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: 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( # 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) 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)