94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
import logging
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import os
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import traceback
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from glob import glob
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from pathlib import Path
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import soundfile as sf
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import torch
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from pydub import AudioSegment
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from rvc.configs.config import Config
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from rvc.modules.uvr5.mdxnet import MDXNetDereverb
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from rvc.modules.uvr5.vr import AudioPre, AudioPreDeEcho
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logger: logging.Logger = logging.getLogger(__name__)
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class UVR:
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def __init__(self):
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self.need_reformat: bool = True
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self.config: Config = Config()
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def uvr_wrapper(
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self,
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audio_path: Path,
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save_vocal_path: Path | None = None,
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save_ins_path: Path | None = None,
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agg: int = 10,
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export_format: str = "flac",
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model_name: str | None = None,
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temp_path: Path | None = None,
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):
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infos = []
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save_vocal_path = (
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os.getenv("save_uvr_path") if not save_vocal_path else save_vocal_path
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)
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save_ins_path = (
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os.getenv("save_uvr_path") if not save_ins_path else save_ins_path
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)
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if model_name is None:
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model_name = os.path.basename(glob(f"{os.getenv('weight_uvr5_root')}/*")[0])
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is_hp3 = "HP3" in model_name
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if model_name == "onnx_dereverb_By_FoxJoy":
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pre_fun = MDXNetDereverb(15, self.config.device)
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else:
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func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
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pre_fun = func(
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agg=int(agg),
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model_path=os.path.join(
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os.getenv("weight_uvr5_root"), model_name # + ".pth"
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),
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device=self.config.device,
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is_half=self.config.is_half,
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)
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process_paths = (
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[
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_
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for _ in glob(f"{audio_path}/*")
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if os.path.splitext(_)[-1][1:].upper() in sf.available_formats()
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]
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if os.path.isdir(audio_path)
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else audio_path
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)
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for process_path in [process_paths]:
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print(f"path: {process_path}")
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info = sf.info(process_path)
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if not (info.channels == 2 and info.samplerate == "44100"):
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tmp_path = os.path.join(
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temp_path or os.environ.get("TEMP"), os.path.basename(process_path)
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)
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AudioSegment.from_file(process_path).export(
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tmp_path,
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format="wav",
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codec="pcm_s16le",
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bitrate="16k",
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parameters=["-ar", "44100"],
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)
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pre_fun._path_audio_(
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process_path,
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save_vocal_path,
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save_ins_path,
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export_format,
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is_hp3=is_hp3,
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)
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infos.append(f"{os.path.basename(process_path)}->Success")
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yield "\n".join(infos)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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