mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-08-29 10:09:32 +02:00
112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
import logging
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import os
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import traceback
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import torch
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from configs.config import Config
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from tools.uvr5.bsroformer import Roformer_Loader
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from tools.uvr5.mdxnet import MDXNetDereverb
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from tools.uvr5.vr import AudioPre, AudioPreDeEcho
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from i18n.i18n import I18nAuto
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logger = logging.getLogger(__name__)
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i18n = I18nAuto()
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config = Config()
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weight_uvr5_root = os.getenv("weight_uvr5_root", "assets/uvr5_weights")
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def clean_path(path):
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path = path or ""
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if path.endswith(("\\", "/")):
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path = path[:-1]
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return path.replace("/", os.sep).replace("\\", os.sep).strip(" '\n\"\u202a")
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
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infos = []
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try:
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inp_root = clean_path(inp_root)
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save_root_vocal = clean_path(save_root_vocal)
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save_root_ins = clean_path(save_root_ins)
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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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if config.dml:
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providers = ["DmlExecutionProvider", "CPUExecutionProvider"]
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elif torch.device(config.device).type == "cuda":
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cuda_device = torch.device(config.device)
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device_id = cuda_device.index if cuda_device.index is not None else 0
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providers = [
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("CUDAExecutionProvider", {"device_id": str(device_id)}),
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"CPUExecutionProvider",
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]
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else:
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providers = ["CPUExecutionProvider"]
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pre_fun = MDXNetDereverb(15, providers, config.device)
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elif "roformer" in model_name.lower():
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pre_fun = Roformer_Loader(
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model_path=os.path.join(weight_uvr5_root, model_name + ".ckpt"),
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config_path=os.path.join(weight_uvr5_root, model_name + ".yaml"),
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device=config.device,
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is_half=config.is_half,
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)
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if not os.path.exists(
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os.path.join(weight_uvr5_root, model_name + ".yaml")
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):
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infos.append(i18n("未找到Roformer模型配置文件，正在使用内置默认配置"))
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yield "\n".join(infos)
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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(weight_uvr5_root, model_name + ".pth"),
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device=config.device,
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is_half=config.is_half,
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)
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if inp_root:
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paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
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else:
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paths = [path.name for path in (paths or [])]
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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if not os.path.isfile(inp_path):
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continue
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try:
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# Let each model loader decode the original file. Its
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# torchaudio path can then perform any required 44.1 kHz
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# conversion on the selected CUDA device instead of hiding it
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# behind a CPU FFmpeg pre-conversion.
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pre_fun._path_audio_(
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inp_path,
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save_root_ins,
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save_root_vocal,
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format0,
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is_hp3,
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)
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infos.append(i18n("%s → 成功") % os.path.basename(inp_path))
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yield "\n".join(infos)
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except Exception:
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infos.append(
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"%s → %s\n%s"
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% (os.path.basename(inp_path), i18n("失败"), traceback.format_exc())
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)
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yield "\n".join(infos)
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except Exception:
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infos.append("%s\n%s" % (i18n("失败"), traceback.format_exc()))
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yield "\n".join(infos)
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finally:
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try:
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if model_name == "onnx_dereverb_By_FoxJoy":
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del pre_fun.pred.model
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del pre_fun.pred.model_
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else:
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del pre_fun.model
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del pre_fun
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except:
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traceback.print_exc()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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logger.info("Executed torch.cuda.empty_cache()")
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yield "\n".join(infos)
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