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https://github.com/wendy7756/AI-Video-Transcriber

Transcribe and summarize video content using AI. Open-source, multi-platform, and supports multiple languages.
https://github.com/wendy7756/AI-Video-Transcriber

aitool tiktok transcribe videototext youtube

Last synced: 9 months ago
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Transcribe and summarize video content using AI. Open-source, multi-platform, and supports multiple languages.

Awesome Lists containing this project

README 

          



# AI Video Transcriber



English | [δΈ­ζ–‡](README_ZH.md)



An AI-powered video transcription and summarization tool that supports multiple video platforms including YouTube, Tiktok, Bilibili, and 30+ platforms.



![Interface](en-video.png)






## ✨ Features



- πŸŽ₯ **Multi-Platform Support**: Works with YouTube, Tiktok, Bilibili, and 30+ more

- πŸ—£οΈ **Intelligent Transcription**: High-accuracy speech-to-text using Faster-Whisper

- πŸ€– **AI Text Optimization**: Automatic typo correction, sentence completion, and intelligent paragraphing

- 🌍 **Multi-Language Summaries**: Generate intelligent summaries in multiple languages

- ⚑ **Real-Time Progress**: Live progress tracking and status updates

- βš™οΈ **Conditional Translation**: When the selected summary language differs from the detected transcript language, the system auto-translates with GPT‑4o

- πŸ“± **Mobile-Friendly**: Perfect support for mobile devices



## πŸš€ Quick Start



### Prerequisites



- Python 3.8+

- FFmpeg

- Optional: OpenAI API key (for AI summary features)



### Installation



#### Method 1: Automatic Installation



```bash

# Clone the repository

git clone https://github.com/wendy7756/AI-Video-Transcriber.git

cd AI-Video-Transcriber



# Run installation script

chmod +x install.sh

./install.sh

```



#### Method 2: Docker



```bash

# Clone the repository

git clone https://github.com/wendy7756/AI-Video-Transcriber.git

cd AI-Video-Transcriber



# Using Docker Compose (easiest)

cp .env.example .env

# Edit .env file and set your OPENAI_API_KEY

docker-compose up -d



# Or using Docker directly

docker build -t ai-video-transcriber .

docker run -p 8000:8000 -e OPENAI_API_KEY="your_api_key_here" ai-video-transcriber

```



#### Method 3: Manual Installation



1. **Install Python Dependencies**

```bash

# macOS (PEP 668) strongly recommends using a virtualenv

python3 -m venv .venv

source .venv/bin/activate

python -m pip install --upgrade pip

pip install -r requirements.txt

```



2. **Install FFmpeg**

```bash

# macOS

brew install ffmpeg



# Ubuntu/Debian

sudo apt update && sudo apt install ffmpeg



# CentOS/RHEL

sudo yum install ffmpeg

```



3. **Configure Environment Variables**

```bash

# Required for AI summary/translation features

export OPENAI_API_KEY="your_api_key_here"



# Optional: only if you use a custom OpenAI-compatible gateway



### Start the Service



```bash

python3 start.py

```



After the service starts, open your browser and visit `http://localhost:8000`



#### Production Mode (Recommended for long videos)



To avoid SSE disconnections during long processing, start in production mode (hot-reload disabled):



```bash

python3 start.py --prod

```



This keeps the SSE connection stable throughout long tasks (30–60+ min).



#### Run with explicit env (example)



```bash

source .venv/bin/activate

export OPENAI_API_KEY=your_api_key_here

# export OPENAI_BASE_URL=https://oneapi.basevec.com/v1   # if using a custom endpoint

python3 start.py --prod

```



## πŸ“– Usage Guide



1. **Enter Video URL**: Paste a video link from YouTube, Bilibili, or other supported platforms

2. **Select Summary Language**: Choose the language for the generated summary

3. **Start Processing**: Click the "Start" button

4. **Monitor Progress**: Watch real-time progress through multiple stages:

   - Video download and parsing

   - Audio transcription with Faster-Whisper

   - AI-powered transcript optimization (typo correction, sentence completion, intelligent paragraphing)

   - AI summary generation in selected language

5. **View Results**: Review the optimized transcript and intelligent summary

   - If transcript language β‰  selected summary language, a third tab β€œTranslation” is shown containing a translated transcript

6. **Download Files**: Click download buttons to save Markdown-formatted files (Transcript / Translation / Summary)



## πŸ› οΈ Technical Architecture



### Backend Stack

- **FastAPI**: Modern Python web framework

- **yt-dlp**: Video downloading and processing

- **Faster-Whisper**: Efficient speech transcription

- **OpenAI API**: Intelligent text summarization



### Frontend Stack

- **HTML5 + CSS3**: Responsive interface design

- **JavaScript (ES6+)**: Modern frontend interactions

- **Marked.js**: Markdown rendering

- **Font Awesome**: Icon library



### Project Structure

