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| import audioread | |
| import librosa | |
| import numpy as np | |
| import soundfile as sf | |
| import math | |
| import platform | |
| import traceback | |
| from . import pyrb | |
| from scipy.signal import correlate, hilbert | |
| import io | |
| OPERATING_SYSTEM = platform.system() | |
| SYSTEM_ARCH = platform.platform() | |
| SYSTEM_PROC = platform.processor() | |
| ARM = 'arm' | |
| AUTO_PHASE = "Automatic" | |
| POSITIVE_PHASE = "Positive Phase" | |
| NEGATIVE_PHASE = "Negative Phase" | |
| NONE_P = "None", | |
| LOW_P = "Shifts: Low", | |
| MED_P = "Shifts: Medium", | |
| HIGH_P = "Shifts: High", | |
| VHIGH_P = "Shifts: Very High" | |
| MAXIMUM_P = "Shifts: Maximum" | |
| progress_value = 0 | |
| last_update_time = 0 | |
| is_macos = False | |
| 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" | |
| wav_resolution_float_resampling = "kaiser_best" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution | |
| is_macos = True | |
| else: | |
| wav_resolution = "sinc_fastest" | |
| wav_resolution_float_resampling = wav_resolution | |
| MAX_SPEC = 'Max Spec' | |
| MIN_SPEC = 'Min Spec' | |
| LIN_ENSE = 'Linear Ensemble' | |
| MAX_WAV = MAX_SPEC | |
| MIN_WAV = 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 normalize(wave, is_normalize=False): | |
| """Normalize audio""" | |
| maxv = np.abs(wave).max() | |
| if maxv > 1.0: | |
| if is_normalize: | |
| print("Above clipping threshold.") | |
| wave /= maxv | |
| return wave | |
| def auto_transpose(audio_array:np.ndarray): | |
| """ | |
| Ensure that the audio array is in the (channels, samples) format. | |
| Parameters: | |
| audio_array (ndarray): Input audio array. | |
| Returns: | |
| ndarray: Transposed audio array if necessary. | |
| """ | |
| # If the second dimension is 2 (indicating stereo channels), transpose the array | |
| if audio_array.shape[1] == 2: | |
| return audio_array.T | |
| return audio_array | |
| def write_array_to_mem(audio_data, subtype): | |
| if isinstance(audio_data, np.ndarray): | |
| audio_buffer = io.BytesIO() | |
| sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format='WAV') | |
| audio_buffer.seek(0) | |
| return audio_buffer | |
| else: | |
| return audio_data | |
| 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 convert_channels(spec, mp, band): | |
| cc = mp.param['band'][band].get('convert_channels') | |
| if 'mid_side_c' == cc: | |
| spec_left = np.add(spec[0], spec[1] * .25) | |
| spec_right = np.subtract(spec[1], spec[0] * .25) | |
| elif 'mid_side' == cc: | |
| spec_left = np.add(spec[0], spec[1]) / 2 | |
| spec_right = np.subtract(spec[0], spec[1]) | |
| elif 'stereo_n' == cc: | |
| spec_left = np.add(spec[0], spec[1] * .25) / 0.9375 | |
| spec_right = np.add(spec[1], spec[0] * .25) / 0.9375 | |
| else: | |
| return spec | |
| return np.asfortranarray([spec_left, spec_right]) | |
| def combine_spectrograms(specs, mp, is_v51_model=False): | |
| 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: | |
| if is_v51_model: | |
| spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param['pre_filter_start'], mp.param['pre_filter_stop']) | |
| else: | |
| 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 wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False): | |
| if wave.ndim == 1: | |
| wave = np.asfortranarray([wave,wave]) | |
| if not is_v51_model: | |
| if mp.param['reverse']: | |
| wave_left = np.flip(np.asfortranarray(wave[0])) | |
| wave_right = np.flip(np.asfortranarray(wave[1])) | |
| elif mp.param['mid_side']: | |
| wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2) | |
| wave_right = np.asfortranarray(np.subtract(wave[0], wave[1])) | |
| elif mp.param['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]) | |
| 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]) | |
| if is_v51_model: | |
| spec = convert_channels(spec, mp, band) | |
| return spec | |
