Y2A-Auto: The Complete Solution for Automated YouTube to AcFun Video Transfers

Effortlessly bridge content across platforms with AI-powered translation, automated processing, and intelligent monitoring

1. Why Automated Video Transfer Matters

Content creators face consistent challenges:

• Manual downloading/reuploading wastes hours weekly
• Language barriers limit audience reach
• Platform-specific formatting requires technical skills
• Consistent cross-posting demands significant effort

Y2A-Auto solves these fundamentally. This open-source Flask application automates YouTube-to-AcFun transfers while handling technical complexities behind the scenes.

2. Core Functionality Breakdown

2.1 Intelligent YouTube Monitoring

graph LR
A[Monitoring Sources] --> B{Monitoring Types}
B --> C(Trending Videos)
B --> D(Keyword Searches)
B --> E(Specific Channels)
E --> F[Blacklist/Whitelist]
C --> G[Custom Filters]
G --> H[Views/Likes/Duration]
H --> I[Auto-Queueing]

Monitoring capabilities:

• Regional tracking: US, JP and other regional trends
• Precision filtering: View thresholds (≥100K), like ratios (≥95%)
• Flexible scheduling: Timed scans (every 6hrs) or manual triggers
• Content exclusion: Block sensitive or low-quality keywords

2.2 AI-Enhanced Processing Pipeline

2.3 Visual Task Management

Web interface displays:

• Progress tracking (30% downloaded → 50% translated → 100% uploaded)
• Real-time error alerts (API failures/network issues)
• Historical performance analytics
• Manual review checkpoint

3. 5-Minute Deployment Guide

3.1 Docker Deployment (Recommended)

# Clone repository
git clone https://github.com/fqscfqj/Y2A-Auto.git
cd Y2A-Auto

# Launch service
docker-compose up -d

# Verify operation
docker-compose logs -f

Directory structure:

/app
├── config # Configuration files
├── db # Database
├── downloads # Video cache
├── cookies # Platform credentials
└── logs # Operation records

3.2 Native Python Installation

# Create virtual environment
python -m venv .venv
source .venv/bin/activate # Linux/Mac

# Install dependencies
pip install -r requirements.txt

# Install FFmpeg (Ubuntu example)
sudo apt install ffmpeg

# Start service
python app.py

4. Practical Workflow Demonstration

4.1 Browser Extension Workflow

1. Install Tampermonkey extension
2. Add push script:

   // ==UserScript==
   // @name         PushToY2AAuto
   // @match        *://www.youtube.com/*
   // @grant        GM_xmlhttpRequest
   // ==/UserScript==
   

3. Click “Push to Y2A-Auto” on YouTube pages

4.2 Manual Task Processing

1. Access http://localhost:5000
2. Paste YouTube URL in task manager
3. Configure options:

   • [x] Translate title
   • [x] Generate AI tags
   • [ ] Skip content review
4. Click “Start Processing”

4.3 Subtitle Configuration

{
  "SUBTITLE_TRANSLATION_ENABLED": true,
  "SUBTITLE_SOURCE_LANGUAGE": "auto",
  "SUBTITLE_TARGET_LANGUAGE": "zh",
  "SUBTITLE_BATCH_SIZE": 5,
  "SUBTITLE_EMBED_IN_VIDEO": true
}

Function: Translates 5 lines per batch to Chinese and embeds subtitles directly in video

5. Critical Configuration Guide

5.1 API Key Setup

YouTube Data API:

1. Visit Google Cloud Console
2. Create project → Enable YouTube Data API v3
3. Generate API Key in Credentials section

OpenAI Configuration:

{
  "OPENAI_API_KEY": "sk-xxx",
  "OPENAI_MODEL_NAME": "gpt-3.5-turbo",
  "TRANSLATE_DESCRIPTION": true
}

5.2 Cookie Management

AcFun Cookie Extraction:

1. Log in to AcFun → Press F12
2. Navigate: Application → Cookies
3. Export all cookies as text file
4. Upload via web interface to /cookies

Required Format:

.acfun.cn TRUE / FALSE 1672485767 auth_key xxxxxxx
.acfun.cn TRUE / FALSE 1672485767 acPasstoken yyyyyyy

