7 @@ class VocalRemover(object):
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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 = self._execute(X_mag_pad, roi_size, n_window, params)
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pred = pred[:, :, :n_frame]
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pad_l += roi_size // 2
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@ -85,39 +86,51 @@ class VocalRemover(object):
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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 = self._execute(X_mag_pad, roi_size, n_window, params)
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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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nn_arch_sizes = [
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31191, # default
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33966, 123821, 537238 # custom
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]
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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_model', '-P', type=str, default='models/baseline.pth')
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p.add_argument('--input', '-i', required=True)
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p.add_argument('--nn_architecture', '-n', type=str, choices=['default', '33966KB', '123821KB', '129605KB', '537238KB'], default='default')
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p.add_argument('--nn_architecture', '-n', type=str, choices= ['auto'] + list('{}KB'.format(s) for s in nn_arch_sizes), default='auto')
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p.add_argument('--model_params', '-m', type=str, default='')
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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('--deepextraction', '-D', action='store_true')
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p.add_argument('--postprocess', '-p', action='store_true')
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p.add_argument('--is_vocal_model', '-vm', 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', 'mirroring', 'mirroring2'], default='mirroring')
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p.add_argument('--aggressiveness', '-A', type=float, default=0.07)
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p.add_argument('--no_vocals', '-nv', action='store_true')
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p.add_argument('--tta', '-t', action='store_true', help='Test-Time-Augmentation')
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p.add_argument('--high_end_process', '-H', type=str, choices=['mirroring', 'mirroring2', 'bypass', 'none'], default='mirroring')
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p.add_argument('--aggressiveness', '-A', type=float, default=0.07, help='The strength of the vocal isolation. From 0.0 to 1.0.')
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p.add_argument('--no_vocals', '-nv', action='store_true', help='Don\'t create Vocals stem.')
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p.add_argument('--chunks', '-c', type=int, default=1, help='Split the input file into chunks to reduce RAM consumption.')
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p.add_argument('--model_test_mode', '-mt', action='store_true', help='Include the model name in the output file name.')
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p.add_argument('--normalize', action='store_true')
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args = p.parse_args()
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nets = importlib.import_module('lib.nets' + f'_{args.nn_architecture}'.replace('_default', ''), package=None)
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dir = 'ensembled/temp'
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for file in os.scandir(dir):
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separated_dir = 'separated'
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ensembled_dir = 'ensembled/temp'
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for file in os.scandir(ensembled_dir):
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os.remove(file.path)
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mp = ModelParameters(args.model_params)
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if 'auto' == args.nn_architecture:
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model_size = math.ceil(os.stat(args.pretrained_model).st_size / 1024)
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args.nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size)))
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nets = importlib.import_module('lib.nets' + f'_{args.nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None)
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mp = ModelParameters(args.model_params)
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start_time = time.time()
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print('loading model...', end=' ')
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@ -133,105 +146,177 @@ def main():
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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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X_spec = {}
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input_is_mono = False
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basename = os.path.splitext(os.path.basename(args.input))[0]
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basenameb = '"{}"'.format(os.path.splitext(os.path.basename(args.input))[0])
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bands_n = len(mp.param['band'])
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# high-end band
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bp = mp.param['band'][bands_n]
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wave, _ = librosa.load(args.input, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if wave.ndim == 1:
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input_is_mono = True
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wave = np.asarray([wave, wave])
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if args.normalize:
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wave /= max(np.max(wave), abs(np.min(wave)))
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X_spec[bands_n] = spec_utils.wave_to_spectrogram(wave, bp['hl'], bp['n_fft'], mp, True)
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X_spec[bands_n] = spec_utils.convert_channels(X_spec[bands_n], mp, bands_n)
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if np.max(wave[0]) == 0.0:
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print('Empty audio file!')
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raise ValueError('Empty audio file')
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if args.high_end_process != 'none':
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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[bands_n][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
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for d in range(bands_n, 0, -1):
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# lower bands
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for d in range(bands_n - 1, 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(X_wave[d], bp['hl'], bp['n_fft'], mp, True)
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if d == bands_n and args.high_end_process != 'none':
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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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wave = librosa.resample(wave, mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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X_spec[d] = spec_utils.wave_to_spectrogram(wave, bp['hl'], bp['n_fft'], mp, True)
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X_spec[d] = spec_utils.convert_channels(X_spec[d], mp, d)
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del X_wave, X_spec_s
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X_spec = spec_utils.combine_spectrograms(X_spec, mp)
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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 or args.is_vocal_model: # 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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print('Deprecated: correlation will be removed in the final release. Please use the mirroring instead.')
