
Y2A-Auto
YouTube到AcFun自动化搬运工具,支持AI翻译、字幕生成、内容审核、智能监控
Stars: 196

Y2A-Auto is an automation tool that transfers YouTube videos to AcFun. It automates the entire process from downloading, translating subtitles, content moderation, intelligent tagging, to partition recommendation and upload. It also includes a web management interface and YouTube monitoring feature. The tool supports features such as downloading videos and covers using yt-dlp, AI translation and embedding of subtitles, AI generation of titles/descriptions/tags, content moderation using Aliyun Green, uploading to AcFun, task management, manual review, and forced upload. It also offers settings for automatic mode, concurrency, proxies, subtitles, login protection, brute force lock, YouTube monitoring, channel/trend capturing, scheduled tasks, history records, optional GPU/hardware acceleration, and Docker deployment or local execution.
README:
把 YouTube 视频搬运到 AcFun 的自动化工具
从下载、翻译字幕、内容审核、智能打标签,到分区推荐与上传,全流程自动化;附带 Web 管理界面与 YouTube 监控功能。
快速开始 · 功能特性 · 部署与运行 · 配置说明 · 使用指南 · 常见问题
📣 Telegram 转发机器人(试用):
@Y2AAuto_bot
自部署版本:Y2A-Auto-tgbot
- 一条龙自动化
- yt-dlp 下载视频与封面
- 字幕下载、AI 翻译并可嵌入视频
- AI 生成标题/描述与标签,推荐分区
- 内容安全审核(阿里云 Green)
- 上传至 AcFun(基于 acfun_upload)
- Web 管理后台
- 任务列表、人工审核、强制上传
- 设置中心(开关自动模式、并发、代理、字幕等)
- 登录保护与暴力破解锁定
- YouTube 监控
- 频道/趋势抓取(需配置 API Key)
- 定时任务与历史记录
- 可选 GPU/硬件加速
- Docker 一键部署,或本地运行
Y2A-Auto/
├─ app.py # Flask Web 入口
├─ requirements.txt # 依赖列表
├─ Dockerfile # Docker 构建
├─ docker-compose.yml # 生产/拉取镜像运行
├─ docker-compose-build.yml # 本地构建镜像运行
├─ Makefile # 常用 Docker 管理命令
├─ acfunid/ # AcFun 分区映射
│ └─ id_mapping.json
├─ modules/ # 核心后端模块
│ ├─ youtube_handler.py # YouTube 下载/元数据/封面/字幕
│ ├─ youtube_monitor.py # YouTube 监控与定时任务
│ ├─ acfun_uploader.py # AcFun 上传
│ ├─ subtitle_translator.py # 字幕翻译与嵌入
│ ├─ ai_enhancer.py # 标题/描述/标签 AI 生成
│ ├─ content_moderator.py # 内容审核
│ ├─ speech_recognition.py # 语音转写(Whisper/OpenAI 兼容)
│ ├─ task_manager.py # 任务编排、并发与转码
│ ├─ config_manager.py # 配置读写与默认项
│ └─ utils.py # 工具函数
├─ templates/ # 前端页面(Jinja2)
├─ static/ # 前端静态资源(CSS/JS/图片)
├─ config/ # 应用配置(首次运行生成)
│ └─ config.json
├─ cookies/ # Cookie(自备:yt_cookies.txt、ac_cookies.txt)
├─ db/ # SQLite 数据库
├─ downloads/ # 任务产物(视频/封面/字幕/元数据)
├─ logs/ # 运行与任务日志(task_xxx.log)
├─ fonts/ # 字幕字体(思源黑体变体)