| def spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True): | |
| 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 is_v51_model: | |
| cc = mp.param['band'][band].get('convert_channels') | |
| if 'mid_side_c' == cc: | |
| return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)]) | |
| elif 'mid_side' == cc: | |
| return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]) | |
| elif 'stereo_n' == cc: | |
| return np.asfortranarray([np.subtract(wave_left, wave_right * .25), np.subtract(wave_right, wave_left * .25)]) | |
| else: | |
| if mp.param['reverse']: | |
| return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)]) | |
| elif mp.param['mid_side']: | |
| return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)]) | |
| elif mp.param['mid_side_b2']: | |
| return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)]) | |
| return np.asfortranarray([wave_left, wave_right]) | |
| def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False): | |
| 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: | |
| if is_v51_model: | |
| spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1) | |
| else: | |
| 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, d, is_v51_model) | |
| else: | |
| wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model)) | |
| else: | |
| sr = mp.param['band'][d+1]['sr'] | |
| if d == 1: # lower | |
| if is_v51_model: | |
| spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop']) | |
| else: | |
| spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop']) | |
| wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model), bp['sr'], sr, res_type=wav_resolution) | |
| else: # mid | |
| if is_v51_model: | |
| spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1) | |
| spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop']) | |
| else: | |
| 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, d, is_v51_model)) | |
| wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution) | |
| return wave | |
| def get_lp_filter_mask(n_bins, bin_start, bin_stop): | |
| mask = np.concatenate([ | |
| np.ones((bin_start - 1, 1)), | |
| np.linspace(1, 0, bin_stop - bin_start + 1)[:, None], | |
| np.zeros((n_bins - bin_stop, 1)) | |
| ], axis=0) | |
| return mask | |
| def get_hp_filter_mask(n_bins, bin_start, bin_stop): | |
| mask = np.concatenate([ | |
| np.zeros((bin_stop + 1, 1)), | |
| np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None], | |
| np.ones((n_bins - bin_start - 2, 1)) | |
| ], axis=0) | |
| return mask | |
| 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 spectrogram_to_wave_old(spec, hop_length=1024): | |
| if spec.ndim == 2: | |
| wave = librosa.istft(spec, hop_length=hop_length) | |
| elif spec.ndim == 3: | |
| 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) | |
| wave = np.asfortranarray([wave_left, wave_right]) | |
| return wave | |
| def wave_to_spectrogram_old(wave, hop_length, n_fft): | |
| 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 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'] * 2 | |
| 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]) | |
| 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, inputs, is_wavs=False): | |
| for i in range(1, len(inputs)): | |
| if i == 1: | |
| input = inputs[0] | |
| if is_wavs: | |
| ln = min([input.shape[1], inputs[i].shape[1]]) | |
| input = input[:,:ln] | |
| inputs[i] = inputs[i][:,:ln] | |
| else: | |
| ln = min([input.shape[2], inputs[i].shape[2]]) | |
| input = input[:,:,:ln] | |
| inputs[i] = inputs[i][:,:,:ln] | |
| if MIN_SPEC == a: | |
| input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input) | |
| if MAX_SPEC == a: | |
| input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input) | |
| #linear_ensemble | |
| #input = ensemble_wav(inputs, split_size=1) | |
| return input | |
| def ensemble_for_align(waves): | |
| specs = [] | |
| for wav in waves: | |
| spec = wave_to_spectrogram_no_mp(wav.T) | |
| specs.append(spec) | |
| wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T | |
| wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True) | |
| return wav_aligned | |
| def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False): | |
| 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 if is_wave else 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))] | |
| if is_wave: | |
| output = ensembling(algorithm, specs, is_wavs=True) | |
| else: | |
| 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 detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024): | |
| """ | |
| Detect silence at the beginning of an audio signal. | |
| :param audio: np.array, audio signal | |
| :param sr: int, sample rate | |
| :param silence_threshold: float, magnitude threshold below which is considered silence | |
| :param frame_length: int, the number of samples to consider for each check | |
| :return: float, duration of the leading silence in milliseconds | |
| """ | |
| if len(audio.shape) == 2: | |
| # If stereo, pick the channel with more energy to determine the silence | |
| channel = np.argmax(np.sum(np.abs(audio), axis=1)) | |
| audio = audio[channel] | |
| for i in range(0, len(audio), frame_length): | |
| if np.max(np.abs(audio[i:i+frame_length])) > silence_threshold: | |
| return (i / sr) * 1000 | |
| return (len(audio) / sr) * 1000 | |
| def adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024): | |
| """ | |
| Adjust the leading silence of the target_audio to match the leading silence of the reference_audio. | |
| :param target_audio: np.array, audio signal that will have its silence adjusted | |
| :param reference_audio: np.array, audio signal used as a reference | |
| :param sr: int, sample rate | |
| :param silence_threshold: float, magnitude threshold below which is considered silence | |
| :param frame_length: int, the number of samples to consider for each check | |
| :return: np.array, target_audio adjusted to have the same leading silence as reference_audio | |
| """ | |
| def find_silence_end(audio): | |
| if len(audio.shape) == 2: | |
| # If stereo, pick the channel with more energy to determine the silence | |
| channel = np.argmax(np.sum(np.abs(audio), axis=1)) | |
| audio_mono = audio[channel] | |
| else: | |
| audio_mono = audio | |
| for i in range(0, len(audio_mono), frame_length): | |
| if np.max(np.abs(audio_mono[i:i+frame_length])) > silence_threshold: | |
| return i | |
| return len(audio_mono) | |
| ref_silence_end = find_silence_end(reference_audio) | |
| target_silence_end = find_silence_end(target_audio) | |
| silence_difference = ref_silence_end - target_silence_end | |
| try: | |
| ref_silence_end_p = (ref_silence_end / 44100) * 1000 | |
| target_silence_end_p = (target_silence_end / 44100) * 1000 | |
| silence_difference_p = ref_silence_end_p - target_silence_end_p | |
| print("silence_difference: ", silence_difference_p) | |
| except Exception as e: | |
| pass | |
| if silence_difference > 0: # Add silence to target_audio | |
| if len(target_audio.shape) == 2: # stereo | |
| silence_to_add = np.zeros((target_audio.shape[0], silence_difference)) | |
| else: # mono | |
| silence_to_add = np.zeros(silence_difference) | |
| return np.hstack((silence_to_add, target_audio)) | |
| elif silence_difference < 0: # Remove silence from target_audio | |
| if len(target_audio.shape) == 2: # stereo | |
| return target_audio[:, -silence_difference:] | |
| else: # mono | |
| return target_audio[-silence_difference:] | |
| else: # No adjustment needed | |
| return target_audio | |
| def match_array_shapes(array_1:np.ndarray, array_2:np.ndarray, is_swap=False): | |
| if is_swap: | |
| array_1, array_2 = array_1.T, array_2.T | |
| #print("before", array_1.shape, array_2.shape) | |
| if array_1.shape[1] > array_2.shape[1]: | |
| array_1 = array_1[:,:array_2.shape[1]] | |
| elif array_1.shape[1] < array_2.shape[1]: | |
| padding = array_2.shape[1] - array_1.shape[1] | |
