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import os,librosa |
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import numpy as np |
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import soundfile as sf |
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from tqdm import tqdm |
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import json,math ,hashlib |
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def crop_center(h1, h2): |
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h1_shape = h1.size() |
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h2_shape = h2.size() |
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if h1_shape[3] == h2_shape[3]: |
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return h1 |
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elif h1_shape[3] < h2_shape[3]: |
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raise ValueError('h1_shape[3] must be greater than h2_shape[3]') |
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s_time = (h1_shape[3] - h2_shape[3]) // 2 |
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e_time = s_time + h2_shape[3] |
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h1 = h1[:, :, :, s_time:e_time] |
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return h1 |
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def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False): |
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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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elif mid_side_b2: |
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5)) |
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5)) |
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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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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, mid_side_b2=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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elif mid_side_b2: |
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5)) |
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5)) |
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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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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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offset = 0 |
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bands_n = len(mp.param['band']) |
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for d in range(1, bands_n + 1): |
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h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start'] |
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spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l] |
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offset += h |
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if offset > mp.param['bins']: |
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raise ValueError('Too much bins') |
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if mp.param['pre_filter_start'] > 0: |
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if bands_n == 1: |
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spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop']) |
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else: |
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gp = 1 |
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for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']): |
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g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0) |
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gp = g |
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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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if np.iscomplexobj(spec): |
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y = np.abs(spec) |
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else: |
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y = spec |
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y = np.log10(y ** 2 + 1e-8) |
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elif mode == 'phase': |
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if np.iscomplexobj(spec): |
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y = np.angle(spec) |
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else: |
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y = spec |
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y -= y.min() |
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y *= 255 / y.max() |
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img = np.uint8(y) |
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if y.ndim == 3: |
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img = img.transpose(1, 2, 0) |
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img = np.concatenate([ |
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np.max(img, axis=2, keepdims=True), img |
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], axis=2) |
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return img |
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def reduce_vocal_aggressively(X, y, softmask): |
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v = X - y |
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y_mag_tmp = np.abs(y) |
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v_mag_tmp = np.abs(v) |
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v_mask = v_mag_tmp > y_mag_tmp |
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y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf) |
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return y_mag * np.exp(1.j * np.angle(y)) |
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def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32): |
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if min_range < fade_size * 2: |
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raise ValueError('min_range must be >= fade_area * 2') |
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mag = mag.copy() |
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idx = np.where(ref.mean(axis=(0, 1)) < thres)[0] |
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starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0]) |
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ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1]) |
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uninformative = np.where(ends - starts > min_range)[0] |
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if len(uninformative) > 0: |
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starts = starts[uninformative] |
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ends = ends[uninformative] |
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old_e = None |
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for s, e in zip(starts, ends): |
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if old_e is not None and s - old_e < fade_size: |
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s = old_e - fade_size * 2 |
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if s != 0: |
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weight = np.linspace(0, 1, fade_size) |
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mag[:, :, s:s + fade_size] += weight * ref[:, :, s:s + fade_size] |
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else: |
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s -= fade_size |
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if e != mag.shape[2]: |
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weight = np.linspace(1, 0, fade_size) |
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mag[:, :, e - fade_size:e] += weight * ref[:, :, e - fade_size:e] |
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else: |
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e += fade_size |
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mag[:, :, s + fade_size:e - fade_size] += ref[:, :, s + fade_size:e - fade_size] |
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old_e = e |
