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Update spec_utils.py

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Added reverse sound effect support
Added merging of outputs.
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aufr33 2021-04-28 06:35:16 +03:00 committed by GitHub
parent b7b3d0263e
commit 3864f2ef71
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1 changed files with 72 additions and 32 deletions
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104
lib/spec_utils.py
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@ -26,8 +26,11 @@ def crop_center(h1, h2):
return h1
def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False):
if mid_side:
def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, reverse=False):
if reverse:
wave_left = np.flip(np.asfortranarray(wave[0]))
wave_right = np.flip(np.asfortranarray(wave[1]))
elif mid_side:
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
else:
@ -42,10 +45,13 @@ def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False):
return spec
def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False):
def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, reverse=False):
import threading
if mid_side:
if reverse:
wave_left = np.flip(np.asfortranarray(wave[0]))
wave_right = np.flip(np.asfortranarray(wave[1]))
elif mid_side:
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
else:
@ -198,7 +204,7 @@ def cache_or_load(mix_path, inst_path, mp):
for d in range(len(mp.param['band']), 0, -1):
bp = mp.param['band'][d]
if d == len(mp.param['band']): # high band
if d == len(mp.param['band']): # high-end band
X_wave[d], _ = librosa.load(
mix_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
y_wave[d], _ = librosa.load(
@ -209,8 +215,8 @@ def cache_or_load(mix_path, inst_path, mp):
X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'])
y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'])
X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
del X_wave, y_wave
@ -228,20 +234,22 @@ def cache_or_load(mix_path, inst_path, mp):
return X_spec_m, y_spec_m
def spectrogram_to_wave(spec, hop_length, mid_side):
def spectrogram_to_wave(spec, hop_length, mid_side, reverse):
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
wave_left = librosa.istft(spec_left, hop_length=hop_length)
wave_right = librosa.istft(spec_right, hop_length=hop_length)
if mid_side:
if reverse:
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
elif mid_side:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
else:
return np.asfortranarray([wave_left, wave_right])
def spectrogram_to_wave_mt(spec, hop_length, mid_side):
def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse):
import threading
spec_left = np.asfortranarray(spec[0])
@ -256,7 +264,9 @@ def spectrogram_to_wave_mt(spec, hop_length, mid_side):
wave_right = librosa.istft(spec_right, hop_length=hop_length)
thread.join()
if mid_side:
if reverse:
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
elif mid_side:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
else:
return np.asfortranarray([wave_left, wave_right])
@ -266,6 +276,21 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
wave_band = {}
bands_n = len(mp.param['band'])
offset = 0
if False:
from scipy import ndimage
intersect_h2 = 167
intersect_y2 = 67
intersect_h1 = 62
intersect_y1 = 244
intersect_left = ndimage.zoom(spec_m[0, intersect_y1:intersect_y1+intersect_h1, :].real, zoom=(intersect_h2 / intersect_h1, 1.0), order=3) * 6
intersect_right = ndimage.zoom(spec_m[1, intersect_y1:intersect_y1+intersect_h1, :].real, zoom=(intersect_h2 / intersect_h1, 1.0), order=3) * 6
s = intersect_y2+intersect_left.shape[0]
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)
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)
for d in range(1, bands_n + 1):
bp = mp.param['band'][d]
@ -281,18 +306,18 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
if bp['hpf_start'] > 0:
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
if bands_n == 1:
wave = spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'])
wave = spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse'])
else:
wave = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side']))
wave = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
else:
sr = mp.param['band'][d+1]['sr']
if d == 1: # lower
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave = librosa.resample(spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side']), bp['sr'], sr, res_type="sinc_fastest")
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")
else: # mid
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave2 = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side']))
wave2 = np.add(wave, spectrogram_to_wave_mt(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse']))
wave = librosa.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
return wave.T
@ -324,31 +349,39 @@ if __name__ == "__main__":
import cv2
import sys
import time
import argparse
from model_param_init import ModelParameters
t0 = time.time()
p = argparse.ArgumentParser()
p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'min_mag', 'max_mag'], default='invert')
p.add_argument('--model_params', '-m', type=str, default='4band_44100.json')
p.add_argument('--output_name', '-o', type=str, default='test')
p.add_argument('input', nargs='+')
args = p.parse_args()
start_time = time.time()
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
mp = ModelParameters('3band_44100.json')
mp = ModelParameters(args.model_params)
for d in range(len(mp.param['band']), 0, -1):
print('band {}'.format(d), end=' ')
bp = mp.param['band'][d]
if d == len(mp.param['band']): # high band
if d == len(mp.param['band']): # high-end band
X_wave[d], _ = librosa.load(
sys.argv[1], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
args.input[0], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
y_wave[d], _ = librosa.load(
sys.argv[2], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
args.input[1], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
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'])
y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'])
y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'])
X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['reverse'])
print('ok')
@ -362,8 +395,13 @@ if __name__ == "__main__":
print('X_spec_m: ' + str(X_spec_m.shape))
print('y_spec_m: ' + str(y_spec_m.shape))
y_spec_m = reduce_vocal_aggressively(X_spec_m, y_spec_m, 0.2)
v_spec_m = X_spec_m - y_spec_m
if args.algorithm == 'invert':
y_spec_m = reduce_vocal_aggressively(X_spec_m, y_spec_m, 0.2)
v_spec_m = X_spec_m - y_spec_m
if args.algorithm == 'min_mag':
v_spec_m = np.where(np.abs(y_spec_m) <= np.abs(X_spec_m), y_spec_m, X_spec_m)
if args.algorithm == 'max_mag':
v_spec_m = np.where(np.abs(y_spec_m) >= np.abs(X_spec_m), y_spec_m, X_spec_m)
X_mag = np.abs(X_spec_m)
y_mag = np.abs(y_spec_m)
@ -373,12 +411,14 @@ if __name__ == "__main__":
y_image = spectrogram_to_image(y_mag)
v_image = spectrogram_to_image(v_mag)
cv2.imwrite('test_X.png', X_image)
#cv2.imwrite('test_y.png', y_image)
#cv2.imwrite('test_v.png', v_image)
if args.algorithm == 'invert':
cv2.imwrite('{}_X.png'.format(args.output_name), X_image)
cv2.imwrite('{}_y.png'.format(args.output_name), y_image)
cv2.imwrite('{}_v.png'.format(args.output_name), v_image)
sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(X_spec_m, mp), mp.param['sr'])
sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(y_spec_m, mp), mp.param['sr'])
sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec_m, mp), mp.param['sr'])
sf.write('test_X.wav', cmb_spectrogram_to_wave(X_spec_m, mp), mp.param['sr'])
#sf.write('test_y.wav', cmb_spectrogram_to_wave(y_spec_m, mp), mp.param['sr'])
#sf.write('test_v.wav', cmb_spectrogram_to_wave(v_spec_m, mp), mp.param['sr'])
print('total time: ' + str(time.time() - t0))
print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))