```

AI-Video-Transcriber/

β”œβ”€β”€ backend/                 # Backend code

β”‚   β”œβ”€β”€ main.py             # FastAPI main application

β”‚   β”œβ”€β”€ video_processor.py  # Video processing module

β”‚   β”œβ”€β”€ transcriber.py      # Transcription module

β”‚   β”œβ”€β”€ summarizer.py       # Summary module

β”‚   └── translator.py       # Translation module

β”œβ”€β”€ static/                 # Frontend files

β”‚   β”œβ”€β”€ index.html          # Main page

β”‚   └── app.js              # Frontend logic

β”œβ”€β”€ temp/                   # Temporary files directory

β”œβ”€β”€ Dockerfile              # Docker image configuration

β”œβ”€β”€ docker-compose.yml      # Docker Compose configuration

β”œβ”€β”€ .dockerignore           # Docker ignore rules

β”œβ”€β”€ .env.example            # Environment variables template

β”œβ”€β”€ requirements.txt        # Python dependencies

β”œβ”€β”€ start.py               # Startup script

└── README.md              # Project documentation

```



## βš™οΈ Configuration Options



### Environment Variables



| Variable | Description | Default | Required |

|----------|-------------|---------|----------|

| `OPENAI_API_KEY` | OpenAI API key | - | Yes (for AI features) |

| `HOST` | Server address | `0.0.0.0` | No |

| `PORT` | Server port | `8000` | No |

| `WHISPER_MODEL_SIZE` | Whisper model size | `base` | No |



### Whisper Model Size Options



| Model | Parameters | English-only | Multilingual | Speed | Memory Usage |

|-------|------------|--------------|--------------|-------|--------------|

| tiny | 39 M | βœ“ | βœ“ | Fast | Low |

| base | 74 M | βœ“ | βœ“ | Medium | Low |

| small | 244 M | βœ“ | βœ“ | Medium | Medium |

| medium | 769 M | βœ“ | βœ“ | Slow | Medium |

| large | 1550 M | βœ— | βœ“ | Very Slow | High |



## πŸ”§ FAQ



### Q: Why is transcription slow?

A: Transcription speed depends on video length, Whisper model size, and hardware performance. Try using smaller models (like tiny or base) to improve speed.



### Q: Which video platforms are supported?

A: All platforms supported by yt-dlp, including but not limited to: YouTube, TikTok, Facebook, Instagram, Twitter, Bilibili, Youku, iQiyi, Tencent Video, etc.



### Q: What if the AI optimization features are unavailable?

A: Both transcript optimization and summary generation require an OpenAI API key. Without it, the system provides the raw transcript from Whisper and a simplified summary.



### Q: I get HTTP 500 errors when starting/using the service. Why?

A: In most cases this is an environment configuration issue rather than a code bug. Please check:

- Ensure a virtualenv is activated: `source .venv/bin/activate`

- Install deps inside the venv: `pip install -r requirements.txt`

- Set `OPENAI_API_KEY` (required for summary/translation)

- If using a custom gateway, set `OPENAI_BASE_URL` correctly and ensure network access

- Install FFmpeg: `brew install ffmpeg` (macOS) / `sudo apt install ffmpeg` (Debian/Ubuntu)

- If port 8000 is occupied, stop the old process or change `PORT`



### Q: How to handle long videos?

A: The system can process videos of any length, but processing time will increase accordingly. For very long videos, consider using smaller Whisper models.



### Q: How to use Docker for deployment?

A: Docker provides the easiest deployment method:



**Prerequisites:**

- Install Docker Desktop from https://www.docker.com/products/docker-desktop/

- Ensure Docker service is running



**Quick Start:**

```bash

# Clone and setup

git clone https://github.com/wendy7756/AI-Video-Transcriber.git

cd AI-Video-Transcriber

cp .env.example .env

# Edit .env file to set your OPENAI_API_KEY



# Start with Docker Compose (recommended)

docker-compose up -d



# Or build and run manually

docker build -t ai-video-transcriber .

