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https://github.com/Anjok07/ultimatevocalremovergui.git
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Update spec_utils.py
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aufr33
GitHub
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commit
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@ -1,11 +1,12 @@
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import os
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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 math
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import json
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import hashlib
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import threading
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from tqdm import tqdm
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@ -26,53 +27,39 @@ def crop_center(h1, h2):
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return h1
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def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, reverse=False):
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if reverse:
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def wave_to_spectrogram(wave, hop_length, n_fft, mp, multithreading):
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if mp.param['reverse']:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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elif mp.param['mid_side_b']:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1] * .5))
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wave_right = np.asfortranarray(np.subtract(wave[1], wave[0] * .5))
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elif mp.param['mid_side']:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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if multithreading:
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def run_thread(**kwargs):
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global spec_left_mt
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spec_left_mt = librosa.stft(**kwargs)
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spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, reverse=False):
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import threading
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
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thread.start()
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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thread.join()
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spec = np.asfortranarray([spec_left_mt, spec_right])
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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def run_thread(**kwargs):
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global spec_left
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spec_left = librosa.stft(**kwargs)
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thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
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thread.start()
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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thread.join()
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spec = np.asfortranarray([spec_left, spec_right])
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spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def combine_spectrograms(specs, mp):
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l = min([specs[i].shape[2] for i in specs])
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spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
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@ -99,7 +86,7 @@ def combine_spectrograms(specs, mp):
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spec_c[:, b, :] *= g
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return np.asfortranarray(spec_c)
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def spectrogram_to_image(spec, mode='magnitude'):
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if mode == 'magnitude':
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@ -214,8 +201,8 @@ def cache_or_load(mix_path, inst_path, 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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X_spec_s[d] = wave_to_spectrogram(X_wave[d], bp['hl'], bp['n_fft'], mp, False)
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y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp, False)
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del X_wave, y_wave
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@ -232,45 +219,36 @@ def cache_or_load(mix_path, inst_path, mp):
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return X_spec_m, y_spec_m
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def spectrogram_to_wave(spec, hop_length, mid_side, reverse):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hop_length)
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
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else:
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return np.asfortranarray([wave_left, wave_right])
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def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse):
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def spectrogram_to_wave(spec, hop_length, mp, multithreading):
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import threading
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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def run_thread(**kwargs):
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global wave_left
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wave_left = librosa.istft(**kwargs)
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thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
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thread.start()
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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thread.join()
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if multithreading:
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def run_thread(**kwargs):
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global wave_left
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wave_left = librosa.istft(**kwargs)
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thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
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thread.start()
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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thread.join()
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else:
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wave_left = librosa.istft(spec_left, hop_length=hop_length)
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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if reverse:
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if mp.param['reverse']:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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elif mp.param['mid_side_b']:
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return np.asfortranarray([np.subtract(wave_left / 1.25, .4 * wave_right), np.add(wave_right / 1.25, .4 * wave_left)])
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elif mp.param['mid_side']:
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return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
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else:
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return np.asfortranarray([wave_left, wave_right])
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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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@ -290,18 +268,18 @@ 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(spec_s, bp['hl'], mp.param['mid_side'], mp.param['reverse'])
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wave = spectrogram_to_wave(spec_s, bp['hl'], mp, False)
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else:
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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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wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, False))
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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(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, False), 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(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, False))
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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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@ -401,7 +379,7 @@ if __name__ == "__main__":
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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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spec[d] = wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp, False)
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specs[i] = combine_spectrograms(spec, mp)
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