mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-28 01:10:56 +01:00
593 lines
23 KiB
Python
593 lines
23 KiB
Python
import os
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import librosa
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import torch
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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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import threading
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import copy
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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, mp, multithreading):
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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if multithreading:
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def run_thread(**kwargs):
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global spec_left_mt
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spec_left_mt = 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_mt, spec_right])
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else:
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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 convert_channels(spec, mp, band):
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cc = mp.param['band'][band].get('convert_channels')
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if mp.param['reverse']:
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spec_left = np.flip(spec[0])
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spec_right = np.flip(spec[1])
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elif mp.param['mid_side_b'] or 'mid_side_b' == cc:
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spec_left = np.add(spec[0], spec[1] * .5)
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spec_right = np.subtract(spec[1], spec[0] * .5)
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elif mp.param['mid_side_b2'] or 'mid_side_b2' == cc:
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spec_left = np.add(spec[1], spec[0] * .5)
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spec_right = np.subtract(spec[0], spec[1] * .5)
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elif 'mid_side_c' == cc:
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spec_left = np.add(spec[0], spec[1] * .25)
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spec_right = np.subtract(spec[1], spec[0] * .25)
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elif mp.param['mid_side'] or 'mid_side' == cc:
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spec_left = np.add(spec[0], spec[1]) / 2
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spec_right = np.subtract(spec[0], spec[1])
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elif mp.param['stereo_n']:
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spec_left = np.add(spec[0], spec[1] * .25) / 0.9375
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spec_right = np.add(spec[1], spec[0] * .25) / 0.9375
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else:
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return spec
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return np.asfortranarray([spec_left, spec_right])
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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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s = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
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#if 'flip' in mp.param['band'][d]:
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# s = np.flip(s, 1)
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spec_c[:, offset:offset+h, :l] = s
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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 *= get_lp_filter_mask(spec_c.shape[1], 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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'''
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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 trim_specs(a, b):
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l = min([a.shape[2], b.shape[2]])
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return a[:,:,:l], b[:,:,: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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# the cache will be common for some model types
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mpp2 = copy.deepcopy(mp.param)
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mpp2.update(dict.fromkeys(['mid_side', 'mid_side_b', 'mid_side_b2', 'reverse'], False))
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for d in mpp2['band']:
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mpp2['band'][d]['convert_channels'] = ''
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cache_dir = 'mp{}'.format(hashlib.sha1(json.dumps(mpp2, 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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'''
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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, False)
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y_spec_s[d] = wave_to_spectrogram(y_wave[d], bp['hl'], bp['n_fft'], mp, False)
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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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'''
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X_spec_m = spec_from_file(mix_path, mp)
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y_spec_m = spec_from_file(inst_path, mp)
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X_spec_m, y_spec_m = trim_specs(X_spec_m, y_spec_m)
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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, mp, band, multithreading):
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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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cc = mp.param['band'][band].get('convert_channels')
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if multithreading:
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def run_thread(**kwargs):
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global wave_left_mt
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wave_left_mt = 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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wave_left = wave_left_mt
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else:
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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 mp.param['reverse']:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mp.param['mid_side_b'] or 'mid_side_b' == cc:
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return np.asfortranarray([np.subtract(wave_left / 1.25, .4 * wave_right), np.add(wave_right / 1.25, .4 * wave_left)])
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elif mp.param['mid_side_b2'] or 'mid_side_b2' == cc:
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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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elif 'mid_side_c' == cc:
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return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])
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elif mp.param['mid_side'] or 'mid_side' == cc:
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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 mp.param['stereo_n']:
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return np.asfortranarray([np.subtract(wave_left, wave_right * .25), np.subtract(wave_right, wave_left * .25)])
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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.zeros(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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#if 'flip' in mp.param['band'][d]:
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# spec_s[:, bp['crop_start']:bp['crop_stop'], :] = np.flip(spec_m[:, offset:offset+h, :], 1)
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#else:
