Cool_Tools/ultimatevocalremovergui
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Anjok07 2022-07-06 02:58:17 -05:00 committed by GitHub
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62
lib_v5/spec_utils.py
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@ -78,6 +78,26 @@ def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=
return spec
def normalize(wave_res):
"""Save output music files"""
maxv = np.abs(wave_res).max()
if maxv > 1.0:
print(f"Input above threshold for clipping. The result was normalized. Max:{maxv}")
wave_res /= maxv
else:
print(f"Input not above threshold for clipping. Max:{maxv}")
return wave_res
def nonormalize(wave_res):
"""Save output music files"""
maxv = np.abs(wave_res).max()
if maxv > 1.0:
print(f"Input above threshold for clipping. The result was not normalized. Max:{maxv}")
else:
print(f"Input not above threshold for clipping. Max:{maxv}")
return wave_res
def combine_spectrograms(specs, mp):
l = min([specs[i].shape[2] for i in specs])
@ -239,7 +259,7 @@ 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, mid_side_b2, reverse):
def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
@ -316,6 +336,46 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
return wave.T
def cmb_spectrogram_to_wave_d(spec_m, mp, extra_bins_h=None, extra_bins=None, demucs=True):
wave_band = {}
bands_n = len(mp.param['band'])
offset = 0
for d in range(1, bands_n + 1):
bp = mp.param['band'][d]
spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
h = bp['crop_stop'] - bp['crop_start']
spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
offset += h
if d == bands_n: # higher
if extra_bins_h: # if --high_end_process bypass
max_bin = bp['n_fft'] // 2
spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
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(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
else:
wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], 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(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], 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(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
wave = librosa.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
print(demucs)
if demucs == True:
wave = librosa.resample(wave, bp['sr'], 44100, res_type="sinc_fastest")
return wave
else:
return wave
def fft_lp_filter(spec, bin_start, bin_stop):
g = 1.0