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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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elif args.high_end_process.startswith('mirroring'):
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input_high_end_ = spec_utils.mirroring(args.high_end_process, y_spec_m, input_high_end, mp)
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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_model))[0]
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sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['inst'])), wave, mp.param['sr'])
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chunk_pfx = ''
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chunk_size = X_spec.shape[2] // args.chunks
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chunks_filelist = {'vocals': {}, 'inst': {}}
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for chunk in range(0, args.chunks):
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chunk_margin_r = 0
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if chunk == 0:
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chunk_offset_m, chunk_offset, chunk_margin = 0, 0, 0
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else:
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chunk_margin = chunk_size // 100 - 1
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chunk_offset_m = chunk * chunk_size - chunk_margin - 1
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chunk_offset = chunk * chunk_size - 1
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if args.chunks > 1:
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chunk_pfx = f'_chunk{chunk}'
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print(f'Chunk {chunk}')
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if chunk < args.chunks - 1:
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chunk_margin_r = chunk_size // 100 - 1
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pd = {
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'aggr_value': args.aggressiveness,
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'aggr_split_bin': mp.param['band'][1]['crop_stop'],
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'aggr_correction': mp.param.get('aggr_correction'),
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'is_vocal_model': args.is_vocal_model
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}
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if not args.no_vocals:
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print('inverse stft of {}...'.format(stems['vocals']), end=' ')
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if args.tta:
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pred, X_mag, X_phase = vr.inference_tta(X_spec[:, :, chunk_offset_m:(chunk+1)*chunk_size+chunk_margin_r], pd)
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else:
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pred, X_mag, X_phase = vr.inference(X_spec[:, :, chunk_offset_m:(chunk+1)*chunk_size+chunk_margin_r], pd)
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if args.high_end_process.startswith('mirroring'):
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input_high_end_ = spec_utils.mirroring(args.high_end_process, v_spec_m, input_high_end, mp)
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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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wave = spec_utils.cmb_spectrogram_to_wave(v_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(v_spec_m, mp)
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stems = {'inst': 'Instruments', 'vocals': 'Vocals'}
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basename_enc = basename
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print('inverse stft of {}...'.format(stems['inst']), end=' ')
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y_spec_m = (pred * X_phase)[:, :, chunk_margin:pred.shape[2]-chunk_margin_r]
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if args.chunks > 1:
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import hashlib
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basename_enc = hashlib.sha1(basename.encode('utf-8')).hexdigest()
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if chunk > 0: # smoothing
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y_spec_m[:, :, 0] = 0.5 * (y_spec_m[:, :, 0] + prev_chunk_edge)
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prev_chunk_edge = y_spec_m[:, :, -1]
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ffmpeg_tmp_fn = '{}_{}_inst'.format(basename_enc, time.time())
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if args.high_end_process == 'bypass':
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wave = spec_utils.cmb_spectrogram_to_wave_ffmpeg(y_spec_m, mp, ffmpeg_tmp_fn, input_high_end_h, input_high_end)
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elif args.high_end_process.startswith('mirroring'):
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input_high_end_ = spec_utils.mirroring(args.high_end_process, y_spec_m, input_high_end[:, :, chunk_offset:(chunk+1)*chunk_size], mp)
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wave = spec_utils.cmb_spectrogram_to_wave_ffmpeg(y_spec_m, mp, ffmpeg_tmp_fn, input_high_end_h, input_high_end_)
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else:
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wave = spec_utils.cmb_spectrogram_to_wave_ffmpeg(y_spec_m, mp, ffmpeg_tmp_fn)
