MoM AI — AI-Powered Minutes of Meeting

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Recall.ai - API for meeting recording

If you’re looking for a meeting recording API, consider checking out Recall.ai, an API that records Zoom, Google Meet, Microsoft Teams, in-person meetings, and more.

Sponsored by Recall.ai — The unified API for meeting bots.

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A clean, modern web application that transcribes meeting recordings and generates structured Minutes of Meeting using your preferred AI providers. Upload once, get professional meeting minutes automatically.

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Features

• AI Transcription — Deepgram, AssemblyAI, Sarvam AI, or ElevenLabs
• Smart Summarization — Any OpenAI-compatible LLM (OpenAI, OpenRouter, Groq, Together, Azure, Ollama, etc.)
• Background Processing — Celery + Redis; upload and come back when it's done
• Auto Cleanup — Files auto-deleted after a configurable number of days
• Two Roles — Admin (full config access) + User (upload & view only)
• Modern UI — Light-themed, fully responsive, smooth animations
• Docker-ready — One command to spin up everything

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Screenshot

Quick Start

1. Configure environment

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cp .env.example .env
# Edit .env — set your API keys

2a. Docker (recommended)

bashCopy
docker compose up --build

App is available at http://localhost:5000

2b. Local development

Prerequisites: Python 3.12+, Redis, ffmpeg

bashCopy
# Install dependencies
pip install -r requirements.txt

# Start Redis (one terminal)
redis-server

# Start Celery worker (another terminal)
celery -A celery_worker:celery worker -l info

# Start Celery beat for periodic cleanup (another terminal)
celery -A celery_worker:celery beat -l info

# Start Flask app
python run.py

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Default Login

Field	Value
Username	admin
Password	admin@123

Change these in .env before deploying.

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Admin Panel

Log in as admin and navigate to Settings to configure:

Tab	What you can set
LLM Provider	Base URL, API key, model name
Speech Provider	Active provider, API keys for all 4 providers
Storage	Retention days (how long files are kept)

Navigate to Users to create/manage user accounts.

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LLM Provider Examples

The app uses the OpenAI client format — works with any compatible endpoint:

Provider	LLM_BASE_URL	Example model
OpenAI	https://api.openai.com/v1	gpt-4o
OpenRouter	https://openrouter.ai/api/v1	anthropic/claude-opus-4
Groq	https://api.groq.com/openai/v1	llama-3.3-70b-versatile
Together AI	https://api.together.xyz/v1	meta-llama/Llama-3-70b-chat-hf
Ollama (local)	http://localhost:11434/v1	llama3.2

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Speech Providers

All four providers produce speaker-labelled transcripts when diarization is available (e.g. [Speaker 0]: …, [speaker_0]: …). This gives the LLM enough context to correctly attribute decisions and action items in the Minutes of Meeting.

Provider	Model	Key capabilities
Deepgram	Nova-3	Fastest; word-level diarization via utterances; paragraph-formatted output
AssemblyAI	Best (auto)	High accuracy; speaker diarization
Sarvam AI	saarika:v2.5	Batch Job API — no duration limit; best for Indian languages + English; diarization (beta)
ElevenLabs	Scribe v2	Multichannel support (stereo → per-channel speakers); single-channel diarization

Sarvam note: The direct /speech-to-text endpoint only accepts ≤ 30 seconds of audio. The provider now uses the full Batch Job pipeline (/speech-to-text/job/v1) which handles meeting recordings of any length.

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Project Structure

├── app/
│   ├── __init__.py          # App factory + seeding
│   ├── config.py            # All configuration
│   ├── extensions.py        # db, login_manager, celery
│   ├── models.py            # User, Meeting, SystemConfig
│   ├── auth/                # Login / logout
│   ├── dashboard/           # Dashboard home
│   ├── meetings/            # Upload, list, detail, delete
│   ├── admin/               # Settings + user management
│   ├── api/                 # REST endpoints (status polling)
│   ├── tasks/               # Celery tasks (process + cleanup)
│   ├── providers/
│   │   ├── llm/             # OpenAI-compatible LLM client
│   │   └── speech/          # Deepgram, AssemblyAI, Sarvam, ElevenLabs
│   ├── utils/               # ffmpeg audio conversion
│   ├── static/              # CSS + JS
│   └── templates/           # Jinja2 HTML templates
├── celery_worker.py         # Celery entry point
├── run.py                   # Flask entry point
├── Dockerfile
├── docker-compose.yml
└── .env.example