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| import librosa | |
| import numpy as np | |
| import soundfile as sf | |
| import math | |
| import random | |
| import math | |
| import platform | |
| import traceback | |
| from . import pyrb | |
| #cur | |
| OPERATING_SYSTEM = platform.system() | |
| SYSTEM_ARCH = platform.platform() | |
| SYSTEM_PROC = platform.processor() | |
| ARM = 'arm' | |
| if OPERATING_SYSTEM == 'Windows': | |
| from pyrubberband import pyrb | |
| else: | |
| from . import pyrb | |
| if OPERATING_SYSTEM == 'Darwin': | |
| wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest" | |
| else: | |
| wav_resolution = "sinc_fastest" | |
| MAX_SPEC = 'Max Spec' | |
| MIN_SPEC = 'Min Spec' | |
| AVERAGE = 'Average' | |
| def crop_center(h1, h2): | |
| h1_shape = h1.size() | |
| h2_shape = h2.size() | |
| if h1_shape[3] == h2_shape[3]: | |
| return h1 | |
| elif h1_shape[3] < h2_shape[3]: | |
| raise ValueError('h1_shape[3] must be greater than h2_shape[3]') | |
| s_time = (h1_shape[3] - h2_shape[3]) // 2 | |
| e_time = s_time + h2_shape[3] | |
| h1 = h1[:, :, :, s_time:e_time] | |
| return h1 | |
| def preprocess(X_spec): | |
| X_mag = np.abs(X_spec) | |
| X_phase = np.angle(X_spec) | |
| return X_mag, X_phase | |
| def make_padding(width, cropsize, offset): | |
| left = offset | |
| roi_size = cropsize - offset * 2 | |
| if roi_size == 0: | |
| roi_size = cropsize | |
| right = roi_size - (width % roi_size) + left | |
| return left, right, roi_size | |
| def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=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])) | |
| elif mid_side_b2: | |
| wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5)) | |
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5)) | |
| else: | |
| wave_left = np.asfortranarray(wave[0]) | |
| wave_right = np.asfortranarray(wave[1]) | |
| spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length) | |
| spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length) | |
| spec = np.asfortranarray([spec_left, spec_right]) | |
| return spec | |
| def wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False): | |
| import threading | |
| 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])) | |
| elif mid_side_b2: | |
| wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5)) | |
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5)) | |
| else: | |
| wave_left = np.asfortranarray(wave[0]) | |
| wave_right = np.asfortranarray(wave[1]) | |
| def run_thread(**kwargs): | |
| global spec_left | |
| spec_left = librosa.stft(**kwargs) | |
| thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length}) | |
| thread.start() | |
| spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length) | |
| thread.join() | |
| spec = np.asfortranarray([spec_left, spec_right]) | |
| return spec | |
| def normalize(wave, is_normalize=False): | |
| """Save output music files""" | |
| maxv = np.abs(wave).max() | |
| if maxv > 1.0: | |
| print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}") | |
| if is_normalize: | |
| print(f"The result was normalized.") | |
| wave /= maxv | |
| else: | |
| print(f"The result was not normalized.") | |
| else: | |
| print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}") | |
| return wave | |
| def normalize_two_stem(wave, mix, is_normalize=False): | |
| """Save output music files""" | |
| maxv = np.abs(wave).max() | |
| max_mix = np.abs(mix).max() | |
| if maxv > 1.0: | |
| print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}") | |
| print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}") | |
| if is_normalize: | |
| print(f"The result was normalized.") | |
| wave /= maxv | |
| mix /= maxv | |
| else: | |
| print(f"The result was not normalized.") | |
| else: | |
| print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}") | |
| print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}") | |
| print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}") | |
| return wave, mix | |
| def combine_spectrograms(specs, mp): | |
| l = min([specs[i].shape[2] for i in specs]) | |
| spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64) | |
| offset = 0 | |
| bands_n = len(mp.param['band']) | |
| for d in range(1, bands_n + 1): | |
| h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start'] | |
| spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l] | |
| offset += h | |
| if offset > mp.param['bins']: | |
| raise ValueError('Too much bins') | |
| # lowpass fiter | |
| if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']: | |
| if bands_n == 1: | |
| spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop']) | |
| else: | |
| gp = 1 | |
| for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']): | |