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print('done')
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sf.write(os.path.join('separated', '{}_{}_{}.wav'.format(basename, model_name, stems['vocals'])), wave, mp.param['sr'])
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model_name = ''
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if args.model_test_mode:
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model_name = '_' + os.path.splitext(os.path.basename(args.pretrained_model))[0]
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if input_is_mono:
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wave = wave.mean(axis=1, keepdims=True)
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fn = os.path.join(separated_dir, '{}{}_{}{}.wav'.format(basename_enc, model_name, stems['inst'], chunk_pfx))
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sf.write(fn, wave, mp.param['sr'])
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chunks_filelist['inst'][chunk] = fn
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if not args.no_vocals:
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print('inverse stft of {}...'.format(stems['vocals']), end=' ')
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ffmpeg_tmp_fn = '{}_{}_vocals'.format(basename_enc, time.time())
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v_spec_m = X_spec[:, :, chunk_offset:(chunk+1)*chunk_size] - y_spec_m
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if args.high_end_process.startswith('mirroring'):
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input_high_end_ = spec_utils.mirroring(args.high_end_process, v_spec_m, input_high_end[:, :, chunk_offset:(chunk+1)*chunk_size], mp)
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wave = spec_utils.cmb_spectrogram_to_wave_ffmpeg(v_spec_m, mp, ffmpeg_tmp_fn, input_high_end_h, input_high_end_)
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else:
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wave = spec_utils.cmb_spectrogram_to_wave_ffmpeg(v_spec_m, mp, ffmpeg_tmp_fn)
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print('done')
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if input_is_mono:
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wave = wave.mean(axis=1, keepdims=True)
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fn = os.path.join(separated_dir, '{}{}_{}{}.wav'.format(basename_enc, model_name, stems['vocals'], chunk_pfx))
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sf.write(fn, wave, mp.param['sr'])
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chunks_filelist['vocals'][chunk] = fn
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for stem in stems:
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if len(chunks_filelist[stem]) > 0 and args.chunks > 1:
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import subprocess
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fn = os.path.join(separated_dir, '{}{}_{}.wav'.format(basename_enc, model_name, stems[stem]))
|
||||
fn2 = os.path.join(separated_dir, '{}{}_{}.wav'.format(basename, model_name, stems[stem]))
|
||||
#os.system('sox "' + '" "'.join([f for f in chunks_filelist[stem].values()]) + f'" "{fn}"')
|
||||
subprocess.run(['sox'] + [f for f in chunks_filelist[stem].values()] + [fn])
|
||||
|
||||
if not os.path.isfile(fn):
|
||||
print('Error: failed to create output file. Make sure that you have installed sox.')
|
||||
|
||||
os.rename(fn, fn2)
|
||||
|
||||
for rf in chunks_filelist[stem].values():
|
||||
os.remove(rf)
|
||||
|
||||
if args.output_image:
|
||||
with open('{}_{}.jpg'.format(basename, stems['inst']), mode='wb') as f:
|
||||
image = spec_utils.spectrogram_to_image(y_spec_m)
|
||||
_, bin_image = cv2.imencode('.jpg', image)
|
||||
bin_image.tofile(f)
|
||||
with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f:
|
||||
image = spec_utils.spectrogram_to_image(v_spec_m)
|
||||
_, bin_image = cv2.imencode('.jpg', image)
|
||||
bin_image.tofile(f)
|
||||
|
||||
if not args.no_vocals:
|
||||
with open('{}_{}.jpg'.format(basename, stems['vocals']), mode='wb') as f:
|
||||
image = spec_utils.spectrogram_to_image(v_spec_m)
|
||||
_, bin_image = cv2.imencode('.jpg', image)
|
||||
bin_image.tofile(f)
|
||||
|
||||
if args.deepextraction:
|
||||
|
||||
@ -239,17 +324,16 @@ def main():
|
||||
{
|
||||
'algorithm':'deep',
|
||||
'model_params':'modelparams/1band_sr44100_hl512.json',
|
||||
'file1':"separated/{}_{}_{}.wav".format(basenameb, model_name, stems['vocals'], mp.param['sr']),
|
||||
'file2':"separated/{}_{}_{}.wav".format(basenameb, model_name, stems['inst'], mp.param['sr']),
|
||||
'output':'separated/{}_{}_{}_Deep_Extraction'.format(basenameb, model_name, stems['inst'], mp.param['sr'])
|
||||
'file1':"{}/{}{}_{}.wav".format(separated_dir, basenameb, model_name, stems['vocals'], mp.param['sr']),
|
||||
'file2':"{}/{}{}_{}.wav".format(separated_dir, basenameb, model_name, stems['inst'], mp.param['sr']),
|
||||
'output':'{}/{}{}_{}_Deep_Extraction'.format(separated_dir, basenameb, model_name, stems['inst'], mp.param['sr'])
|
||||
}
|
||||
]
|
||||
|
||||
for i,e in tqdm(enumerate(deepext), desc="Performing Deep Extraction..."):
|
||||
os.system(f"python lib/spec_utils.py -a {e['algorithm']} -m {e['model_params']} {e['file1']} {e['file2']} -o {e['output']}")
|
||||
|
||||
dir = 'ensembled/temp'
|
||||
for file in os.scandir(dir):
|
||||
for file in os.scandir(ensembled_dir):
|
||||
os.remove(file.path)
|
||||
print('Complete!')
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user