├─ temp/ # 临时目录
└─ build-tools/ # 打包相关脚本
推荐使用 Docker(无需本地安装 Python/FFmpeg/yt-dlp):
- 准备 Cookie(重要)
- 创建
cookies/yt_cookies.txt
(YouTube 登录 Cookie) - 创建
cookies/ac_cookies.txt
(AcFun 登录 Cookie) - 可用浏览器扩展导出 Cookie(例如「Get cookies.txt」);注意保护隐私,避免提交到仓库。
- 启动服务
- 安装好 Docker 与 Docker Compose 后,在项目根目录执行:
docker compose up -d
- 打开 Web 界面
- 浏览器访问:http://localhost:5000
- 首次进入可在「设置」里开启登录保护并设置密码、开启自动模式等。
目录 config/db/downloads/logs/temp/cookies
会被挂载到容器,数据持久化保存。
- 使用预构建镜像:
docker-compose.yml
已配置好端口与挂载目录 - 关闭/重启/查看日志:
- 关闭:
docker compose down
- 重启:
docker compose restart
- 日志:
docker compose logs -f
- 关闭:
前置依赖:
- Python 3.10+
- FFmpeg(命令行可执行)
- yt-dlp(
pip install yt-dlp
)
步骤:
# 1) 创建并启用虚拟环境(Windows PowerShell)
py -3.10 -m venv .venv
.\.venv\Scripts\Activate.ps1
# 2) 安装依赖
pip install -r requirements.txt
# 3) 运行
python app.py
访问 http://127.0.0.1:5000 打开 Web 界面。
应用首次运行会在 config/config.json
生成配置文件;你也可以手动编辑。常用项:
{
"AUTO_MODE_ENABLED": true,
"password_protection_enabled": true,
"password": "建议自行设置",
"YOUTUBE_COOKIES_PATH": "cookies/yt_cookies.txt",
"ACFUN_COOKIES_PATH": "cookies/ac_cookies.txt",
"OPENAI_API_KEY": "可选:用于标题/描述/标签与字幕翻译",
"OPENAI_BASE_URL": "https://api.openai.com/v1",
"OPENAI_MODEL_NAME": "gpt-3.5-turbo",
"SUBTITLE_TRANSLATION_ENABLED": true,
"SUBTITLE_TARGET_LANGUAGE": "zh",
"YOUTUBE_API_KEY": "可选:启用 YouTube 监控",
"VIDEO_ENCODER": "cpu" // 也可 nvenc/qsv/amf
}
提示:
- 仅在本机安全环境中保存密钥,切勿把包含密钥的文件提交到仓库。
- 若需要代理下载 YouTube,可在设置里启用代理并填写地址/账号密码。
- Windows/NVIDIA 用户可将
VIDEO_ENCODER
设为nvenc
获得更快的嵌字/转码。
- 在首页或「任务」页,粘贴 YouTube 视频链接添加任务
- 自动模式下会依次:下载 →(可选)转写/翻译字幕 → 生成标题/描述/标签 → 内容审核 →(可选)人工审核 → 上传到 AcFun
- 人工审核可在「人工审核」页修改标题/描述/标签与分区,再点击「强制上传」
- YouTube 监控:在界面中开启并配置 API Key 后,可添加频道/关键词定时监控
目录说明:
-
downloads/
每个任务一个子目录,包含 video.mp4、cover.jpg、metadata.json、字幕等 -
logs/
运行日志与各任务日志(task_xxx.log) -
db/
SQLite 数据库 -
cookies/
存放 cookies.txt(需自行准备)
仅当在设置中勾选“将字幕嵌入视频”时,本段所述的转码参数才会生效。应用会根据 VIDEO_ENCODER
选择编码器并使用统一参数:
- CPU:libx264,CRF 18,preset=slow,profile=high,level=4.2,yuv420p
- NVIDIA NVENC:hevc_nvenc,preset=p6,cq=20,rc-lookahead=32;若源为 10bit,自动使用 profile=main10 并输出 p010le,否则 profile=main + yuv420p
- 音频:AAC 320kbps,采样率跟随原视频
提示:NVENC/QSV/AMF 取决于系统与 ffmpeg 的编译是否包含对应硬编支持;不可用时会自动回退到 CPU。
应用支持通过 VIDEO_ENCODER
选择编码器:cpu
(默认)/ nvenc
(NVIDIA)/ qsv