| g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0) | |
| gp = g | |
| spec_c[:, b, :] *= g | |
| return np.asfortranarray(spec_c) | |
| def spectrogram_to_image(spec, mode='magnitude'): | |
| if mode == 'magnitude': | |
| if np.iscomplexobj(spec): | |
| y = np.abs(spec) | |
| else: | |
| y = spec | |
| y = np.log10(y ** 2 + 1e-8) | |
| elif mode == 'phase': | |
| if np.iscomplexobj(spec): | |
| y = np.angle(spec) | |
| else: | |
| y = spec | |
| y -= y.min() | |
| y *= 255 / y.max() | |
| img = np.uint8(y) | |
| if y.ndim == 3: | |
| img = img.transpose(1, 2, 0) | |
| img = np.concatenate([ | |
| np.max(img, axis=2, keepdims=True), img | |
| ], axis=2) | |
| return img | |
| def reduce_vocal_aggressively(X, y, softmask): | |
| v = X - y | |
| y_mag_tmp = np.abs(y) | |
| v_mag_tmp = np.abs(v) | |
| v_mask = v_mag_tmp > y_mag_tmp | |
| y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf) | |
| return y_mag * np.exp(1.j * np.angle(y)) | |
| def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32): | |
| mask = y_mask | |
| try: | |
| if min_range < fade_size * 2: | |
| raise ValueError('min_range must be >= fade_size * 2') | |
| idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0] | |
| start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0]) | |
| end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1]) | |
| artifact_idx = np.where(end_idx - start_idx > min_range)[0] | |
| weight = np.zeros_like(y_mask) | |
| if len(artifact_idx) > 0: | |
| start_idx = start_idx[artifact_idx] | |
| end_idx = end_idx[artifact_idx] | |
| old_e = None | |
| for s, e in zip(start_idx, end_idx): | |
| if old_e is not None and s - old_e < fade_size: | |
| s = old_e - fade_size * 2 | |
| if s != 0: | |
| weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size) | |
| else: | |
| s -= fade_size | |
| if e != y_mask.shape[2]: | |
| weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size) | |
| else: | |
| e += fade_size | |
| weight[:, :, s + fade_size:e - fade_size] = 1 | |
| old_e = e | |
| v_mask = 1 - y_mask | |
| y_mask += weight * v_mask | |
| mask = y_mask | |
| except Exception as e: | |
| error_name = f'{type(e).__name__}' | |
| traceback_text = ''.join(traceback.format_tb(e.__traceback__)) | |
| message = f'{error_name}: "{e}"\n{traceback_text}"' | |
| print('Post Process Failed: ', message) | |
| return mask | |
| def align_wave_head_and_tail(a, b): | |
| l = min([a[0].size, b[0].size]) | |
| return a[:l,:l], b[:l,:l] | |
| 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]) | |
| wave_left = librosa.istft(spec_left, hop_length=hop_length) | |
| wave_right = librosa.istft(spec_right, hop_length=hop_length) | |
| 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)]) | |
| elif mid_side_b2: | |
| return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)]) | |
| else: | |
| return np.asfortranarray([wave_left, wave_right]) | |
| def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2): | |
| import threading | |
| spec_left = np.asfortranarray(spec[0]) | |
| spec_right = np.asfortranarray(spec[1]) | |
| def run_thread(**kwargs): | |
| global wave_left | |
| wave_left = librosa.istft(**kwargs) | |
| thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length}) | |
| thread.start() | |
| wave_right = librosa.istft(spec_right, hop_length=hop_length) | |
| thread.join() | |
| 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)]) | |
| elif mid_side_b2: | |
| return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)]) | |
| else: | |
| return np.asfortranarray([wave_left, wave_right]) | |
| def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None): | |
| 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=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=wav_resolution) | |
| return wave | |
| def fft_lp_filter(spec, bin_start, bin_stop): | |
| g = 1.0 | |
| for b in range(bin_start, bin_stop): | |
| g -= 1 / (bin_stop - bin_start) | |
| spec[:, b, :] = g * spec[:, b, :] | |
| spec[:, bin_stop:, :] *= 0 | |
| return spec | |
| def fft_hp_filter(spec, bin_start, bin_stop): | |
| g = 1.0 | |
| for b in range(bin_start, bin_stop, -1): | |
| g -= 1 / (bin_start - bin_stop) | |
| spec[:, b, :] = g * spec[:, b, :] | |
| spec[:, 0:bin_stop+1, :] *= 0 | |
| return spec | |
| def mirroring(a, spec_m, input_high_end, mp): | |
| if 'mirroring' == a: | |
| 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) | |
| mirror = mirror * np.exp(1.j * np.angle(input_high_end)) | |
| return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror) | |
| if 'mirroring2' == a: | |
| 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) | |
| mi = np.multiply(mirror, input_high_end * 1.7) | |
| return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi) | |
| def adjust_aggr(mask, is_non_accom_stem, aggressiveness): | |