| array_1 = np.pad(array_1, ((0,0), (0,padding)), 'constant', constant_values=0) | |
| #print("after", array_1.shape, array_2.shape) | |
| if is_swap: | |
| array_1, array_2 = array_1.T, array_2.T | |
| return array_1 | |
| def match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray): | |
| if len(array_1) > len(array_2): | |
| array_1 = array_1[:len(array_2)] | |
| elif len(array_1) < len(array_2): | |
| padding = len(array_2) - len(array_1) | |
| array_1 = np.pad(array_1, (0, padding), 'constant', constant_values=0) | |
| return array_1 | |
| def change_pitch_semitones(y, sr, semitone_shift): | |
| factor = 2 ** (semitone_shift / 12) # Convert semitone shift to factor for resampling | |
| y_pitch_tuned = [] | |
| for y_channel in y: | |
| y_pitch_tuned.append(librosa.resample(y_channel, sr, sr*factor, res_type=wav_resolution_float_resampling)) | |
| y_pitch_tuned = np.array(y_pitch_tuned) | |
| new_sr = sr * factor | |
| return y_pitch_tuned, new_sr | |
| def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True): | |
| wav, sr = librosa.load(audio_file, sr=44100, mono=False) | |
| if wav.ndim == 1: | |
| wav = np.asfortranarray([wav,wav]) | |
| if not is_time_correction: | |
| wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0] | |
| else: | |
| 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 reshape_sources_ref(wav_1_shape, wav_2: np.ndarray): | |
| if wav_1_shape > wav_2.shape: | |
| wav_2 = to_shape(wav_2, wav_1_shape) | |
| return wav_2 | |
| def combine_arrarys(audio_sources, is_swap=False): | |
| source = np.zeros_like(max(audio_sources, key=np.size)) | |
| for v in audio_sources: | |
| v = match_array_shapes(v, source, is_swap=is_swap) | |
| source += v | |
| return source | |
| def combine_audio(paths: list, audio_file_base=None, wav_type_set='FLOAT', save_format=None): | |
| source = combine_arrarys([load_audio(i) for i in paths]) | |
| save_path = f"{audio_file_base}_combined.wav" | |
| sf.write(save_path, source.T, 44100, subtype=wav_type_set) | |
| save_format(save_path) | |
| def reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9): | |
| # Reduce the volume | |
| inst_source = inst_source * (1 - reduction_rate) | |
| mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True) | |
| return mix_reduced | |
| def organize_inputs(inputs): | |
| input_list = { | |
| "target":None, | |
| "reference":None, | |
| "reverb":None, | |
| "inst":None | |
| } | |
| for i in inputs: | |
| if i.endswith("_(Vocals).wav"): | |
| input_list["reference"] = i | |
| elif "_RVC_" in i: | |
| input_list["target"] = i | |
| elif i.endswith("reverbed_stem.wav"): | |
| input_list["reverb"] = i | |
| elif i.endswith("_(Instrumental).wav"): | |
| input_list["inst"] = i | |
| return input_list | |
| def check_if_phase_inverted(wav1, wav2, is_mono=False): | |
| # Load the audio files | |
| if not is_mono: | |
| wav1 = np.mean(wav1, axis=0) | |
| wav2 = np.mean(wav2, axis=0) | |
| # Compute the correlation | |
| correlation = np.corrcoef(wav1[:1000], wav2[:1000]) | |
| return correlation[0,1] < 0 | |
| def align_audio(file1, | |
| file2, | |
| file2_aligned, | |
| file_subtracted, | |
| wav_type_set, | |
| is_save_aligned, | |
| command_Text, | |
| save_format, | |
| align_window:list, | |
| align_intro_val:list, | |
| db_analysis:tuple, | |
| set_progress_bar, | |
| phase_option, | |
| phase_shifts, | |
| is_match_silence, | |
| is_spec_match): | |
| global progress_value | |
| progress_value = 0 | |
| is_mono = False | |
| def get_diff(a, b): | |
| corr = np.correlate(a, b, "full") | |
| diff = corr.argmax() - (b.shape[0] - 1) | |
| return diff | |
| def progress_bar(length): | |
| global progress_value | |
| progress_value += 1 | |
| if (0.90/length*progress_value) >= 0.9: | |
| length = progress_value + 1 | |
| set_progress_bar(0.1, (0.9/length*progress_value)) | |
| # read tracks | |
| if file1.endswith(".mp3") and is_macos: | |
| length1 = rerun_mp3(file1) | |
| wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False) | |
| else: | |
| wav1, sr1 = librosa.load(file1, sr=44100, mono=False) | |
| if file2.endswith(".mp3") and is_macos: | |
| length2 = rerun_mp3(file2) | |
| wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False) | |
| else: | |
| wav2, sr2 = librosa.load(file2, sr=44100, mono=False) | |
| if wav1.ndim == 1 and wav2.ndim == 1: | |
| is_mono = True | |
| elif wav1.ndim == 1: | |
| wav1 = np.asfortranarray([wav1,wav1]) | |
| elif wav2.ndim == 1: | |
| wav2 = np.asfortranarray([wav2,wav2]) | |
| # Check if phase is inverted | |
| if phase_option == AUTO_PHASE: | |
| if check_if_phase_inverted(wav1, wav2, is_mono=is_mono): | |
| wav2 = -wav2 | |
| elif phase_option == POSITIVE_PHASE: | |
| wav2 = +wav2 | |
| elif phase_option == NEGATIVE_PHASE: | |
| wav2 = -wav2 | |
| if is_match_silence: | |
| wav2 = adjust_leading_silence(wav2, wav1) | |
| wav1_length = int(librosa.get_duration(y=wav1, sr=44100)) | |
| wav2_length = int(librosa.get_duration(y=wav2, sr=44100)) | |
| if not is_mono: | |
| wav1 = wav1.transpose() | |
| wav2 = wav2.transpose() | |
| wav2_org = wav2.copy() | |
| command_Text("Processing files... \n") | |
| seconds_length = min(wav1_length, wav2_length) | |
| wav2_aligned_sources = [] | |
| for sec_len in align_intro_val: | |
| # pick a position at 1 second in and get diff | |
| sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len) | |
| index = sr1*sec_seg # 1 second in, assuming sr1 = sr2 = 44100 | |
| if is_mono: | |
| samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1] | |
| diff = get_diff(samp1, samp2) | |
| #print(f"Estimated difference: {diff}\n") | |
| else: | |
| index = sr1*sec_seg # 1 second in, assuming sr1 = sr2 = 44100 | |
| samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0] | |
| samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1] | |
| diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r) | |
| #print(f"Estimated difference Left Channel: {diff}\nEstimated difference Right Channel: {diff_r}\n") | |
| # make aligned track 2 | |
| if diff > 0: | |
| zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2)) | |
| wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0) | |
| elif diff < 0: | |
| wav2_aligned = wav2_org[-diff:] | |
| else: | |
| wav2_aligned = wav2_org | |
| #command_Text(f"Audio files already aligned.\n") | |
| if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources): | |
| wav2_aligned_sources.append(wav2_aligned) | |
| #print("Unique Sources: ", len(wav2_aligned_sources)) | |
| unique_sources = len(wav2_aligned_sources) | |
| sub_mapper_big_mapper = {} | |
| for s in wav2_aligned_sources: | |
| wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True) | |
| if align_window: | |
| wav_sub = time_correction(wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts) | |
| wav_sub_size = np.abs(wav_sub).mean() | |
| sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}} | |
| else: | |
| wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20) | |
| db_range = db_analysis[1] | |
| for db_adjustment in db_range: | |
| # Adjust the dB of track2 | |
| s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20)) | |
| wav_sub = wav1 - s_adjusted | |
| wav_sub_size = np.abs(wav_sub).mean() | |
| sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}} | |
| #print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys())) | |
| sub_mapper_value_list = list(sub_mapper_big_mapper.values()) | |
| if is_spec_match and len(sub_mapper_value_list) >= 2: | |
| #print("using spec ensemble with align") | |
| wav_sub = ensemble_for_align(list(sub_mapper_big_mapper.values())) | |
| else: | |
| #print("using linear ensemble with align") | |
| wav_sub = ensemble_wav(list(sub_mapper_big_mapper.values())) | |
| #print(f"Mix Mean: {np.abs(wav1).mean()}\nInst Mean: {np.abs(wav2).mean()}") | |
| #print('Final: ', np.abs(wav_sub).mean()) | |
| wav_sub = np.clip(wav_sub, -1, +1) | |
