176 lines
6.6 KiB
Python
176 lines
6.6 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, agg, 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": agg,
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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,
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61968,
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123821,
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123812,
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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(
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min(nn_arch_sizes, key=lambda x: abs(x - model_size))
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)
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nets = importlib.import_module(
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"uvr5_pack.lib_v5.nets"
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+ f"_{nn_architecture}".replace("_{}KB".format(nn_arch_sizes[0]), ""),
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package=None,
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)
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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:
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model = model.half().to(device)
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else:
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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:
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return "No save root."
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name = os.path.basename(music_file)
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if ins_root is not None:
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os.makedirs(ins_root, exist_ok=True)
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if vocal_root is not None:
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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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(
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X_wave[d],
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_,
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) = 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,
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bp["sr"],
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False,
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dtype=np.float32,
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res_type=bp["res_type"],
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)
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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(
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X_wave[d + 1],
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self.mp.param["band"][d + 1]["sr"],
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bp["sr"],
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res_type=bp["res_type"],
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)
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# Stft of wave source
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X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(
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X_wave[d],
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bp["hl"],
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bp["n_fft"],
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self.mp.param["mid_side"],
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self.mp.param["mid_side_b2"],
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self.mp.param["reverse"],
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)
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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"]) + (
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self.mp.param["pre_filter_stop"] - self.mp.param["pre_filter_start"]
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)
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input_high_end = X_spec_s[d][
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:, bp["n_fft"] // 2 - input_high_end_h : bp["n_fft"] // 2, :
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]
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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 = {
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"value": aggresive_set,
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"split_bin": self.mp.param["band"][1]["crop_stop"],
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}
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with torch.no_grad():
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pred, X_mag, X_phase = inference(
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X_spec_m, self.device, self.model, aggressiveness, self.data
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)
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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(
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self.data["high_end_process"], y_spec_m, input_high_end, self.mp
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)
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(
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y_spec_m, self.mp, input_high_end_h, input_high_end_
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)
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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(
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os.path.join(
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ins_root, "instrument_{}_{}.wav".format(name, self.data["agg"])
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),
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self.mp.param["sr"],
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(np.array(wav_instrument) * 32768).astype("int16"),
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) #
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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(
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self.data["high_end_process"], v_spec_m, input_high_end, self.mp
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)
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(
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v_spec_m, self.mp, input_high_end_h, input_high_end_
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)
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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(
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os.path.join(
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vocal_root, "vocal_{}_{}.wav".format(name, self.data["agg"])
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),
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self.mp.param["sr"],
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(np.array(wav_vocals) * 32768).astype("int16"),
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)
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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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