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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: # high-end
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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 *= get_hp_filter_mask(spec_s.shape[1], 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, d, False)
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else:
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wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, False))
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else:
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sr = mp.param['band'][d+1]['sr']
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if d == 1: # low-end
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spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
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wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp, d, False), bp['sr'], sr, res_type="sinc_fastest")
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else: # mid
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spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
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spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
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wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, False))
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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 cmb_spectrogram_to_wave_ffmpeg(spec_m, mp, tmp_basename, extra_bins_h=None, extra_bins=None):
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import subprocess
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bands_n = len(mp.param['band'])
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offset = 0
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ffmprc = {}
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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.zeros(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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tmp_wav = os.path.join('tmp', '{}_cstw_b{}_sr{}'.format(tmp_basename, d, str(bp['sr']) + '.wav'))
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tmp_wav2 = os.path.join('tmp', '{}_cstw_b{}_sr{}'.format(tmp_basename, d, str(mp.param['sr']) + '.wav'))
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offset += h
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if d == bands_n: # high-end
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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 *= get_hp_filter_mask(spec_s.shape[1], 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, d, True)
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else:
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wave = spectrogram_to_wave(spec_s, bp['hl'], mp, d, True)
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else:
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if d == 1: # low-end
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spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
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else: # mid
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spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
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spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
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sf.write(tmp_wav, spectrogram_to_wave(spec_s, bp['hl'], mp, d, True).T, bp['sr'])
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ffmprc[d] = subprocess.Popen(['ffmpeg', '-hide_banner', '-loglevel', 'panic', '-y', '-i', tmp_wav, '-ar', str(mp.param['sr']), '-ac', '2', '-c:a', 'pcm_s16le', tmp_wav2])
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for s in ffmprc:
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ffmprc[s].communicate()
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for d in range(bands_n - 1, 0, -1):
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os.remove(os.path.join('tmp', f'{tmp_basename}_cstw_b{d}_sr' + str(mp.param['band'][d]['sr']) + '.wav'))
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tmp_wav2 = os.path.join('tmp', f'{tmp_basename}_cstw_b{d}_sr' + str(mp.param['sr']) + '.wav')
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wave2, _ = librosa.load(tmp_wav2, mp.param['sr'], False, dtype=np.float32, res_type="sinc_fastest")
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os.remove(tmp_wav2)
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wave = np.add(wave, wave2)
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return wave.T
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'''
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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):
|
|
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 get_lp_filter_mask(bins_n, bin_start, bin_stop):
|
|
mask = np.concatenate([
|
|
np.ones((bin_start - 1, 1)),
|
|
np.linspace(1, 0, bin_stop - bin_start + 1)[:, None],
|
|
np.zeros((bins_n - bin_stop, 1))
|
|
], axis=0)
|
|
|
|
return mask
|
|
|
|
|
|
def get_hp_filter_mask(bins_n, bin_start, bin_stop):
|
|
mask = np.concatenate([
|
|
np.zeros((bin_stop + 1, 1)),
|
|
np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None],
|
|
np.ones((bins_n - bin_start - 2, 1))
|
|
], axis=0)
|
|
|
|
return mask
|
|
|
|
|
|
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 adjust_aggr(mask, params):
|
|
aggr = params.get('aggr_value', 0.0)
|
|
|
|
if aggr != 0:
|
|
if params.get('is_vocal_model'):
|
|
aggr = 1 - aggr
|
|
|
|
aggr_l = aggr_r = aggr
|
|
|
|
if params['aggr_correction'] is not None:
|
|
aggr_l += params['aggr_correction']['left']
|
|
aggr_r += params['aggr_correction']['right']
|
|
|
|
mask[:, 0, :params['aggr_split_bin']] = torch.pow(mask[:, 0, :params['aggr_split_bin']], 1 + aggr_l / 3)
|
|
mask[:, 0, params['aggr_split_bin']:] = torch.pow(mask[:, 0, params['aggr_split_bin']:], 1 + aggr_l)
|
|
|
|
mask[:, 1, :params['aggr_split_bin']] = torch.pow(mask[:, 1, :params['aggr_split_bin']], 1 + aggr_r / 3)
|
|
mask[:, 1, params['aggr_split_bin']:] = torch.pow(mask[:, 1, params['aggr_split_bin']:], 1 + aggr_r)
|
|
|
|
return mask
|
|
|
|
|
|
def ensembling(a, specs, sr=44100):
|
|
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]
|
|
freq_to_bin = 2 * spec.shape[1] / sr
|
|
|
|
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)
|
|
if 'mul' == a:
|
|
s1 = specs[i] * spec
|
|
s2 = .5 * (specs[i] + spec)
|
|
spec = np.divide(s1, s2, out=np.zeros_like(s1), where=s2!=0)
|
|
if 'crossover' == a:
|
|
bs = int(500 * freq_to_bin)
|
|
be = int(14000 * freq_to_bin)
|
|
spec = specs[i] * get_lp_filter_mask(spec.shape[1], bs, be) + spec * get_hp_filter_mask(spec.shape[1], be, bs)
|
|
if 'min_mag_co' == a:
|
|
specs[i] += specs[i] * get_hp_filter_mask(spec.shape[1], int(14000 * freq_to_bin), int(4000 * freq_to_bin))
|
|
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
|
|
|
|
return spec
|
|
|
|
|
|
def spec_from_file(filename, mp):
|
|
wave, 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, _ = librosa.load(
|
|
filename, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
|
|
|
|
if len(wave.shape) == 1: # mono to stereo
|
|
wave = np.array([wave, wave])
|
|
else: # lower bands
|
|
wave = librosa.resample(wave, mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
|
|
|
|
spec[d] = wave_to_spectrogram(wave, bp['hl'], bp['n_fft'], mp, False)
|
|
spec[d] = convert_channels(spec[d], mp, d)
|
|
|
|
return combine_spectrograms(spec, mp)
|
|
|
|
|
|
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', 'mul', 'crossover', 'min_mag_co', '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.')
|
|
|
|
specs = {}
|
|
mp = ModelParameters(args.model_params)
|
|
|
|
for i in range(len(args.input)):
|
|
specs[i] = spec_from_file(args.input[i], mp)
|
|
|
|
specs[0], specs[1] = trim_specs(specs[0], specs[1])
|
|
|
|
if args.algorithm == 'deep':
|
|
d_spec = np.where(np.abs(specs[0]) <= np.abs(specs[1]), specs[0], specs[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'):
|
|
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'])
|
|
|
|
#print('Total time: {0:.{1}f}s'.format(time.time() - start_time, 1))
|
|
|
|
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']}")
|