Summary

Y2A-Auto addresses the challenge of automating YouTube video content migration to AcFun and Bilibili. This integrated tool suite targets content creators and aggregators seeking to streamline cross-platform uploads, offering a full pipeline from download to AI-enhanced publishing. Its primary benefit is the complete automation of video re-upload workflows, significantly reducing manual effort.

How It Works

The system employs a multi-stage automated pipeline. It utilizes yt-dlp for video and metadata acquisition, integrates various Automatic Speech Recognition (ASR) providers (Whisper, Voxtral, FireRedASR2S) for subtitle generation, and leverages AI models for subtitle translation, quality checking, and metadata creation (titles, descriptions, tags). Content safety is managed via Alibaba Cloud Green, followed by automated uploads to target platforms. Deployment flexibility is offered through Docker or local installations, with support for CPU and GPU-accelerated video encoding.

Quick Start & Requirements

Docker is the recommended installation method, initiated with docker compose up -d. Local setup requires Python 3.11+, FFmpeg, and yt-dlp. Crucially, valid login cookies for YouTube, AcFun, and Bilibili (cookies/yt_cookies.txt, cookies/ac_cookies.json, cookies/bili_cookies.json) are mandatory. YouTube monitoring necessitates a YouTube Data API v3 key. Access the web interface at http://localhost:5000 (Docker) or http://127.0.0.1:5000 (local). A Telegram bot (@Y2AAuto_bot) is available for trial.

Highlighted Details

• End-to-End Automation: Manages video download, ASR, subtitle processing, AI metadata generation, content moderation, and platform upload.
• AI-Driven Content Enhancement: Features AI-generated titles, descriptions, tags, and partition recommendations, alongside AI-assisted subtitle translation and quality control.
• Content Safety: Integrates Alibaba Cloud Green for automated content security checks.
• Hardware Acceleration: Supports CPU and GPU (NVIDIA, Intel, AMD) hardware encoding for video transcoding, prioritizing HEVC/H.265 with H.264 fallback.
• YouTube Monitoring: Enables scheduled fetching from specific channels or keyword searches, with options for historical data and automatic task queueing.

Maintenance & Community

The project welcomes community contributions via Issues and Pull Requests. A Telegram bot (@Y2AAuto_bot) is provided for trial use, alongside a link to a self-hosted bot project. Key dependencies include yt-dlp, FFmpeg, Flask, and OpenAI.

Licensing & Compatibility

This project is licensed under the GNU GPL v3. This copyleft license requires derivative works to be distributed under the same terms. Users must adhere to the terms of service of the target platforms (AcFun, Bilibili) and ensure legal compliance.

Limitations & Caveats

Functionality is heavily dependent on valid, up-to-date user login cookies for YouTube, AcFun, and Bilibili, which are prone to expiration or changes. YouTube monitoring requires a paid API key. Content moderation relies on external services and AI models, potentially incurring costs or facing limitations. The GPL v3 license may pose compatibility challenges for integration into closed-source commercial applications.