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)