# Meetily — Privacy-First AI Meeting Assistant with Local Transcription

> An open-source, self-hosted AI meeting assistant that provides real-time transcription, speaker diarization, and local summarization using Whisper and Ollama, with no cloud dependency.

## Install

Save in your project root:

# Meetily — Privacy-First AI Meeting Assistant with Local Transcription

## Quick Use
```bash
git clone https://github.com/Zackriya-Solutions/meetily.git
cd meetily
docker compose up -d
# Open http://localhost:3000
# Ensure Ollama is running locally for summarization
```

## Introduction
Meetily is an open-source AI meeting assistant that runs entirely on your machine. It captures audio, transcribes in real-time using Whisper or Parakeet models, identifies speakers via diarization, and generates meeting summaries through a local Ollama LLM. No audio ever leaves your device.

## What Meetily Does
- Transcribes meeting audio in real-time with sub-second latency using local Whisper or Parakeet models
- Identifies individual speakers through automatic speaker diarization
- Generates structured meeting summaries, action items, and key decisions via local LLMs
- Stores all transcripts and summaries locally in an encrypted database
- Works with any audio source including system audio capture and microphone input

## Architecture Overview
Meetily uses a Rust-based audio capture engine for low-latency system audio recording. The transcription pipeline runs Whisper.cpp or NVIDIA Parakeet locally, feeding segments into a diarization module that clusters speakers by voice embeddings. A Python backend orchestrates the pipeline and serves the web UI. Summarization uses Ollama to run local LLMs like Llama or Mistral against the full transcript.

## Self-Hosting & Configuration
- Install via Docker Compose with GPU passthrough for faster transcription on NVIDIA hardware
- Configure the audio source in settings: system audio loopback, microphone, or both
- Set the Ollama endpoint URL if running on a separate host; defaults to `localhost:11434`
- Choose your transcription model: `whisper-small` for speed or `whisper-large-v3` for accuracy
- Transcripts are stored in `~/.meetily/data`; back up this directory for persistence

## Key Features
- Fully local processing with zero data sent to external servers
- Real-time transcription at 4x faster than standard Whisper using optimized Rust audio pipeline
- Cross-platform support for macOS and Windows with native audio capture
- Speaker-labeled transcripts with automatic name assignment after initial identification
- Export to Markdown, JSON, or plain text for integration with note-taking tools

## Comparison with Similar Tools
- **Otter.ai** — Cloud-based with subscription fees; Meetily is free, local, and private
- **Granola** — Mac-only with cloud summarization; Meetily supports Windows and runs fully offline
- **Krisp** — Focused on noise cancellation; Meetily provides full transcription and summarization
- **Whisper (standalone)** — Raw transcription only; Meetily adds diarization, summarization, and a polished UI
- **Tactiq** — Browser extension for Google Meet; Meetily captures any audio source system-wide

## FAQ
**Q: Does Meetily require a GPU?**
A: No, but a GPU significantly speeds up transcription. CPU-only mode works with smaller Whisper models at slightly higher latency.

**Q: Which meeting platforms does it work with?**
A: Meetily captures system audio, so it works with Zoom, Google Meet, Microsoft Teams, Slack Huddles, or any application that outputs audio on your machine.

**Q: Can I use a custom LLM for summarization?**
A: Yes. Any model available through Ollama can be selected in settings, including fine-tuned models optimized for meeting notes.

**Q: How much disk space do transcripts use?**
A: Text transcripts are lightweight, typically under 100 KB per hour of meetings. Audio recordings, if enabled, require about 50 MB per hour.

## Sources
- https://github.com/Zackriya-Solutions/meetily
- https://meetily.ai

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Source: https://tokrepo.com/en/workflows/3270e558-4080-11f1-9bc6-00163e2b0d79
Author: AI Open Source