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https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2025-02-17 19:19:15 +01:00
Update spec_utils.py
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aufr33
GitHub
parent
a91b85cb8d
commit
3e889fd818
@ -97,7 +97,7 @@ def combine_spectrograms(specs, mp):
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g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
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gp = g
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spec_c[:, b, :] *= g
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return np.asfortranarray(spec_c)
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@ -174,7 +174,7 @@ def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
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return mag
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def align_wave_head_and_tail(a, b, sr=0):
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def align_wave_head_and_tail(a, b):
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l = min([a[0].size, b[0].size])
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return a[:l,:l], b[:l,:l]
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@ -185,8 +185,6 @@ def cache_or_load(mix_path, inst_path, mp):
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inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
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cache_dir = 'mph{}'.format(hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode('utf-8')).hexdigest())
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#mix_cache_dir = os.path.join(os.path.dirname(mix_path), cache_dir)
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#inst_cache_dir = os.path.join(os.path.dirname(inst_path), cache_dir)
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mix_cache_dir = os.path.join('cache', cache_dir)
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inst_cache_dir = os.path.join('cache', cache_dir)
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@ -277,21 +275,6 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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wave_band = {}
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bands_n = len(mp.param['band'])
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offset = 0
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#if False:
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# from scipy import ndimage
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#
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# intersect_h2 = 167
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# intersect_y2 = 67
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# intersect_h1 = 62
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# intersect_y1 = 244
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# intersect_left = ndimage.zoom(spec_m[0, intersect_y1:intersect_y1+intersect_h1, :].real, zoom=(intersect_h2 / intersect_h1, 1.0), order=3) * 6
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# intersect_right = ndimage.zoom(spec_m[1, intersect_y1:intersect_y1+intersect_h1, :].real, zoom=(intersect_h2 / intersect_h1, 1.0), order=3) * 6
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# s = intersect_y2+intersect_left.shape[0]
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# spec_m[0, intersect_y2:s, :] = np.where(np.abs(spec_m[0, intersect_y2:s, :]) <= np.abs(intersect_left) * 1.5, spec_m[0, intersect_y2:s, :], intersect_left)
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# spec_m[1, intersect_y2:s, :] = np.where(np.abs(spec_m[1, intersect_y2:s, :]) <= np.abs(intersect_right) * 1.5, spec_m[1, intersect_y2:s, :], intersect_right)
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for d in range(1, bands_n + 1):
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bp = mp.param['band'][d]
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@ -307,18 +290,18 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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if bp['hpf_start'] > 0:
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spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
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if bands_n == 1:
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wave = spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse'])
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wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse'])
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else:
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wave = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
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wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
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else:
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sr = mp.param['band'][d+1]['sr']
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if d == 1: # lower
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spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
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wave = librosa.resample(spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']), bp['sr'], sr, res_type="sinc_fastest")
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wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']), bp['sr'], sr, res_type="sinc_fastest")
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else: # mid
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spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
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spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
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wave2 = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
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wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
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wave = librosa.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
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return wave.T
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@ -344,8 +327,8 @@ def fft_hp_filter(spec, bin_start, bin_stop):
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spec[:, 0:bin_stop+1, :] *= 0
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return spec
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def mirroring(a, spec_m, input_high_end, mp):
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if 'mirroring' == a:
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mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
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@ -360,6 +343,23 @@ def mirroring(a, spec_m, input_high_end, mp):
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return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
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def ensembling(a, specs):
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for i in range(1, len(specs)):
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if i == 1:
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spec = specs[i-1]
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ln = min([spec.shape[2], specs[i].shape[2]])
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spec = spec[:,:,:ln]
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specs[i] = specs[i][:,:,:ln]
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if 'min_mag' == a:
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spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
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if 'max_mag' == a:
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spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
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return spec
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if __name__ == "__main__":
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import cv2
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import sys
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@ -368,76 +368,66 @@ if __name__ == "__main__":
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from model_param_init import ModelParameters
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p = argparse.ArgumentParser()
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p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'invertB', 'min_mag', 'max_mag'], default='invert') #'invertB' only writes output "v".