docker run -p 8000:8000 --env-file .env ai-video-transcriber

```



**Common Docker Issues:**

- **Port conflict**: Change port mapping `-p 8001:8000` if 8000 is occupied

- **Permission denied**: Ensure Docker Desktop is running and you have proper permissions

- **Build fails**: Check disk space (need ~2GB free) and network connection

- **Container won't start**: Verify .env file exists and contains valid OPENAI_API_KEY



**Docker Commands:**

```bash

# View running containers

docker ps



# Check container logs

docker logs ai-video-transcriber-ai-video-transcriber-1



# Stop service

docker-compose down



# Rebuild after changes

docker-compose build --no-cache

```



### Q: What are the memory requirements?

A: Memory usage varies depending on the deployment method and workload:



**Docker Deployment:**

- **Base memory**: ~128MB for idle container

- **During processing**: 500MB - 2GB depending on video length and Whisper model

- **Docker image size**: ~1.6GB disk space required

- **Recommended**: 4GB+ RAM for smooth operation



**Traditional Deployment:**

- **Base memory**: ~50-100MB for FastAPI server

- **Whisper models memory usage**:

  - `tiny`: ~150MB

  - `base`: ~250MB  

  - `small`: ~750MB

  - `medium`: ~1.5GB

  - `large`: ~3GB

- **Peak usage**: Base + Model + Video processing (~500MB additional)



**Memory Optimization Tips:**

```bash

# Use smaller Whisper model to reduce memory usage

WHISPER_MODEL_SIZE=tiny  # or base



# For Docker, limit container memory if needed

docker run -m 1g -p 8000:8000 --env-file .env ai-video-transcriber



# Monitor memory usage

docker stats ai-video-transcriber-ai-video-transcriber-1

```



### Q: Network connection errors or timeouts?

A: If you encounter network-related errors during video downloading or API calls, try these solutions:



**Common Network Issues:**

- Video download fails with "Unable to extract" or timeout errors

- OpenAI API calls return connection timeout or DNS resolution failures

- Docker image pull fails or is extremely slow



**Solutions:**

1. **Switch VPN/Proxy**: Try connecting to a different VPN server or switch your proxy settings

2. **Check Network Stability**: Ensure your internet connection is stable

3. **Retry After Network Change**: Wait 30-60 seconds after changing network settings before retrying

4. **Use Alternative Endpoints**: If using custom OpenAI endpoints, verify they're accessible from your network

5. **Docker Network Issues**: Restart Docker Desktop if container networking fails



**Quick Network Test:**

```bash

# Test video platform access

curl -I https://www.youtube.com/



# Test OpenAI API access (replace with your endpoint)

curl -I https://api.openai.com



# Test Docker Hub access

docker pull hello-world

``



## 🎯 Supported Languages



### Transcription

- Supports 100+ languages through Whisper

- Automatic language detection

- High accuracy for major languages



### Summary Generation

- English

- Chinese (Simplified)

- Japanese

- Korean

- Spanish

- French

- German

- Portuguese

- Russian

- Arabic

- And more...



## πŸ“ˆ Performance Tips



- **Hardware Requirements**:

  - Minimum: 4GB RAM, dual-core CPU

  - Recommended: 8GB RAM, quad-core CPU

  - Ideal: 16GB RAM, multi-core CPU, SSD storage



- **Processing Time Estimates**:

  | Video Length | Estimated Time | Notes |

  |-------------|----------------|-------|

  | 1 minute | 30s-1 minute | Depends on network and hardware |

  | 5 minutes | 2-5 minutes | Recommended for first-time testing |

  | 15 minutes | 5-15 minutes | Suitable for regular use |



## 🀝 Contributing



We welcome Issues and Pull Requests!



1. Fork the project

2. Create a feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request



## Acknowledgments



- [yt-dlp](https://github.com/yt-dlp/yt-dlp) - Powerful video downloading tool

- [Faster-Whisper](https://github.com/guillaumekln/faster-whisper) - Efficient Whisper implementation

- [FastAPI](https://fastapi.tiangolo.com/) - Modern Python web framework

- [OpenAI](https://openai.com/) - Intelligent text processing API



## πŸ“ž Contact



For questions or suggestions, please submit an Issue or contact Wendy.



## ⭐ Star History



If you find this project helpful, please consider giving it a star!