{"id":30740200,"url":"https://github.com/sameeh07/meeting-audio-summarisation","last_synced_at":"2026-05-18T03:33:50.373Z","repository":{"id":311692528,"uuid":"1044612311","full_name":"Sameeh07/Meeting-audio-summarisation","owner":"Sameeh07","description":"Transcribe meeting audio and summarises.","archived":false,"fork":false,"pushed_at":"2025-09-03T20:16:49.000Z","size":14,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-03T22:10:23.173Z","etag":null,"topics":["huggingface-transformers","llama3-8b","quantisation","whisper"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"#  Meeting Minutes Generator from Audio\n\nThis project demonstrates how to transcribe meeting audio into text and generate structured meeting minutes (summary, key points, takeaways, and action items) using **OpenAI Whisper**, **Hugging Face Transformers**, and **quantization** techniques for efficient model loading.\n\n##  Features\n\n* Transcribe audio files into text using:\n\n  * [OpenAI Whisper](https://platform.openai.com/docs/guides/speech-to-text)\n  * Open-source Hugging Face models\n* Generate detailed **meeting minutes** in Markdown format:\n\n  * Summary with attendees, date, and location\n  * Key discussion points\n  * Takeaways\n  * Action items with owners\n* **Quantization with BitsAndBytes**: Efficient 4-bit model loading using Hugging Face `BitsAndBytesConfig`\n* **Tokenizer integration with Hugging Face API**: Apply chat templates and manage inputs for LLaMA models\n* Supports GPU acceleration on Google Colab\n\n## ⚙️ Installation\n\nRun the following inside Colab to install dependencies:\n\n```bash\n!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n!pip install -q requests bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 openai\n```\n\n## 🔑 Setup\n\n1. **Mount Google Drive** (to load your audio file):\n\n   ```python\n   from google.colab import drive\n   drive.mount(\"/content/drive\")\n   audio_filename = \"/content/drive/MyDrive/folder/audio_extract.mp3\"\n   ```\n\n2. **Add API Keys**:\n\n   * Hugging Face: `HF_TOKEN`\n   * OpenAI: `OPENAI_API_KEY`\n\n   Store them in **Colab Secrets** for security:\n\n   ```python\n   from google.colab import userdata\n   hf_token = userdata.get(\"HF_TOKEN\")\n   openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n   ```\n\n## 🧠 Models \u0026 Techniques\n\n* **Whisper (OpenAI)** for audio-to-text transcription\n* **Meta-LLaMA 3.1 (8B-Instruct)** for meeting minutes generation\n* **Quantization with BitsAndBytes** to run large models on limited GPU(T4 free) memory:\n\n\n## ▶️ Usage\n\n1. 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