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--- |
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title: Lec2note |
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emoji: 🐢 |
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colorFrom: red |
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colorTo: yellow |
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sdk: streamlit |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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short_description: Using for automatically generating notes from video lectures |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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### Project Overview |
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Lec2Note is an **automatic lecture-to-note generator**. Upload a lecture video (MP4/MKV/AVI) and receive a well-formatted Markdown study note containing: |
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- **ASR transcription** powered by OpenAI Whisper. |
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- **Video segmentation** using semantic & visual cues. |
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- **LLM summarisation** (e.g. GPT-4) for each segment, extracting key points, formulas and insights. |
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- **Image extraction** of key frames to illustrate the note. |
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- **Markdown assembly** into a single readable document. |
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### Installation |
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```bash |
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# Requires Python ≥ 3.10 |
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git clone https://github.com/your-name/Lec2Note.git |
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cd Lec2Note |
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python -m venv .venv && source .venv/bin/activate |
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pip install -r requirements.txt |
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``` |
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GPU inference: ensure CUDA and the matching PyTorch build are installed. |
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### Quick Start |
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#### 1. Web UI |
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Navigate to `https://huggingface.co/spaces/LRU1/lec2note` . |
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#### 2. CLI |
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```bash |
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python -m lec2note.scripts.run_pipeline --video path/to/lecture.mp4 --output notes.md |
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``` |
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#### 3. Required Environment Variables |
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```bash |
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export OPENAI_API_KEY=your_openai_api_key |
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export REPLICATE_API_TOKEN=your_replicate_api_token |
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export LOG_LEVEL=DEBUG(optional) |
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export AUDIO2TEXT_LOCAL=true|false(optional) |
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``` |
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### Directory Structure |
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```text |
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Lec2Note/ |
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├── app.py # Streamlit front-end |
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├── lec2note/ |
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│ ├── ingestion/ # Audio/video preprocessing & ASR |
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│ ├── segmentation/ # Semantic + visual segmentation |
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│ ├── processing/ # LLM summarisation & note generation |
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│ ├── synthesis/ # Markdown assembly |
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│ └── scripts/ # CLI entry points |
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└── tests/ # Test suite |
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``` |
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### Environment Variables |
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Some modules require the following environment variables: |
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- `OPENAI_API_KEY`: OpenAI access token. |
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- `WHISPER_MODEL`: Whisper model name, default `base`. |
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### Contributing |
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Pull requests and issues are welcome! See `DEVELOPER_GUIDE.md` for code conventions and workflow. |
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### License |
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Released under the Apache-2.0 license. |
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