import os, sys, torch, warnings, pdb now_dir = os.getcwd() sys.path.append(now_dir) from json import load as ll 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 import soundfile as sf from uvr5_pack.lib_v5.nets_new import CascadedNet from uvr5_pack.lib_v5 import nets_61968KB as nets class _audio_pre_: def __init__(self, agg, 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": agg, "high_end_process": "mirroring", } mp = ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v2.json") 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: 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, format="flac"): 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( # 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 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) sf.write( os.path.join( ins_root, "instrument_{}_{}.{}".format(name, self.data["agg"], format), ), (np.array(wav_instrument) * 32768).astype("int16"), self.mp.param["sr"], ) # 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) sf.write( os.path.join( vocal_root, "vocal_{}_{}.{}".format(name, self.data["agg"], format) ), (np.array(wav_vocals) * 32768).astype("int16"), self.mp.param["sr"], ) class _audio_pre_new: def __init__(self, agg, 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": agg, "high_end_process": "mirroring", } mp = ModelParameters("uvr5_pack/lib_v5/modelparams/4band_v3.json") nout = 64 if "DeReverb" in model_path else 48 model = CascadedNet(mp.param["bins"] * 2, nout) cpk = torch.load(model_path, map_location="cpu") model.load_state_dict(cpk) model.eval() if is_half: model = model.half().to(device) else: model = model.to(device) self.mp = mp self.model = model def _path_audio_( self, music_file, vocal_root=None, ins_root=None, format="flac" ): # 3个VR模型vocal和ins是反的 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( # 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 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) sf.write( os.path.join( ins_root, "main_vocal_{}_{}.{}".format(name, self.data["agg"], format), ), (np.array(wav_instrument) * 32768).astype("int16"), self.mp.param["sr"], ) # 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) sf.write( os.path.join( vocal_root, "others_{}_{}.{}".format(name, self.data["agg"], format) ), (np.array(wav_vocals) * 32768).astype("int16"), self.mp.param["sr"], ) if __name__ == "__main__": device = "cuda" is_half = True # model_path = "uvr5_weights/2_HP-UVR.pth" # model_path = "uvr5_weights/VR-DeEchoDeReverb.pth" # model_path = "uvr5_weights/VR-DeEchoNormal.pth" model_path = "uvr5_weights/DeEchoNormal.pth" # pre_fun = _audio_pre_(model_path=model_path, device=device, is_half=True,agg=10) pre_fun = _audio_pre_new(model_path=model_path, device=device, is_half=True, agg=10) audio_path = "雪雪伴奏对消HP5.wav" save_path = "opt" pre_fun._path_audio_(audio_path, save_path, save_path)