mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 09:21:03 +01:00
473 lines
18 KiB
Python
473 lines
18 KiB
Python
import os
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import librosa
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import numpy as np
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import soundfile as sf
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import math
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import json
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import hashlib
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from tqdm import tqdm
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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_freq = (h2_shape[2] - h1_shape[2]) // 2
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# e_freq = s_freq + h1_shape[2]
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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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# lowpass fiter
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if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
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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']): # high-end 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: # lower bands
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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: # higher
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if extra_bins_h: # if --high_end_process bypass
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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: # lower
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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: # mid
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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.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
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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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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']): # high-end 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: # mono to stereo
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wave[d] = np.array([wave[d], wave[d]])
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else: # lower bands
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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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|
|
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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]))
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else:
|
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specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
|
|
v_spec = specs[0] - specs[1]
|
|
|
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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'])
|
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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))
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