This Python script automatically generates a structured outline from lecture videos using WhisperAPI.com for transcription and OpenRouter's API for outline generation.

• Transcribes video to text using WhisperAPI.com (Whisper Large V3)
• Generates structured outlines with timestamps
• Supports Czech language (or auto-detection)
• Creates both SRT subtitles and JSON outlines
• Multi-level topic hierarchy
• Organizes output files in video-specific directories
• No local GPU required - uses cloud APIs
• Speaker detection included
• Affordable pricing ($0.17/hour after free trial)
• Handles large files (>100MB) via Google Drive or FTP upload

# Clone the repository
git clone https://github.com/matak/videooutliner.git
cd videooutliner

# Install dependencies (using uv)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv pip install -r requirements.txt

# Set up your API keys
cp .env.example .env
# Edit .env with your API keys

# Run the script
python generate_outline.py path/to/lecture.mp4

You can also run the script directly using uv:

uv run python generate_outline.py path/to/lecture.mp4

• Python 3.8 or higher
• WhisperAPI.com API key (30 hours free trial available)
• OpenRouter API key
• FFmpeg (for audio extraction)
• Google Drive API credentials or FTP server (for files >100MB)

1. Clone this repository:

git clone https://github.com/matak/videooutliner.git
cd videooutliner

2. Install dependencies using either pip or uv:

Using pip:

pip install -r requirements.txt

Using uv (recommended, faster):

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv pip install -r requirements.txt

3. Create a .env file in the project root:

WHISPER_API_KEY=your_whisper_api_key_here
OPENROUTER_API_KEY=your_openrouter_api_key_here

4. (Optional) Set up file upload for large files:

   a. For Google Drive:

   • Go to Google Cloud Console
   • Create a new project or select an existing one
   • Enable the Google Drive API
   • Create OAuth 2.0 credentials
   • Download the credentials and save as google_drive_credentials.json in the project directory

   b. For FTP:

   • Copy ftp_settings.example.json to ftp_settings.json
   • Edit ftp_settings.json with your FTP server details:

     {
         "host": "ftp.example.com",
         "username": "your_username",
         "password": "your_password",
         "path": "/public_html/temp",
         "public_url": "https://example.com/temp"
     }

Run the script with a video file as input:

python generate_outline.py path/to/lecture.mp4

The script will:

1. Create a web/public/videos directory if it doesn't exist
2. Create a subdirectory named after the video (without extension) in web/public/videos
3. Extract audio from the video file
4. For files >100MB:
   • Upload to Google Drive if configured
   • Or upload to FTP server if configured
   • Or raise an error if no upload service is configured
5. Upload the audio to WhisperAPI.com for transcription
6. Generate an SRT file with timestamps and transcript (including speaker detection)
7. Create a JSON outline with structured topics and timestamps
8. Clean up any temporary uploaded files

For a video named lecture.mp4, the output will be organized as follows:

web/public/videos/
└── lecture/
    ├── lecture.srt
    └── lecture_outline.json

Standard subtitle format with timestamps and text, including speaker labels.

[
  {
    "title": "Main Topic",
    "start_time": "00:00:00",
    "subsections": [
      {
        "title": "Subtopic",
        "start_time": "00:03:12",
        "subsections": []
      }
    ]
  }
]

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

• Uses WhisperAPI.com for transcription (no local GPU required)
• First month includes 30 hours of free transcription
• After free trial, costs $0.17 per hour of audio
• Processing time depends on video length and API response times
• Each processed video gets its own directory in web/public/videos
• Includes speaker detection in the transcription
• Files larger than 100MB are automatically uploaded to Google Drive or FTP server
• Temporary uploaded files are automatically cleaned up after processing

This project is licensed under the MIT License - see the LICENSE file for details.