6. Technical Architecture Analysis

6.1 System Workflow

flowchart TB
    subgraph Input Sources
        A[YouTube URLs] --> B[Monitoring System]
        C[Browser Extension] --> B
    end

    subgraph Processing Core
        B --> D[Download Module]
        D --> E[Subtitle Engine]
        E --> F[AI Translation]
        F --> G[Content Moderation]
        G --> H[AcFun Uploader]
    end

    subgraph Output
        H --> I[AcFun Video Page]
    end

    subgraph Support Systems
        J[Flask Dashboard] --> K[Task Scheduler]
        L[SQLite Database] --> M[State Management]
    end

6.2 Technology Stack

7. Troubleshooting Common Issues

7.1 Monitoring Errors

Problem: YouTube API quota exhaustion
Solutions:

1. Reduce scan frequency (e.g., hourly → daily)
2. Request quota increase via Google Cloud Console
3. Apply strict filters to minimize requests

Problem: Low-quality video detection
Configuration fix:

{
  "MIN_VIEW_COUNT": 100000,
  "MIN_LIKE_RATIO": 0.95,
  "BLACKLIST_KEYWORDS": ["sensitive","spam"]
}

7.2 Subtitle Translation Optimization

Quality improvements:

• Upgrade model: OPENAI_MODEL_NAME=gpt-4
• Reduce batch size: SUBTITLE_BATCH_SIZE=3 (slower but more accurate)
• Specify source language: SUBTITLE_SOURCE_LANGUAGE="en"

Error resolution:

ERROR: Subtitle translation failed - OpenAI timeout

Diagnosis steps:

1. Test connectivity to api.openai.com
2. Verify API key balance
3. Check rate limit status

7.3 Upload Failures

Error: AcFun upload failed: Cookie expired
Resolution:

1. Refresh AcFun login credentials
2. Re-upload cookie via web interface
3. Restart service

Network diagnostics:

# Test AcFun connectivity
curl -I https://www.acfun.cn

# Validate YouTube access
docker exec y2a-auto yt-dlp -F https://youtube.com/watch?v=xxx

8. Advanced Implementation Techniques

8.1 Multi-Channel Monitoring

1. Create monitor_config.json:

   [
     {
       "name": "Tech Channel Monitor",
       "type": "channel",
       "channel_id": "UCxxxx",
       "schedule": "every 6 hours",
       "min_views": 50000
     },
     {
       "name": "Trend Keyword Tracker",
       "type": "search",
       "keywords": "AI breakthroughs",
       "region": "CN"
     }
   ]
   

2. Import via web interface
3. Enable auto-scheduling

8.2 Video Preprocessing Rules

Define in video_rules.json:

{
  "resolution": "1080p",
  "format": "mp4",
  "thumbnail_style": {
    "add_logo": true,
    "logo_position": "bottom-right"
  }
}

9. Security & Maintenance Protocol

9.1 Security Practices

1. Credential management:

   • Never commit API keys to Git
   • Use environment variables for secrets
2. Access control:

   # Restrict dashboard access
   flask run --host=127.0.0.1
   

3. Audit logging:

   • Automatic daily log rotation: logs/access_20240608.gz

9.2 System Maintenance

Backup procedure:

# Full system backup
tar -czf y2a-backup-$(date +%Y%m%d).tar.gz config db cookies

Version upgrade:

# Docker update sequence
docker-compose down
docker pull fqscfqj/y2a-auto:latest
docker-compose up -d

10. Value Proposition & Roadmap

10.1 Core Benefits

• Time savings: 1-hour manual process → 10-minute automation
• Language solutions: 85%+ translation accuracy
• Compliance protection: Automated content screening
• Resource efficiency: 60% server load reduction

10.2 Future Development

1. Platform expansion: Bilibili/Douyin support
2. Voice cloning: Localized dubbing
3. Auto-editing: Short-form content generation
4. Cluster deployment: Distributed processing

GitHub Repository: https://github.com/fqscfqj/Y2A-Auto
License: GNU GPL v3.0

Appendix: Technical Reference

Technical FAQ

Q: Docker container fails to start. How to diagnose?
A: Check sequentially:

1. Port conflict: lsof -i:5000
2. Storage space: df -h /
3. Image integrity: docker-compose build --no-cache

Q: Video processing stalls at download phase.
A: Troubleshooting steps:

# Manual download test
docker exec -it y2a-auto yt-dlp https://youtube.com/watch?v=xxx
# Verify directory permissions
ls -ld downloads/

Q: AI-generated tags are inaccurate.
A: Modify prompt template:

# In ai_enhancer.py
PROMPT_TEMPLATE = "Generate 5 Chinese tags based on: {video_info}"