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import os,sys,torch,warnings,pdb |
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warnings.filterwarnings("ignore") |
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import librosa |
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import importlib |
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import numpy as np |
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import hashlib , math |
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from tqdm import tqdm |
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from uvr5_pack.lib_v5 import spec_utils |
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from uvr5_pack.utils import _get_name_params,inference |
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from uvr5_pack.lib_v5.model_param_init import ModelParameters |
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from scipy.io import wavfile |
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class _audio_pre_(): |
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def __init__(self, model_path,device,is_half): |
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self.model_path = model_path |
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self.device = device |
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self.data = { |
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'postprocess': False, |
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'tta': False, |
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'window_size': 512, |
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'agg': 10, |
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'high_end_process': 'mirroring', |
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} |
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nn_arch_sizes = [ |
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31191, |
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33966,61968, 123821, 123812, 537238 |
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] |
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self.nn_architecture = list('{}KB'.format(s) for s in nn_arch_sizes) |
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model_size = math.ceil(os.stat(model_path ).st_size / 1024) |
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nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size))) |
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nets = importlib.import_module('uvr5_pack.lib_v5.nets' + f'_{nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None) |
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model_hash = hashlib.md5(open(model_path,'rb').read()).hexdigest() |
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param_name ,model_params_d = _get_name_params(model_path , model_hash) |
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mp = ModelParameters(model_params_d) |
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model = nets.CascadedASPPNet(mp.param['bins'] * 2) |
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cpk = torch.load( model_path , map_location='cpu') |
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model.load_state_dict(cpk) |
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model.eval() |
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if(is_half==True):model = model.half().to(device) |
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else:model = model.to(device) |
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self.mp = mp |
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self.model = model |
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def _path_audio_(self, music_file ,ins_root=None,vocal_root=None): |
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if(ins_root is None and vocal_root is None):return "No save root." |
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name=os.path.basename(music_file) |
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if(ins_root is not None):os.makedirs(ins_root, exist_ok=True) |
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if(vocal_root is not None):os.makedirs(vocal_root , exist_ok=True) |
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X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {} |
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bands_n = len(self.mp.param['band']) |
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for d in range(bands_n, 0, -1): |
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bp = self.mp.param['band'][d] |
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if d == bands_n: |
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X_wave[d], _ = librosa.core.load( |
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music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type']) |
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if X_wave[d].ndim == 1: |
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X_wave[d] = np.asfortranarray([X_wave[d], X_wave[d]]) |
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else: |
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X_wave[d] = librosa.core.resample(X_wave[d+1], self.mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type']) |
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X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], self.mp.param['mid_side'], self.mp.param['mid_side_b2'], self.mp.param['reverse']) |
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if d == bands_n and self.data['high_end_process'] != 'none': |
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input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + ( self.mp.param['pre_filter_stop'] - self.mp.param['pre_filter_start']) |
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input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :] |
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X_spec_m = spec_utils.combine_spectrograms(X_spec_s, self.mp) |
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aggresive_set = float(self.data['agg']/100) |
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aggressiveness = {'value': aggresive_set, 'split_bin': self.mp.param['band'][1]['crop_stop']} |
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with torch.no_grad(): |
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pred, X_mag, X_phase = inference(X_spec_m,self.device,self.model, aggressiveness,self.data) |
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if self.data['postprocess']: |
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pred_inv = np.clip(X_mag - pred, 0, np.inf) |
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pred = spec_utils.mask_silence(pred, pred_inv) |
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y_spec_m = pred * X_phase |
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v_spec_m = X_spec_m - y_spec_m |
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if (ins_root is not None): |
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if self.data['high_end_process'].startswith('mirroring'): |
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input_high_end_ = spec_utils.mirroring(self.data['high_end_process'], y_spec_m, input_high_end, self.mp) |
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp,input_high_end_h, input_high_end_) |
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else: |
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, self.mp) |
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print ('%s instruments done'%name) |
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wavfile.write(os.path.join(ins_root, 'instrument_{}.wav'.format(name) ), self.mp.param['sr'], (np.array(wav_instrument)*32768).astype("int16")) |
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if (vocal_root is not None): |
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if self.data['high_end_process'].startswith('mirroring'): |
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input_high_end_ = spec_utils.mirroring(self.data['high_end_process'], v_spec_m, input_high_end, self.mp) |
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp, input_high_end_h, input_high_end_) |
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else: |
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp) |
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print ('%s vocals done'%name) |
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wavfile.write(os.path.join(vocal_root , 'vocal_{}.wav'.format(name) ), self.mp.param['sr'], (np.array(wav_vocals)*32768).astype("int16")) |
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if __name__ == '__main__': |
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device = 'cuda' |
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is_half=True |
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model_path='uvr5_weights/2_HP-UVR.pth' |
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pre_fun = _audio_pre_(model_path=model_path,device=device,is_half=True) |
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audio_path = '神女劈观.aac' |
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save_path = 'opt' |
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pre_fun._path_audio_(audio_path , save_path,save_path) |
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