import os,librosa
import numpy as np
import soundfile as sf
from tqdm import tqdm
import json,math ,hashlib
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, 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 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 mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
if min_range < fade_size * 2:
raise ValueError('min_range must be >= fade_area * 2')
mag = mag.copy()
idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
uninformative = np.where(ends - starts > min_range)[0]
if len(uninformative) > 0:
starts = starts[uninformative]
ends = ends[uninformative]
old_e = None
for s, e in zip(starts, ends):
if old_e is not None and s - old_e < fade_size:
s = old_e - fade_size * 2
if s != 0:
weight = np.linspace(0, 1, fade_size)
mag[:, :, s:s + fade_size] += weight * ref[:, :, s:s + fade_size]
else:
s -= fade_size
if e != mag.shape[2]:
weight = np.linspace(1, 0, fade_size)
mag[:, :, e - fade_size:e] += weight * ref[:, :, e - fade_size:e]
else:
e += fade_size
mag[:, :, s + fade_size:e - fade_size] += ref[:, :, s + fade_size:e - fade_size]
old_e = e
return mag
def align_wave_head_and_tail(a, b):
l = min([a[0].size, b[0].size])
return a[:l,:l], b[:l,:l]
def cache_or_load(mix_path, inst_path, mp):
mix_basename = os.path.splitext(os.path.basename(mix_path))[0]
inst_basename = os.path.splitext(os.path.basename(inst_path))[0]
cache_dir = 'mph{}'.format(hashlib.sha1(json.dumps(mp.param, sort_keys=True).encode('utf-8')).hexdigest())
mix_cache_dir = os.path.join('cache', cache_dir)
inst_cache_dir = os.path.join('cache', 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_spec_m = np.load(mix_cache_path)
y_spec_m = np.load(inst_cache_path)
else:
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
for d in range(len(mp.param['band']), 0, -1):
bp = mp.param['band'][d]
if d == len(mp.param['band']): # high-end band
X_wave[d], _ = librosa.load(
mix_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
y_wave[d], _ = librosa.load(
inst_path, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
else: # lower bands
X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
y_wave[d] = librosa.resample(y_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
X_wave[d], y_wave[d] = align_wave_head_and_tail(X_wave[d], y_wave[d])
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'])
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'])
del X_wave, y_wave
X_spec_m = combine_spectrograms(X_spec_s, mp)
y_spec_m = combine_spectrograms(y_spec_s, mp)
if X_spec_m.shape != y_spec_m.shape:
raise ValueError('The combined spectrograms are different: ' + mix_path)
_, ext = os.path.splitext(mix_path)
np.save(mix_cache_path, X_spec_m)
np.save(inst_cache_path, y_spec_m)
return X_spec_m, y_spec_m
def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
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):
wave_band = {}
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="sinc_fastest")
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.core.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
wave = librosa.core.resample(wave2, bp['sr'], sr,res_type='scipy')
return wave.T
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 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_mag' == a:
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
if 'max_mag' == a:
spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
return spec
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])
if __name__ == "__main__":
import cv2
import sys
import time
import argparse
from model_param_init import ModelParameters
p = argparse.ArgumentParser()
p.add_argument('--algorithm', '-a', type=str, choices=['invert', 'invert_p', 'min_mag', 'max_mag', 'deep', 'align'], default='min_mag')
p.add_argument('--model_params', '-m', type=str, default=os.path.join('modelparams', '1band_sr44100_hl512.json'))
p.add_argument('--output_name', '-o', type=str, default='output')
p.add_argument('--vocals_only', '-v', action='store_true')
p.add_argument('input', nargs='+')
args = p.parse_args()
start_time = time.time()
if args.algorithm.startswith('invert') and len(args.input) != 2:
raise ValueError('There should be two input files.')
if not args.algorithm.startswith('invert') and len(args.input) < 2:
raise ValueError('There must be at least two input files.')
wave, specs = {}, {}
mp = ModelParameters(args.model_params)
for i in range(len(args.input)):
spec = {}
for d in range(len(mp.param['band']), 0, -1):
bp = mp.param['band'][d]
if d == len(mp.param['band']): # high-end band
wave[d], _ = librosa.load(
args.input[i], bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
if len(wave[d].shape) == 1: # mono to stereo
wave[d] = np.array([wave[d], wave[d]])
else: # lower bands
wave[d] = librosa.resample(wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
spec[d] = wave_to_spectrogram(wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
specs[i] = combine_spectrograms(spec, mp)
del wave
if args.algorithm == 'deep':
d_spec = np.where(np.abs(specs[0]) <= np.abs(spec[1]), specs[0], spec[1])
v_spec = d_spec - specs[1]
sf.write(os.path.join('{}.wav'.format(args.output_name)), cmb_spectrogram_to_wave(v_spec, mp), mp.param['sr'])
if args.algorithm.startswith('invert'):
ln = min([specs[0].shape[2], specs[1].shape[2]])
specs[0] = specs[0][:,:,:ln]
specs[1] = specs[1][:,:,:ln]
if 'invert_p' == args.algorithm:
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]
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']}")
#print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))