This project is a powerful tool that harnesses the of OpenAI's Whisper ASR and GPT-4 models to automate the process of generating meeting minutes from audio recordings, as well as text summary and key point extraction.
This Python-based application transcribes audio, provides summaries, extracts key points and action items, and performs sentiment analysis.
You should have a basic understanding of Python and an OpenAI API key. You can get an API key here. This project was built using Python 3.11.5.
Create a virtual environment and install the requirements:
python -m venv venv
source venv/bin/activate
pip install openai
pip install python-docx
pip install python-dotenv
Add your OpenAI API key to the .env
file.
- Transcribe Audio with Whisper ASR. Refer to
transcription.py
- Summarize and Analyze Transcript with GPT-3. Refer to
sentiment_analysis.py
- Extract Key Points and Action Items. Refer to
sentiment_analysis.py
- Generate Meeting Minutes. Refer to
meeting_minutes.py
- Save Meeting Minutes as a Word Document. Refer to
save_as_docx.py
- Improve the accuracy of the Whisper ASR model by training it on a custom dataset.
- Experiment with prompt engineering to enhance model performance.
- Customize the system messages to provide more context to the GPT-4 model for better results.