Build Meeting Transcription & Action Item Extractor

Build a Meeting Transcription and Action Item Extractor

What You'll Learn

In this tutorial, you will learn how to build an automated meeting assistant using the OpenAI Whisper API for speech-to-text conversion and GPT-4o for intelligent analysis. You will create a Python application that transcribes audio files and extracts actionable tasks, deadlines, and summaries. This workflow demonstrates the power of combining specialized models for high-accuracy results.

This project is essential for modern productivity tools. It reduces manual note-taking time by 80% or more. You will gain hands-on experience with API integration, prompt engineering, and asynchronous data processing.

Prerequisites

Before starting, ensure you have the following:

• An OpenAI API key with access to Whisper and GPT-4o models.
• Python 3.9 or higher installed on your machine.
• Basic knowledge of Python programming and virtual environments.
• A sample audio file (MP3 or WAV) of a meeting for testing.

Setting Up Your Environment

Start by creating a dedicated project directory. This keeps your dependencies isolated and clean. Open your terminal and run the following commands to set up your workspace.

mkdir meeting-extractor
cd meeting-extractor
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

Next, install the required libraries. The openai library handles API calls efficiently. The python-dotenv package helps manage your secret keys securely. Install these packages using pip.

pip install openai python-dotenv

Create a .env file in your root directory. Store your API key here to avoid hardcoding it in your scripts. This is a critical security best practice. Add your key as follows:

OPENAI_API_KEY=your_actual_api_key_here

Initializing the OpenAI Client

You need to initialize the client to interact with OpenAI's servers. Import the necessary modules in your main script, main.py. Load the environment variables immediately to ensure authentication works.

import os
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

Verify your connection with a simple test. This step prevents debugging later if the key is invalid. Print a success message if the client initializes correctly.

try:
    models = client.models.list()
    print("Connection successful!")
except Exception as e:
    print(f"Error connecting: {e}")

Transcribing Audio with Whisper API

The Whisper API converts spoken language into text. It supports multiple languages and handles background noise well. Upload your audio file to the API for transcription. Ensure your file is under 25 MB for standard uploads.

python
audio_file_path = "meeting.mp3"
with open(audio_file_path, "rb") as audio_file:
    transcript_response = client.audio.transcriptions.create(
        model="whisper-1",
        file=audio_fi

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