| command_Text(f"Saving inverted track... ") | |
| if is_save_aligned or is_spec_match: | |
| wav1 = match_mono_array_shapes(wav1, wav_sub) if is_mono else match_array_shapes(wav1, wav_sub, is_swap=True) | |
| wav2_aligned = wav1 - wav_sub | |
| if is_spec_match: | |
| if wav1.ndim == 1 and wav2.ndim == 1: | |
| wav2_aligned = np.asfortranarray([wav2_aligned, wav2_aligned]).T | |
| wav1 = np.asfortranarray([wav1, wav1]).T | |
| wav2_aligned = ensemble_for_align([wav2_aligned, wav1]) | |
| wav_sub = wav1 - wav2_aligned | |
| if is_save_aligned: | |
| sf.write(file2_aligned, wav2_aligned, sr1, subtype=wav_type_set) | |
| save_format(file2_aligned) | |
| sf.write(file_subtracted, wav_sub, sr1, subtype=wav_type_set) | |
| save_format(file_subtracted) | |
| def phase_shift_hilbert(signal, degree): | |
| analytic_signal = hilbert(signal) | |
| return np.cos(np.radians(degree)) * analytic_signal.real - np.sin(np.radians(degree)) * analytic_signal.imag | |
| def get_phase_shifted_tracks(track, phase_shift): | |
| if phase_shift == 180: | |
| return [track, -track] | |
| step = phase_shift | |
| end = 180 - (180 % step) if 180 % step == 0 else 181 | |
| phase_range = range(step, end, step) | |
| flipped_list = [track, -track] | |
| for i in phase_range: | |
| flipped_list.extend([phase_shift_hilbert(track, i), phase_shift_hilbert(track, -i)]) | |
| return flipped_list | |
| def time_correction(mix:np.ndarray, instrumental:np.ndarray, seconds_length, align_window, db_analysis, sr=44100, progress_bar=None, unique_sources=None, phase_shifts=NONE_P): | |
| # Function to align two tracks using cross-correlation | |
| def align_tracks(track1, track2): | |
| # A dictionary to store each version of track2_shifted and its mean absolute value | |
| shifted_tracks = {} | |
| # Loop to adjust dB of track2 | |
| track2 = track2 * np.power(10, db_analysis[0] / 20) | |
| db_range = db_analysis[1] | |
| if phase_shifts == 190: | |
| track2_flipped = [track2] | |
| else: | |
| track2_flipped = get_phase_shifted_tracks(track2, phase_shifts) | |
| for db_adjustment in db_range: | |
| for t in track2_flipped: | |
| # Adjust the dB of track2 | |
| track2_adjusted = t * (10 ** (db_adjustment / 20)) | |
| corr = correlate(track1, track2_adjusted) | |
| delay = np.argmax(np.abs(corr)) - (len(track1) - 1) | |
| track2_shifted = np.roll(track2_adjusted, shift=delay) | |
| # Compute the mean absolute value of track2_shifted | |
| track2_shifted_sub = track1 - track2_shifted | |
| mean_abs_value = np.abs(track2_shifted_sub).mean() | |
| # Store track2_shifted and its mean absolute value in the dictionary | |
| shifted_tracks[mean_abs_value] = track2_shifted | |
| # Return the version of track2_shifted with the smallest mean absolute value | |
| return shifted_tracks[min(shifted_tracks.keys())] | |
| # Make sure the audio files have the same shape | |
| assert mix.shape == instrumental.shape, f"Audio files must have the same shape - Mix: {mix.shape}, Inst: {instrumental.shape}" | |
| seconds_length = seconds_length // 2 | |
| sub_mapper = {} | |
| progress_update_interval = 120 | |
| total_iterations = 0 | |
| if len(align_window) > 2: | |
| progress_update_interval = 320 | |
| for secs in align_window: | |
| step = secs / 2 | |
| window_size = int(sr * secs) | |
| step_size = int(sr * step) | |
| if len(mix.shape) == 1: | |
| total_mono = (len(range(0, len(mix) - window_size, step_size))//progress_update_interval)*unique_sources | |
| total_iterations += total_mono | |
| else: | |
| total_stereo_ = len(range(0, len(mix[:, 0]) - window_size, step_size))*2 | |
| total_stereo = (total_stereo_//progress_update_interval) * unique_sources | |
| total_iterations += total_stereo | |
| #print(total_iterations) | |
| for secs in align_window: | |
| sub = np.zeros_like(mix) | |
| divider = np.zeros_like(mix) | |
| step = secs / 2 | |
| window_size = int(sr * secs) | |
| step_size = int(sr * step) | |
| window = np.hanning(window_size) | |
| # For the mono case: | |