| aggr = aggressiveness['value'] | |
| if aggr != 0: | |
| if is_non_accom_stem: | |
| aggr = 1 - aggr | |
| aggr = [aggr, aggr] | |
| if aggressiveness['aggr_correction'] is not None: | |
| aggr[0] += aggressiveness['aggr_correction']['left'] | |
| aggr[1] += aggressiveness['aggr_correction']['right'] | |
| for ch in range(2): | |
| mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3) | |
| mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch]) | |
| # if is_non_accom_stem: | |
| # mask = (1.0 - mask) | |
| return mask | |
| def stft(wave, nfft, hl): | |
| wave_left = np.asfortranarray(wave[0]) | |
| wave_right = np.asfortranarray(wave[1]) | |
| spec_left = librosa.stft(wave_left, nfft, hop_length=hl) | |
| spec_right = librosa.stft(wave_right, nfft, hop_length=hl) | |
| spec = np.asfortranarray([spec_left, spec_right]) | |
| return spec | |
| def istft(spec, hl): | |
| spec_left = np.asfortranarray(spec[0]) | |
| spec_right = np.asfortranarray(spec[1]) | |
| wave_left = librosa.istft(spec_left, hop_length=hl) | |
| wave_right = librosa.istft(spec_right, hop_length=hl) | |
| wave = np.asfortranarray([wave_left, wave_right]) | |
| return wave | |
| def spec_effects(wave, algorithm='Default', value=None): | |
| spec = [stft(wave[0],2048,1024), stft(wave[1],2048,1024)] | |
| if algorithm == 'Min_Mag': | |
| v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0]) | |
| wave = istft(v_spec_m,1024) | |
| elif algorithm == 'Max_Mag': | |
| v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0]) | |
| wave = istft(v_spec_m,1024) | |
| elif algorithm == 'Default': | |
| wave = (wave[1] * value) + (wave[0] * (1-value)) | |
| elif algorithm == 'Invert_p': | |
| X_mag = np.abs(spec[0]) | |
| y_mag = np.abs(spec[1]) | |
| max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) | |
| v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0])) | |
| wave = istft(v_spec,1024) | |
| return wave | |
| def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024): | |
| wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length) | |
| if wave.ndim == 1: | |
| wave = np.asfortranarray([wave,wave]) | |
| return wave | |
| def wave_to_spectrogram_no_mp(wave): | |
| spec = librosa.stft(wave, n_fft=2048, hop_length=1024) | |
| if spec.ndim == 1: | |
| spec = np.asfortranarray([spec,spec]) | |
| return spec | |
| def invert_audio(specs, invert_p=True): | |
| ln = min([specs[0].shape[2], specs[1].shape[2]]) | |
| specs[0] = specs[0][:,:,:ln] | |
| specs[1] = specs[1][:,:,:ln] | |
| if invert_p: | |
| X_mag = np.abs(specs[0]) | |
| y_mag = np.abs(specs[1]) | |
| max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) | |
| v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0])) | |
| else: | |
| specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2) | |
| v_spec = specs[0] - specs[1] | |
| return v_spec | |
| def invert_stem(mixture, stem): | |
| mixture = wave_to_spectrogram_no_mp(mixture) | |
| stem = wave_to_spectrogram_no_mp(stem) | |
| output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem])) | |
| return -output.T | |
| def ensembling(a, specs): | |
| for i in range(1, len(specs)): | |
| if i == 1: | |
| spec = specs[0] | |
| ln = min([spec.shape[2], specs[i].shape[2]]) | |
| spec = spec[:,:,:ln] | |
| specs[i] = specs[i][:,:,:ln] | |
| if MIN_SPEC == a: | |
| spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec) | |
| if MAX_SPEC == a: | |
| spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec) | |
| if AVERAGE == a: | |
| spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec) | |
| return spec | |
| def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path): | |
| wavs_ = [] | |
| if algorithm == AVERAGE: | |
| output = average_audio(audio_input) | |
| samplerate = 44100 | |
| else: | |
| specs = [] | |
| for i in range(len(audio_input)): | |
| wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100) | |
| wavs_.append(wave) | |
| spec = wave_to_spectrogram_no_mp(wave) | |
| specs.append(spec) | |
| wave_shapes = [w.shape[1] for w in wavs_] | |
| target_shape = wavs_[wave_shapes.index(max(wave_shapes))] | |
| output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs)) | |
| output = to_shape(output, target_shape.shape) | |
| sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set) | |
| def to_shape(x, target_shape): | |
| padding_list = [] | |
| for x_dim, target_dim in zip(x.shape, target_shape): | |
| pad_value = (target_dim - x_dim) | |
| pad_tuple = ((0, pad_value)) | |
| padding_list.append(pad_tuple) | |
| return np.pad(x, tuple(padding_list), mode='constant') | |
| def to_shape_minimize(x: np.ndarray, target_shape): | |
| padding_list = [] | |
| for x_dim, target_dim in zip(x.shape, target_shape): | |
| pad_value = (target_dim - x_dim) | |
