mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
175 lines
6.6 KiB
Python
175 lines
6.6 KiB
Python
import os
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import logging
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logger = logging.getLogger(__name__)
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import librosa
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import numpy as np
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from infer.lib.audio import save_audio
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import torch
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from infer.lib.uvr5_pack.lib_v5 import nets_123821KB as Nets
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from infer.lib.uvr5_pack.lib_v5 import spec_utils
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from infer.lib.uvr5_pack.lib_v5.model_param_init import ModelParameters
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from infer.lib.uvr5_pack.lib_v5.nets import CascadedNet
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from infer.lib.uvr5_pack.utils import inference
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class AudioPre:
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def __init__(self, agg, model_path, device, is_half, tta=False):
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self.model_path = model_path
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self.device = device
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self.is_de_echo = "DeEcho" in model_path
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self.is_reverse = self.is_de_echo or "HP3" in model_path
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self.data = {
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# Processing Options
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"postprocess": False,
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"tta": tta,
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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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if self.is_de_echo:
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mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v3.json")
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nout = 64 if "DeReverb" in model_path else 48
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model = CascadedNet(mp.param["bins"] * 2, nout)
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else:
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mp = ModelParameters("infer/lib/uvr5_pack/lib_v5/modelparams/4band_v2.json")
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model = Nets.CascadedASPPNet(mp.param["bins"] * 2)
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cpk = torch.load(model_path, map_location="cpu", weights_only=True)
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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, format="flac"):
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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.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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sr=bp["sr"],
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mono=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.resample(
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X_wave[d + 1],
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orig_sr=self.mp.param["band"][d + 1]["sr"],
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target_sr=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"],
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y_spec_m,
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input_high_end,
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self.mp.param["pre_filter_start"],
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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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logger.info("%s instruments done" % name)
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if self.is_reverse:
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head = "vocal_"
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else:
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head = "instrument_"
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save_audio(
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os.path.join(
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ins_root,
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head + "{}_{}.{}".format(name, self.data["agg"], format),
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),
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wav_instrument,
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self.mp.param["sr"],
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f32=True,
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format=format,
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)
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if vocal_root is not None:
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if self.is_reverse:
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head = "instrument_"
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else:
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head = "vocal_"
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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"],
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v_spec_m,
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input_high_end,
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self.mp.param["pre_filter_start"],
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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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logger.info("%s vocals done" % name)
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save_audio(
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os.path.join(
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vocal_root,
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head + "{}_{}.{}".format(name, self.data["agg"], format),
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),
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wav_vocals,
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self.mp.param["sr"],
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f32=True,
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format=format,
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)
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