| if len(mix.shape) == 1: | |
| # The files are mono | |
| counter = 0 | |
| for i in range(0, len(mix) - window_size, step_size): | |
| counter += 1 | |
| if counter % progress_update_interval == 0: | |
| progress_bar(total_iterations) | |
| window_mix = mix[i:i+window_size] * window | |
| window_instrumental = instrumental[i:i+window_size] * window | |
| window_instrumental_aligned = align_tracks(window_mix, window_instrumental) | |
| sub[i:i+window_size] += window_mix - window_instrumental_aligned | |
| divider[i:i+window_size] += window | |
| else: | |
| # The files are stereo | |
| counter = 0 | |
| for ch in range(mix.shape[1]): | |
| for i in range(0, len(mix[:, ch]) - window_size, step_size): | |
| counter += 1 | |
| if counter % progress_update_interval == 0: | |
| progress_bar(total_iterations) | |
| window_mix = mix[i:i+window_size, ch] * window | |
| window_instrumental = instrumental[i:i+window_size, ch] * window | |
| window_instrumental_aligned = align_tracks(window_mix, window_instrumental) | |
| sub[i:i+window_size, ch] += window_mix - window_instrumental_aligned | |
| divider[i:i+window_size, ch] += window | |
| # Normalize the result by the overlap count | |
| sub = np.where(divider > 1e-6, sub / divider, sub) | |
| sub_size = np.abs(sub).mean() | |
| sub_mapper = {**sub_mapper, **{sub_size: sub}} | |
| #print("SUB_LEN", len(list(sub_mapper.values()))) | |
| sub = ensemble_wav(list(sub_mapper.values()), split_size=12) | |
| return sub | |
| def ensemble_wav(waveforms, split_size=240): | |
| # Create a dictionary to hold the thirds of each waveform and their mean absolute values | |
| waveform_thirds = {i: np.array_split(waveform, split_size) for i, waveform in enumerate(waveforms)} | |
| # Initialize the final waveform | |
| final_waveform = [] | |
| # For chunk | |
| for third_idx in range(split_size): | |
| # Compute the mean absolute value of each third from each waveform | |
| means = [np.abs(waveform_thirds[i][third_idx]).mean() for i in range(len(waveforms))] | |
| # Find the index of the waveform with the lowest mean absolute value for this third | |
| min_index = np.argmin(means) | |
| # Add the least noisy third to the final waveform | |
| final_waveform.append(waveform_thirds[min_index][third_idx]) | |
| # Concatenate all the thirds to create the final waveform | |
| final_waveform = np.concatenate(final_waveform) | |
| return final_waveform | |
| def ensemble_wav_min(waveforms): | |
| for i in range(1, len(waveforms)): | |
| if i == 1: | |
| wave = waveforms[0] | |
| ln = min(len(wave), len(waveforms[i])) | |
| wave = wave[:ln] | |
| waveforms[i] = waveforms[i][:ln] | |
| wave = np.where(np.abs(waveforms[i]) <= np.abs(wave), waveforms[i], wave) | |
| return wave | |
| def align_audio_test(wav1, wav2, sr1=44100): | |
| def get_diff(a, b): | |
| corr = np.correlate(a, b, "full") | |
| diff = corr.argmax() - (b.shape[0] - 1) | |
| return diff | |
| # read tracks | |
| wav1 = wav1.transpose() | |
| wav2 = wav2.transpose() | |
| #print(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n") | |
| wav2_org = wav2.copy() | |
| # pick a position at 1 second in and get diff | |
| index = sr1#*seconds_length # 1 second in, assuming sr1 = sr2 = 44100 | |
| samp1 = wav1[index : index + sr1, 0] # currently use left channel | |
| samp2 = wav2[index : index + sr1, 0] | |
| diff = get_diff(samp1, samp2) | |
| # make aligned track 2 | |
| if diff > 0: | |
| wav2_aligned = np.append(np.zeros((diff, 1)), wav2_org, axis=0) | |
| elif diff < 0: | |
| wav2_aligned = wav2_org[-diff:] | |
| else: | |
| wav2_aligned = wav2_org | |
| return wav2_aligned | |
| def load_audio(audio_file): | |
| wav, sr = librosa.load(audio_file, sr=44100, mono=False) | |
| if wav.ndim == 1: | |
| wav = np.asfortranarray([wav,wav]) | |
| return wav | |
| def rerun_mp3(audio_file): | |
| with audioread.audio_open(audio_file) as f: | |
| track_length = int(f.duration) | |
| return track_length | |