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return mag |
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def align_wave_head_and_tail(a, b): |
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l = min([a[0].size, b[0].size]) |
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return a[:l,:l], b[:l,:l] |
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def cache_or_load(mix_path, inst_path, mp): |
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mix_basename = os.path.splitext(os.path.basename(mix_path))[0] |
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inst_basename = os.path.splitext(os.path.basename(inst_path))[0] |
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cache_dir = 'mph{}'.format(hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode('utf-8')).hexdigest()) |
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mix_cache_dir = os.path.join('cache', cache_dir) |
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inst_cache_dir = os.path.join('cache', cache_dir) |
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os.makedirs(mix_cache_dir, exist_ok=True) |
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os.makedirs(inst_cache_dir, exist_ok=True) |
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mix_cache_path = os.path.join(mix_cache_dir, mix_basename + '.npy') |
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inst_cache_path = os.path.join(inst_cache_dir, inst_basename + '.npy') |
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if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path): |
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X_spec_m = np.load(mix_cache_path) |
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y_spec_m = np.load(inst_cache_path) |
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else: |
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} |
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for d in range(len(mp.param['band']), 0, -1): |
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bp = mp.param['band'][d] |
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if d == len(mp.param['band']): |
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X_wave[d], _ = librosa.load( |
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mix_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) |
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y_wave[d], _ = librosa.load( |
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inst_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) |
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else: |
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X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) |
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y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) |
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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['mid_side_b2'], 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['mid_side_b2'], mp.param['reverse']) |
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del X_wave, y_wave |
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X_spec_m = combine_spectrograms(X_spec_s, mp) |
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y_spec_m = combine_spectrograms(y_spec_s, mp) |
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if X_spec_m.shape != y_spec_m.shape: |
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raise ValueError('The combined spectrograms are different: ' + mix_path) |
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_, ext = os.path.splitext(mix_path) |
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np.save(mix_cache_path, X_spec_m) |
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np.save(inst_cache_path, y_spec_m) |
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return X_spec_m, y_spec_m |
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def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, 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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elif mid_side_b2: |
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return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)]) |
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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, mid_side_b2): |
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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 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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elif mid_side_b2: |
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return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)]) |
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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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offset = 0 |
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for d in range(1, bands_n + 1): |
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bp = mp.param['band'][d] |
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spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex) |
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h = bp['crop_stop'] - bp['crop_start'] |
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spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :] |
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offset += h |
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if d == bands_n: |
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if extra_bins_h: |
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max_bin = bp['n_fft'] // 2 |
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spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :] |
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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['mid_side_b2'], mp.param['reverse']) |
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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['mid_side_b2'], mp.param['reverse'])) |
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else: |
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sr = mp.param['band'][d+1]['sr'] |
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if d == 1: |
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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['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type="sinc_fastest") |
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else: |
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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['mid_side_b2'], mp.param['reverse'])) |
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wave = librosa.core.resample(wave2, bp['sr'], sr,res_type='scipy') |
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return wave.T |
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def fft_lp_filter(spec, bin_start, bin_stop): |
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g = 1.0 |
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for b in range(bin_start, bin_stop): |
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g -= 1 / (bin_stop - bin_start) |
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spec[:, b, :] = g * spec[:, b, :] |
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spec[:, bin_stop:, :] *= 0 |
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return spec |
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def fft_hp_filter(spec, bin_start, bin_stop): |
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g = 1.0 |
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for b in range(bin_start, bin_stop, -1): |
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g -= 1 / (bin_start - bin_stop) |
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spec[:, b, :] = g * spec[:, b, :] |
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spec[:, 0:bin_stop+1, :] *= 0 |
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return spec |
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def mirroring(a, spec_m, input_high_end, mp): |
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if 'mirroring' == a: |
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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) |
