109 lines
5.5 KiB
Python
109 lines
5.5 KiB
Python
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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# Processing Options
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'postprocess': False,
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'tta': False,
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# Constants
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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, # default
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33966,61968, 123821, 123812, 537238 # custom
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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):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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# print(bands_n)
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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: # high-end band
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X_wave[d], _ = librosa.core.load(#理论上librosa读取可能对某些音频有bug，应该上ffmpeg读取，但是太麻烦了弃坑
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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: # lower bands
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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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# Stft of wave source
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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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# pdb.set_trace()
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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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# Postprocess
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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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