import os import librosa import numpy as np import soundfile as sf 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_freq = (h2_shape[2] - h1_shape[2]) // 2 # e_freq = s_freq + h1_shape[2] 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 wave_to_spectrogram(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 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 aggressively_remove_vocal(X, y, weight): X_mag = np.abs(X) y_mag = np.abs(y) # v_mag = np.abs(X_mag - y_mag) v_mag = X_mag - y_mag v_mag *= v_mag > y_mag y_mag = np.clip(y_mag - v_mag * weight, 0, np.inf) return y_mag * np.exp(1.j * np.angle(y)) def merge_artifacts(y_mask, thres=0.05, min_range=64, fade_size=32): 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 return y_mask def align_wave_head_and_tail(a, b, sr): a, _ = librosa.effects.trim(a) b, _ = librosa.effects.trim(b) a_mono = a[:, :sr * 4].sum(axis=0) b_mono = b[:, :sr * 4].sum(axis=0) a_mono -= a_mono.mean() b_mono -= b_mono.mean() offset = len(a_mono) - 1 delay = np.argmax(np.correlate(a_mono, b_mono, 'full')) - offset if delay > 0: a = a[:, delay:] else: b = b[:, np.abs(delay):] if a.shape[1] < b.shape[1]: b = b[:, :a.shape[1]] else: a = a[:, :b.shape[1]] return a, b def cache_or_load(mix_path, inst_path, sr, hop_length, n_fft): mix_basename = os.path.splitext(os.path.basename(mix_path))[0] inst_basename = os.path.splitext(os.path.basename(inst_path))[0] cache_dir = 'sr{}_hl{}_nf{}'.format(sr, hop_length, n_fft) mix_cache_dir = os.path.join(os.path.dirname(mix_path), cache_dir) inst_cache_dir = os.path.join(os.path.dirname(inst_path), cache_dir) os.makedirs(mix_cache_dir, exist_ok=True) os.makedirs(inst_cache_dir, exist_ok=True) mix_cache_path = os.path.join(mix_cache_dir, mix_basename + '.npy') inst_cache_path = os.path.join(inst_cache_dir, inst_basename + '.npy') if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path): X = np.load(mix_cache_path) y = np.load(inst_cache_path) else: X, _ = librosa.load( mix_path, sr, False, dtype=np.float32, res_type='kaiser_fast') y, _ = librosa.load( inst_path, sr, False, dtype=np.float32, res_type='kaiser_fast') X, y = align_wave_head_and_tail(X, y, sr) X = wave_to_spectrogram(X, hop_length, n_fft) y = wave_to_spectrogram(y, hop_length, n_fft) np.save(mix_cache_path, X) np.save(inst_cache_path, y) return X, y, mix_cache_path, inst_cache_path def spectrogram_to_wave(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 if __name__ == "__main__": import cv2 import sys bins = 2048 // 2 + 1 freq_to_bin = 2 * bins / 44100 unstable_bins = int(200 * freq_to_bin) stable_bins = int(22050 * freq_to_bin) reduction_weight = np.concatenate([ np.linspace(0, 1, unstable_bins, dtype=np.float32)[:, None], np.linspace(1, 0, stable_bins - unstable_bins, dtype=np.float32)[:, None], np.zeros((bins - stable_bins, 1)) ], axis=0) * 0.2 X, _ = librosa.load( sys.argv[1], 44100, False, dtype=np.float32, res_type='kaiser_fast') y, _ = librosa.load( sys.argv[2], 44100, False, dtype=np.float32, res_type='kaiser_fast') X, y = align_wave_head_and_tail(X, y, 44100) X_spec = wave_to_spectrogram(X, 1024, 2048) y_spec = wave_to_spectrogram(y, 1024, 2048) X_mag = np.abs(X_spec) y_mag = np.abs(y_spec) # v_mag = np.abs(X_mag - y_mag) v_mag = X_mag - y_mag v_mag *= v_mag > y_mag # y_mag = np.clip(y_mag - v_mag * reduction_weight, 0, np.inf) y_spec = y_mag * np.exp(1j * np.angle(y_spec)) v_spec = v_mag * np.exp(1j * np.angle(X_spec)) X_image = spectrogram_to_image(X_mag) y_image = spectrogram_to_image(y_mag) v_image = spectrogram_to_image(v_mag) cv2.imwrite('test_X.jpg', X_image) cv2.imwrite('test_y.jpg', y_image) cv2.imwrite('test_v.jpg', v_image) sf.write('test_X.wav', spectrogram_to_wave(X_spec).T, 44100) sf.write('test_y.wav', spectrogram_to_wave(y_spec).T, 44100) sf.write('test_v.wav', spectrogram_to_wave(v_spec).T, 44100)