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p.add_argument('--model_params', '-m', type=str, default='4band_44100.json')
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p.add_argument('--output_name', '-o', type=str, default='test')
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p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'min_mag', 'max_mag'], default='min_mag')
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p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json'))
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p.add_argument('--output_name', '-o', type=str, default='output')
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p.add_argument('--vocals_only', '-v', action='store_true')
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p.add_argument('input', nargs='+')
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args = p.parse_args()
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start_time = time.time()
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
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if args.algorithm == 'invert' and len(args.input) != 2:
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raise ValueError('There should be two input files.')
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if args.algorithm != 'invert' and len(args.input) < 2:
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raise ValueError('There must be at least two input files.')
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wave, specs = {}, {}
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mp = ModelParameters(args.model_params)
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for d in range(len(mp.param['band']), 0, -1):
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print('Band(s) {}'.format(d), end=' ')
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bp = mp.param['band'][d]
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for i in range(len(args.input)):
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for d in range(len(mp.param['band']), 0, -1):
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bp = mp.param['band'][d]
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spec = {}
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if d == len(mp.param['band']): # high-end band
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wave[d], _ = librosa.load(
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args.input[i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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if d == len(mp.param['band']): # high-end band
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X_wave[d], _ = librosa.load(
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args.input[0], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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y_wave[d], _ = librosa.load(
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args.input[1], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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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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y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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if len(wave[d].shape) == 1: # mono to stereo
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wave[d] = np.array([wave[d], wave[d]])
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else: # lower bands
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wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
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spec[d] = wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
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specs[i] = combine_spectrograms(spec, mp)
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X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
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X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
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y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
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print('ok')
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del X_wave, y_wave
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X_spec_m = combine_spectrograms(X_spec_s, mp)
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y_spec_m = combine_spectrograms(y_spec_s, mp)
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if y_spec_m.shape != X_spec_m.shape:
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print('Warning: The combined spectrograms are different!')
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print('X_spec_m: ' + str(X_spec_m.shape))
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print('y_spec_m: ' + str(y_spec_m.shape))
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del wave
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if args.algorithm == 'invert':
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y_spec_m = reduce_vocal_aggressively(X_spec_m, y_spec_m, 0.2)
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v_spec_m = X_spec_m - y_spec_m
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if args.algorithm == 'invertB':
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y_spec_m = reduce_vocal_aggressively(X_spec_m, y_spec_m, 0.2)
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v_spec_m = X_spec_m - y_spec_m
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if args.algorithm == 'min_mag':
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v_spec_m = np.where(np.abs(y_spec_m) <= np.abs(X_spec_m), y_spec_m, X_spec_m)
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if args.algorithm == 'max_mag':
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v_spec_m = np.where(np.abs(y_spec_m) >= np.abs(X_spec_m), y_spec_m, X_spec_m)
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specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
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v_spec = specs[0] - specs[1]
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X_mag = np.abs(X_spec_m)
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y_mag = np.abs(y_spec_m)
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v_mag = np.abs(v_spec_m)
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if not args.vocals_only:
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X_mag = np.abs(specs[0])
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y_mag = np.abs(specs[1])
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v_mag = np.abs(v_spec)
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X_image = spectrogram_to_image(X_mag)
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y_image = spectrogram_to_image(y_mag)
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v_image = spectrogram_to_image(v_mag)
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if args.algorithm == 'invert':
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cv2.imwrite('{}_X.png'.format(args.output_name), X_image)
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cv2.imwrite('{}_y.png'.format(args.output_name), y_image)
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cv2.imwrite('{}_v.png'.format(args.output_name), v_image)
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sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(X_spec_m, mp), mp.param['sr'])
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sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(y_spec_m, mp), mp.param['sr'])
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sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec_m, mp), mp.param['sr'])
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X_image = spectrogram_to_image(X_mag)
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y_image = spectrogram_to_image(y_mag)
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v_image = spectrogram_to_image(v_mag)
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cv2.imwrite('{}_X.png'.format(args.output_name), X_image)
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cv2.imwrite('{}_y.png'.format(args.output_name), y_image)
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cv2.imwrite('{}_v.png'.format(args.output_name), v_image)
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sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[0], mp), mp.param['sr'])
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sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[1], mp), mp.param['sr'])
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sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])
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else:
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sf.write(os.path.join('ensembled','{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp), mp.param['sr'])
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#print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
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