| pad_tuple = ((0, pad_value)) | |
| padding_list.append(pad_tuple) | |
| return np.pad(x, tuple(padding_list), mode='constant') | |
| def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False): | |
| wav, sr = librosa.load(audio_file, sr=44100, mono=False) | |
| if wav.ndim == 1: | |
| wav = np.asfortranarray([wav,wav]) | |
| if is_pitch: | |
| wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None) | |
| wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None) | |
| else: | |
| wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None) | |
| wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None) | |
| 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) | |
| wav_mix = np.asfortranarray([wav_1, wav_2]) | |
| sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set) | |
| save_format(export_path) | |
| def average_audio(audio): | |
| waves = [] | |
| wave_shapes = [] | |
| final_waves = [] | |
| for i in range(len(audio)): | |
| wave = librosa.load(audio[i], sr=44100, mono=False) | |
| waves.append(wave[0]) | |
| wave_shapes.append(wave[0].shape[1]) | |
| wave_shapes_index = wave_shapes.index(max(wave_shapes)) | |
| target_shape = waves[wave_shapes_index] | |
| waves.pop(wave_shapes_index) | |
| final_waves.append(target_shape) | |
| for n_array in waves: | |
| wav_target = to_shape(n_array, target_shape.shape) | |
| final_waves.append(wav_target) | |
| waves = sum(final_waves) | |
| waves = waves/len(audio) | |
| return waves | |
| def average_dual_sources(wav_1, wav_2, value): | |
| 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) | |
| wave = (wav_1 * value) + (wav_2 * (1-value)) | |
| return wave | |
| def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray): | |
| if wav_1.shape > wav_2.shape: | |
| wav_2 = to_shape(wav_2, wav_1.shape) | |
| if wav_1.shape < wav_2.shape: | |
| ln = min([wav_1.shape[1], wav_2.shape[1]]) | |
| wav_2 = wav_2[:,:ln] | |
| ln = min([wav_1.shape[1], wav_2.shape[1]]) | |
| wav_1 = wav_1[:,:ln] | |
| wav_2 = wav_2[:,:ln] | |
| return wav_2 | |
| def align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format): | |
| def get_diff(a, b): | |
| corr = np.correlate(a, b, "full") | |
| diff = corr.argmax() - (b.shape[0] - 1) | |
| return diff | |
| progress_bar_main_var.set(10) | |
| # read tracks | |
| wav1, sr1 = librosa.load(file1, sr=44100, mono=False) | |
| wav2, sr2 = librosa.load(file2, sr=44100, mono=False) | |
| wav1 = wav1.transpose() | |
| wav2 = wav2.transpose() | |
| command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n") | |
| wav2_org = wav2.copy() | |
| progress_bar_main_var.set(20) | |
| command_Text("Processing files... \n") | |
| # pick random position and get diff | |
| counts = {} # counting up for each diff value | |
| progress = 20 | |
| check_range = 64 | |
| base = (64 / check_range) | |
| for i in range(check_range): | |
| index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2)) | |
| shift = int(random.uniform(-22050,+22050)) | |
| samp1 = wav1[index :index +44100, 0] # currently use left channel | |
| samp2 = wav2[index+shift:index+shift+44100, 0] | |
| progress += 1 * base | |
| progress_bar_main_var.set(progress) | |
| diff = get_diff(samp1, samp2) | |
| diff -= shift | |
| if abs(diff) < 22050: | |
| if not diff in counts: | |
| counts[diff] = 0 | |
| counts[diff] += 1 | |
| # use max counted diff value | |
| max_count = 0 | |
| est_diff = 0 | |
| for diff in counts.keys(): | |
| if counts[diff] > max_count: | |
| max_count = counts[diff] | |
| est_diff = diff | |
| command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n") | |
| progress_bar_main_var.set(90) | |
| audio_files = [] | |
| def save_aligned_audio(wav2_aligned): | |
| command_Text(f"Aligned File 2 with File 1.\n") | |
| command_Text(f"Saving files... ") | |
| sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set) | |
| save_format(file2_aligned) | |
| min_len = min(wav1.shape[0], wav2_aligned.shape[0]) | |
| wav_sub = wav1[:min_len] - wav2_aligned[:min_len] | |
| audio_files.append(file2_aligned) | |
| return min_len, wav_sub | |
| # make aligned track 2 | |
| if est_diff > 0: | |
| wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0) | |
| min_len, wav_sub = save_aligned_audio(wav2_aligned) | |
| elif est_diff < 0: | |
| wav2_aligned = wav2_org[-est_diff:] | |
| min_len, wav_sub = save_aligned_audio(wav2_aligned) | |
| else: | |
| command_Text(f"Audio files already aligned.\n") | |
| command_Text(f"Saving inverted track... ") | |
| min_len = min(wav1.shape[0], wav2.shape[0]) | |
| wav_sub = wav1[:min_len] - wav2[:min_len] | |
| wav_sub = np.clip(wav_sub, -1, +1) | |
| sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set) | |
| save_format(file_subtracted) | |
| progress_bar_main_var.set(95) |