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mirror = mirror * np.exp(1.j * np.angle(input_high_end)) |
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return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror) |
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if 'mirroring2' == a: |
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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) |
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mi = np.multiply(mirror, input_high_end * 1.7) |
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return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi) |
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def ensembling(a, specs): |
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for i in range(1, len(specs)): |
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if i == 1: |
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spec = specs[0] |
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ln = min([spec.shape[2], specs[i].shape[2]]) |
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spec = spec[:,:,:ln] |
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specs[i] = specs[i][:,:,:ln] |
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if 'min_mag' == a: |
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spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec) |
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if 'max_mag' == a: |
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spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec) |
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return spec |
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def stft(wave, nfft, hl): |
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wave_left = np.asfortranarray(wave[0]) |
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wave_right = np.asfortranarray(wave[1]) |
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spec_left = librosa.stft(wave_left, nfft, hop_length=hl) |
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spec_right = librosa.stft(wave_right, nfft, hop_length=hl) |
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spec = np.asfortranarray([spec_left, spec_right]) |
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return spec |
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def istft(spec, hl): |
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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=hl) |
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wave_right = librosa.istft(spec_right, hop_length=hl) |
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wave = np.asfortranarray([wave_left, wave_right]) |
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if __name__ == "__main__": |
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import cv2 |
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import sys |
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import time |
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import argparse |
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from model_param_init import ModelParameters |
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p = argparse.ArgumentParser() |
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p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'invert_p', 'min_mag', 'max_mag', 'deep', 'align'], default='min_mag') |
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p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json')) |
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p.add_argument('--output_name', '-o', type=str, default='output') |
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p.add_argument('--vocals_only', '-v', action='store_true') |
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p.add_argument('input', nargs='+') |
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args = p.parse_args() |
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start_time = time.time() |
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if args.algorithm.startswith('invert') and len(args.input) != 2: |
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raise ValueError('There should be two input files.') |
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if not args.algorithm.startswith('invert') and len(args.input) < 2: |
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raise ValueError('There must be at least two input files.') |
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wave, specs = {}, {} |
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mp = ModelParameters(args.model_params) |
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for i in range(len(args.input)): |
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spec = {} |
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for d in range(len(mp.param['band']), 0, -1): |
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bp = mp.param['band'][d] |
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if d == len(mp.param['band']): |
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wave[d], _ = librosa.load( |
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args.input[i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) |
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if len(wave[d].shape) == 1: |
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wave[d] = np.array([wave[d], wave[d]]) |
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else: |
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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['mid_side_b2'], mp.param['reverse']) |
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specs[i] = combine_spectrograms(spec, mp) |
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del wave |
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if args.algorithm == 'deep': |
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d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1]) |
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v_spec = d_spec - specs[1] |
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sf.write(os.path.join('{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr']) |
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if args.algorithm.startswith('invert'): |
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ln = min([specs[0].shape[2], specs[1].shape[2]]) |
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specs[0] = specs[0][:,:,:ln] |
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specs[1] = specs[1][:,:,:ln] |
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|
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if 'invert_p' == args.algorithm: |
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X_mag = np.abs(specs[0]) |
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y_mag = np.abs(specs[1]) |
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max_mag = np.where(X_mag >= y_mag, X_mag, y_mag) |
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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] |
|
|
|
if not args.vocals_only: |
|
X_mag = np.abs(specs[0]) |
|
y_mag = np.abs(specs[1]) |
|
v_mag = np.abs(v_spec) |
|
|
|
X_image = spectrogram_to_image(X_mag) |
|
y_image = spectrogram_to_image(y_mag) |
|
v_image = spectrogram_to_image(v_mag) |
|
|
|
cv2.imwrite('{}_X.png'.format(args.output_name), X_image) |
|
cv2.imwrite('{}_y.png'.format(args.output_name), y_image) |
|
cv2.imwrite('{}_v.png'.format(args.output_name), v_image) |
|
|
|
sf.write('{}_X.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[0], mp), mp.param['sr']) |
|
sf.write('{}_y.wav'.format(args.output_name), cmb_spectrogram_to_wave(specs[1], mp), mp.param['sr']) |
|
|
|
sf.write('{}_v.wav'.format(args.output_name), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr']) |
|
else: |
|
if not args.algorithm == 'deep': |
|
sf.write(os.path.join('ensembled','{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(ensembling(args.algorithm, specs), mp), mp.param['sr']) |
|
|
|
if args.algorithm == 'align': |
|
|
|
trackalignment = [ |
|
{ |
|
'file1':'"{}"'.format(args.input[0]), |
|
'file2':'"{}"'.format(args.input[1]) |
|
} |
|
] |
|
|
|
for i,e in tqdm(enumerate(trackalignment), desc="Performing Alignment..."): |
|
os.system(f"python lib/align_tracks.py {e['file1']} {e['file2']}") |
|
|
|
|
|
|
