# -*- coding: utf-8 -*- """ 模型下载工具 - 自动从 Hugging Face 下载所需模型 """ import os import hashlib import requests from pathlib import Path from tqdm import tqdm from typing import Optional, Dict, List # 模型下载配置 MODELS = { "HP2_all_vocals.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth", "path": "assets/uvr5_weights/HP2_all_vocals.pth", "size_mb": 140, "description": "UVR5 HP2 vocal model" }, "HP3_all_vocals.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth", "path": "assets/uvr5_weights/HP3_all_vocals.pth", "size_mb": 140, "description": "UVR5 HP3 vocal model" }, "HP5_only_main_vocal.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth", "path": "assets/uvr5_weights/HP5_only_main_vocal.pth", "size_mb": 140, "description": "UVR5 HP5 main vocal model" }, "VR-DeEchoAggressive.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth", "path": "assets/uvr5_weights/VR-DeEchoAggressive.pth", "size_mb": 130, "description": "UVR5 de-echo aggressive" }, "VR-DeEchoDeReverb.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth", "path": "assets/uvr5_weights/VR-DeEchoDeReverb.pth", "size_mb": 130, "description": "UVR5 de-echo + de-reverb" }, "VR-DeEchoNormal.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth", "path": "assets/uvr5_weights/VR-DeEchoNormal.pth", "size_mb": 130, "description": "UVR5 de-echo normal" }, "onnx_dereverb_By_FoxJoy/vocals.onnx": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx", "path": "assets/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx", "size_mb": 50, "description": "UVR5 ONNX dereverb" }, "hubert_base.pt": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt", "path": "assets/hubert/hubert_base.pt", "size_mb": 189, "description": "HuBERT 特征提取模型" }, "rmvpe.pt": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt", "path": "assets/rmvpe/rmvpe.pt", "size_mb": 181, "description": "RMVPE 音高提取模型" }, "f0G48k.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth", "path": "assets/pretrained_v2/f0G48k.pth", "size_mb": 55, "description": "48kHz 生成器预训练权重" }, "f0D48k.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth", "path": "assets/pretrained_v2/f0D48k.pth", "size_mb": 55, "description": "48kHz 判别器预训练权重" }, "f0G40k.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth", "path": "assets/pretrained_v2/f0G40k.pth", "size_mb": 55, "description": "40kHz 生成器预训练权重" }, "f0D40k.pth": { "url": "https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth", "path": "assets/pretrained_v2/f0D40k.pth", "size_mb": 55, "description": "40kHz 判别器预训练权重" } } # 必需模型列表 REQUIRED_MODELS = ["hubert_base.pt", "rmvpe.pt", "HP2_all_vocals.pth"] # Mature DeEcho / DeReverb models downloaded separately MATURE_DEECHO_MODELS = [ "VR-DeEchoDeReverb.pth", "onnx_dereverb_By_FoxJoy/vocals.onnx", "VR-DeEchoNormal.pth", "VR-DeEchoAggressive.pth", ] def get_project_root() -> Path: """获取项目根目录""" return Path(__file__).parent.parent def download_file(url: str, dest_path: Path, desc: str = None) -> bool: """ 下载文件，支持断点续传和进度显示 Args: url: 下载链接 dest_path: 目标路径 desc: 进度条描述 Returns: bool: 下载是否成功 """ dest_path.parent.mkdir(parents=True, exist_ok=True) # 检查已下载的部分 resume_pos = 0 if dest_path.exists(): resume_pos = dest_path.stat().st_size headers = {} if resume_pos > 0: headers["Range"] = f"bytes={resume_pos}-" if "huggingface.co" in url: hf_token = ( os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") ) if hf_token: headers["Authorization"] = f"Bearer {hf_token}" try: response = requests.get(url, headers=headers, stream=True, timeout=30) # 检查是否支持断点续传 if response.status_code == 416: # Range not satisfiable print(f" 文件已完整下载: {dest_path.name}") return True if response.status_code not in [200, 206]: print(f" 下载失败: HTTP {response.status_code}") return False # 获取文件总大小 total_size = int(response.headers.get("content-length", 0)) if response.status_code == 206: total_size += resume_pos # 下载模式 mode = "ab" if resume_pos > 0 else "wb" with open(dest_path, mode) as f: with tqdm( total=total_size, initial=resume_pos, unit="B", unit_scale=True, desc=desc or dest_path.name ) as pbar: for chunk in response.iter_content(chunk_size=8192): if chunk: f.write(chunk) pbar.update(len(chunk)) return True except requests.exceptions.RequestException as e: print(f" 下载错误: {e}") return False def check_model(name: str) -> bool: """ 检查模型是否已下载 Args: name: 模型名称 Returns: bool: 模型是否存在 """ if name not in MODELS: return False model_path = get_project_root() / MODELS[name]["path"] return model_path.exists() def download_model(name: str) -> bool: """ 下载指定模型 Args: name: 模型名称 Returns: bool: 下载是否成功 """ if name not in MODELS: print(f"未知模型: {name}") return False model_info = MODELS[name] model_path = get_project_root() / model_info["path"] if model_path.exists(): print(f"模型已存在: {name}") return True print(f"正在下载: {model_info['description']} ({model_info['size_mb']}MB)") return download_file(model_info["url"], model_path, name) def download_required_models() -> bool: """ 下载所有必需模型 Returns: bool: 是否全部下载成功 """ print("=" * 50) print("检查必需模型...") print("=" * 50) success = True for name in REQUIRED_MODELS: if not check_model(name): if not download_model(name): success = False else: print(f"[OK] {name} 已存在") return success def download_all_models() -> bool: """ 下载所有模型 Returns: bool: 是否全部下载成功 """ print("=" * 50) print("下载所有模型...") print("=" * 50) success = True for name in MODELS: if not check_model(name): if not download_model(name): success = False else: print(f"[OK] {name} 已存在") return success def check_all_models() -> Dict[str, bool]: """ 检查所有模型状态 Returns: dict: 模型名称 -> 是否存在 """ return {name: check_model(name) for name in MODELS} def get_available_mature_deecho_models() -> List[str]: """Return locally available mature DeEcho / DeReverb models.""" return [name for name in MATURE_DEECHO_MODELS if check_model(name)] def get_preferred_mature_deecho_model() -> Optional[str]: """Return the preferred learned DeEcho model by priority.""" available = set(get_available_mature_deecho_models()) for name in MATURE_DEECHO_MODELS: if name in available: return name return None def download_mature_deecho_models() -> bool: """Download mature DeEcho / DeReverb recommended models.""" print("=" * 50) print("Downloading mature DeEcho / DeReverb models...") print("=" * 50) success = True for name in MATURE_DEECHO_MODELS: if not check_model(name): if not download_model(name): success = False else: print(f"[OK] {name} already exists") return success def print_model_status(): """打印模型状态""" print("=" * 50) print("模型状态") print("=" * 50) status = check_all_models() for name, exists in status.items(): info = MODELS[name] mark = "OK" if exists else "MISSING" print(f" {mark} {name}") print(f" {info['description']}") print(f" 大小: {info['size_mb']}MB") if name in REQUIRED_MODELS: print(f" [必需]") print() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="RVC 模型下载工具") parser.add_argument("--check", action="store_true", help="检查模型状态") parser.add_argument("--all", action="store_true", help="下载所有模型") parser.add_argument("--model", type=str, help="下载指定模型") args = parser.parse_args() if args.check: print_model_status() elif args.model: download_model(args.model) elif args.all: download_all_models() else: download_required_models()