---
title: Lec2note
emoji: 🐢
colorFrom: red
colorTo: yellow
sdk: streamlit
app_file: app.py
pinned: false
license: apache-2.0
short_description: Using for automatically generating notes from video lectures
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

### Project Overview
Lec2Note is an **automatic lecture-to-note generator**. Upload a lecture video (MP4/MKV/AVI) and receive a well-formatted Markdown study note containing:

- **ASR transcription** powered by OpenAI Whisper.
- **Video segmentation** using semantic & visual cues.
- **LLM summarisation** (e.g. GPT-4) for each segment, extracting key points, formulas and insights.
- **Image extraction** of key frames to illustrate the note.
- **Markdown assembly** into a single readable document.


### Installation
```bash
# Requires Python ≥ 3.10
git clone https://github.com/your-name/Lec2Note.git
cd Lec2Note
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
```
GPU inference: ensure CUDA and the matching PyTorch build are installed.

### Quick Start
#### 1. Web UI
Navigate to `https://huggingface.co/spaces/LRU1/lec2note` .

#### 2. CLI
```bash
python -m lec2note.scripts.run_pipeline --video path/to/lecture.mp4 --output notes.md
```

#### 3. Required Environment Variables
```bash
export OPENAI_API_KEY=your_openai_api_key
export REPLICATE_API_TOKEN=your_replicate_api_token
export LOG_LEVEL=DEBUG(optional)
export AUDIO2TEXT_LOCAL=true|false(optional)
```

### Directory Structure
```text
Lec2Note/
├── app.py                  # Streamlit front-end
├── lec2note/
│   ├── ingestion/          # Audio/video preprocessing & ASR
│   ├── segmentation/       # Semantic + visual segmentation
│   ├── processing/         # LLM summarisation & note generation
│   ├── synthesis/          # Markdown assembly
│   └── scripts/            # CLI entry points
└── tests/                  # Test suite
```

### Environment Variables
Some modules require the following environment variables:
- `OPENAI_API_KEY`: OpenAI access token.
- `WHISPER_MODEL`: Whisper model name, default `base`.

### Contributing
Pull requests and issues are welcome! See `DEVELOPER_GUIDE.md` for code conventions and workflow.

### License
Released under the Apache-2.0 license.