Cool_Tools/ultimatevocalremovergui
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Anjok07 2022-12-27 04:33:25 -06:00 committed by GitHub
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1 changed files with 12 additions and 38 deletions
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50
lib_v5/spec_utils.py
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@ -5,7 +5,14 @@ import math
import random
import pyrubberband
import math
#import noisereduce as nr
import platform
OPERATING_SYSTEM = platform.system()
if OPERATING_SYSTEM == 'Windows':
wav_resolution = "sinc_fastest"
else:
wav_resolution = "polyphase"
MAX_SPEC = 'Max Spec'
MIN_SPEC = 'Min Spec'
@ -189,7 +196,7 @@ def reduce_vocal_aggressively(X, y, softmask):
return y_mag * np.exp(1.j * np.angle(y))
def merge_artifacts(y_mask, thres=0.05, min_range=64, fade_size=32):
def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
if min_range < fade_size * 2:
raise ValueError('min_range must be >= fade_size * 2')
@ -224,7 +231,7 @@ def merge_artifacts(y_mask, thres=0.05, min_range=64, fade_size=32):
return y_mask
def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
def mask_silence(mag, ref, thres=0.1, min_range=64, fade_size=32):
if min_range < fade_size * 2:
raise ValueError('min_range must be >= fade_area * 2')
@ -329,12 +336,12 @@ def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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")
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=wav_resolution)
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")
wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
return wave
@ -460,30 +467,6 @@ def wave_to_spectrogram_no_mp(wave):
return spec
# def noise_reduction(audio_file):
# noise_pro = 'noise_pro.wav'
# wav, sr = librosa.load(audio_file, sr=44100, mono=False)
# wav_noise, noise_rate = librosa.load(noise_pro, sr=44100, mono=False)
# if wav.ndim == 1:
# wav = np.asfortranarray([wav,wav])
# wav_1 = nr.reduce_noise(audio_clip=wav[0], noise_clip=wav_noise, verbose=True)
# wav_2 = nr.reduce_noise(audio_clip=wav[1], noise_clip=wav_noise, verbose=True)
# if wav_1.shape > wav_2.shape:
# wav_2 = to_shape(wav_2, wav_1.shape)
# if wav_1.shape < wav_2.shape:
# wav_1 = to_shape(wav_1, wav_2.shape)
# #print('wav_1.shape: ', wav_1.shape)
# wav_mix = np.asfortranarray([wav_1, wav_2])
# return wav_mix, sr
def invert_audio(specs, invert_p=True):
ln = min([specs[0].shape[2], specs[1].shape[2]])
@ -518,8 +501,6 @@ def ensembling(a, specs):
spec = spec[:,:,:ln]
specs[i] = specs[i][:,:,:ln]
#print('spec: ', a)
if MIN_SPEC == a:
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
if MAX_SPEC == a:
@ -531,8 +512,6 @@ def ensembling(a, specs):
def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
#print(algorithm)
if algorithm == AVERAGE:
output = average_audio(audio_input)
samplerate = 44100
@ -543,9 +522,6 @@ def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save
wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
spec = wave_to_spectrogram_no_mp(wave)
specs.append(spec)
#print('output size: ', sys.getsizeof(spec))
#print('output size: ', sys.getsizeof(specs))
output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
@ -572,8 +548,6 @@ def to_shape_minimize(x: np.ndarray, target_shape):
def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
#print(rate)
wav, sr = librosa.load(audio_file, sr=44100, mono=False)
if wav.ndim == 1: