How to Build a Google Meet Recording Processor

I use google meet recording transcription processing to transcribe recordings and extract action items. This removes the manual work that piles up when your team relies on Google Workspace but still copies data by hand.

The system connects to the Google Meet API, pulls or pushes the data you need, and runs on a schedule so it stays current.

What You Need

• Python 3.9+
• Google Cloud service account with Meet API enabled
• The Drive API + Whisper library
• A Google Meet resource shared with your service account

Step 1: Set Up Google API Access

pip install Drive API + Whisper google-auth python-dotenv

from google.oauth2.service_account import Credentials
from dotenv import load_dotenv
import os

load_dotenv()

def get_credentials():
    return Credentials.from_service_account_file(
        os.getenv("GOOGLE_CREDENTIALS_PATH"),
        scopes=["https://www.googleapis.com/auth/meet"]
    )

Step 2: Connect to the Service

from googleapiclient.discovery import build

def get_service():
    creds = get_credentials()
    service = build("meet", "v1" if "meet" != "admin" else "directory_v1", credentials=creds)
    return service

Step 3: Build the Core Logic

import json
from datetime import datetime

def process_data(service):
    # Fetch current data
    print(f"Fetching data at {datetime.now().isoformat()}")

    # Your processing logic here
    results = fetch_and_transform(service)

    print(f"Processed {len(results)} items")
    return results

def fetch_and_transform(service):
    # Replace with your specific API calls
    raw_data = []  # service.get_data()
    transformed = [transform_item(item) for item in raw_data]
    return transformed

def transform_item(item):
    return {
        "id": item.get("id", ""),
        "processed": True,
        "timestamp": datetime.now().isoformat()
    }

Step 4: Write Results Back

def write_results(service, results, target_id):
    # Write processed data back to Google Meet
    print(f"Writing {len(results)} results to {target_id}")

    for result in results:
        # Your write logic here
        pass

    print("Write complete")

Step 5: Schedule the Automation

def main():
    service = get_service()
    target_id = os.getenv("MEET_TARGET_ID")

    results = process_data(service)
    write_results(service, results, target_id)

    print(f"Automation complete: {datetime.now().isoformat()}")

if __name__ == "__main__":
    main()

Run this with cron or a task scheduler:

# Run every day at 7am
0 7 * * * cd /path/to/project && python build_google_meet_recording_processor.py

What to Build Next

Add error notifications so you know when the sync fails. A simple Slack message on error saves you from discovering stale data in a client meeting.

Related Reading

• Building an AI Meeting Assistant - getting more from meetings with less manual work
• Building a Simple AI Dashboard with Google Sheets - getting more from Google Workspace with automation
• Building Automated Task Lists from Meeting Transcripts - getting more from meetings with less manual work