(Intel)。注意:容器内需有“包含对应硬件编码器的 ffmpeg”。默认镜像为发行版 ffmpeg,通常不含 NVENC/QSV;若需硬件转码,请按下述方案:
- 使用自定义镜像引入已启用 NVENC/QSV 的 ffmpeg
- 或改用已包含硬件编码器的 ffmpeg 基础镜像
前提:安装 NVIDIA 驱动与 NVIDIA Container Toolkit。
docker-compose 关键配置示例:
services:
y2a-auto:
image: fqscfqj/y2a-auto:latest
ports:
- "5000:5000"
volumes:
- ./config:/app/config
- ./db:/app/db
- ./downloads:/app/downloads
- ./logs:/app/logs
- ./cookies:/app/cookies
- ./temp:/app/temp
environment:
- TZ=Asia/Shanghai
- PYTHONIOENCODING=utf-8
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
并在应用设置或 config/config.json
中设置:
{"VIDEO_ENCODER": "nvenc"}
可选自检(容器内):
ffmpeg -hide_banner -encoders | grep -i nvenc
前提:宿主机启用 iGPU,驱动正常;容器映射 /dev/dri
。
docker-compose 关键配置示例:
services:
y2a-auto:
image: fqscfqj/y2a-auto:latest
devices:
- /dev/dri:/dev/dri
environment:
- LIBVA_DRIVER_NAME=iHD
- TZ=Asia/Shanghai
- PYTHONIOENCODING=utf-8
并在应用设置或 config/config.json
中设置:
{"VIDEO_ENCODER": "qsv"}
可选自检(容器内):
ffmpeg -hide_banner -encoders | grep -i qsv
若默认镜像缺少硬件编码器,可在自定义镜像中引入已编译好的 ffmpeg,例如基于 jrottenberg/ffmpeg
(示意):
FROM jrottenberg/ffmpeg:6.1-nvidia AS ffmpeg
FROM python:3.10-slim
WORKDIR /app
# 拷贝 ffmpeg 到运行镜像
COPY --from=ffmpeg /usr/local /usr/local
# 安装依赖与应用
RUN apt-get update && apt-get install -y --no-install-recommends \
curl && rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "app.py"]
构建完成后,按前述 NVENC/QSV 的 compose 示例分配设备即可。
提示:容器内 ffmpeg 的编码器可用性以 ffmpeg -encoders
为准;若不可用,请更换镜像或自行编译。
- 403 / 需要登录 / not a bot 等错误
- 通常是 YouTube 反爬或权限问题。请更新
cookies/yt_cookies.txt
(确保包含有效的youtube.com
登录状态)。
- 通常是 YouTube 反爬或权限问题。请更新
- 找不到 FFmpeg / yt-dlp
- Docker 用户无需关心;本地运行请确保两者在 PATH 中或通过
pip install yt-dlp
安装,并单独安装 FFmpeg。
- Docker 用户无需关心;本地运行请确保两者在 PATH 中或通过
- 上传到 AcFun 失败
- 请更新
cookies/ac_cookies.txt
,并在「人工审核」页确认分区、标题与描述合规。
- 请更新
- 字幕翻译速度慢
- 可在设置中调大并发与批大小(注意 API 限速),或使用硬件编码器加速视频处理。
- 欢迎提交 Issue/PR:问题反馈、功能建议都很棒 → Issues
- 提交前请避免包含个人 Cookie、密钥等敏感信息。
特别感谢 @Aruelius 的 acfun_upload 项目为上传实现提供了重要参考。
本项目基于 GNU GPL v3 开源。请遵守各平台服务条款,仅在合规前提下用于学习与研究。
如果对你有帮助,欢迎 Star 支持 ✨
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leapfrogai
LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

llava-docker
This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

carrot
The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM
TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.