🎙️ AI Meeting Notes Assistant

An AI-powered application that converts meeting audio into text, generates intelligent meeting summaries, and enables users to ask questions about past meetings using Generative AI.

Built with Python, Google Gemini AI, LangChain, Speech-to-Text Processing, Vector Search, and Streamlit, this project helps teams automatically document meetings and retrieve insights from previous discussions.

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🚀 Features

• ✅ Upload meeting audio files (.MP3, .WAV, .M4A)
• ✅ Speech-to-Text transcription (Audio → Text)
• ✅ OpenAI Whisper for meeting transcript generation
• ✅ AI-powered meeting summarization
• ✅ Generate professional meeting notes automatically
• ✅ Remove unnecessary filler words and noise
• ✅ Extract key discussion points and action items
• ✅ Ask questions about past meetings (Meeting Q&A)
• ✅ Intelligent retrieval of previous meeting context
• ✅ Streamlit-based interactive UI
• ✅ Gemini AI integration for smart responses
• ✅ Modular and scalable architecture

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🛠️ Tech Stack

Technology	Purpose
Python	Core Backend Development
Streamlit	Interactive User Interface
Google Gemini AI	LLM for summarization & Q&A
LangChain	LLM orchestration
Speech-to-Text Model	OpenAI Whisper
NLP	Text processing & cleaning
Vector Database / Embeddings	Meeting memory & retrieval
RAG	Q&A on previous meetings

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💡 Project Overview

The AI Meeting Notes Assistant automatically converts meeting audio into structured notes and enables intelligent interaction with past meeting discussions.

Instead of manually reviewing long meeting recordings, users can:

• 📂 Upload meeting audio
• 📝 Automatically generate transcripts
• 🤖 Get AI-powered summaries
• ✅ Extract key decisions and action items
• 💬 Ask questions from previous meetings

Example Questions

• “What was discussed about deployment?”
• “Who was assigned frontend work?”
• “What were the action items from last week?”
• “When is the delivery deadline?”

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⚙️ How It Works

Step 1 → Upload Meeting Audio

Supported Formats:

• MP3
• WAV
• M4A

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Step 2 → Speech-to-Text Processing

The model converts speech into clean text transcripts.

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Step 3 → Transcript Cleaning

Removes:

• Filler words
• Repetitions
• Irrelevant conversations

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Step 4 → AI Summarization

Generates:

• 📌 Key Discussion Points
• 📝 Meeting Summary
• ✅ Action Items
• 🎯 Important Decisions

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Step 5 → Meeting Q&A (RAG)

Users can ask questions about previous meetings and get context-aware answers powered by AI.

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📂 Project Structure

Meeting-Notes-Project/
│── modules/                     # Core processing modules
│── app.py                       # Main Streamlit application
│── config.py                    # Configuration settings
│── requirements.txt             # Dependencies
│── README.md                    # Documentation

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🎯 Use Cases

• Business Meetings
• Team Standups
• Client Calls
• Interview Documentation
• Educational Sessions
• Project Planning Meetings
• Meeting Knowledge Management