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