Hi! This is a short presentation for my hobby project, [TranscriptionSuite](https://github.com/homelab-00/TranscriptionSuite).

**TL;DR** A fully local and private Speech-To-Text app with cross-platform support, speaker diarization, Audio Notebook mode, LM Studio integration, and both longform and live transcription.

A personal tool that sprung into a full project. Fully agent-coded; I've taken some programming classes at university (MATLAB, Fortran) but I learned pretty much everything while working on this project - git, uv, toml files, PlantUML, etc. It has been an extremely fun and learning journey.

*If you're interested in more boring dev stuff, go to the bottom section.*

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Short sales pitch:

- **100% Local**: *Everything* runs on your own computer, the app doesn't need internet beyond the initial setup*
- **Multiple Models available**: *WhisperX* (all three sizes of the [`faster-whisper`](https://huggingface.co/Systran/faster-whisper-large-v3) models), NVIDIA NeMo [*Parakeet v3*](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3)/[*Canary v2*](https://huggingface.co/nvidia/canary-1b-v2), and [*VibeVoice-ASR*](https://huggingface.co/microsoft/VibeVoice-ASR) models are supported
- **Speaker Diarization**: Speaker identification & diarization (subtitling) for all three model families; Whisper and Nemo use PyAnnote for diarization while VibeVoice does it by itself
- **Parallel Processing**: If your VRAM budget allows it, transcribe & diarize a recording at the same time - speeding up processing time significantly
- **Truly Multilingual**: Whisper supports [90+ languages](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py); NeMo Parakeet/Canary support [25 European languages](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3); VibeVoice supports [50 languages](https://huggingface.co/microsoft/VibeVoice-ASR)
- **Longform Transcription**: Record as long as you want and have it transcribed in seconds; either using your mic or the system audio
- **Live Mode**: Real-time sentence-by-sentence transcription for continuous dictation workflows (Whisper-only currently)
- **Global Keyboard Shortcuts**: System-wide shortcuts & paste-at-cursor functionality
- **Remote Access**: Securely access your desktop at home running the model from anywhere (utilizing Tailscale) or share it on your local network via LAN
- **Audio Notebook**: An Audio Notebook mode, with a calendar-based view, full-text search, and LM Studio integration (chat with the AI about your notes)

📌*Half an hour of audio transcribed in under a minute (RTX 3060)!*


**If you're interested in a more in-depth tour, check [this](https://github.com/user-attachments/assets/688fd4b2-230b-4e2f-bfed-7f92aa769010) video out.**

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The seed of the project was my desire to quickly and reliably interface with AI chatbots using my voice. That was about a year ago. Though less prevalent back then, still plenty of AI services like GhatGPT offered voice transcription. However the issue is that, like every other AI-infused company, they *always* do it shittily. Yes is works fine for 30s recordings, but what if I want to ramble on for 10 minutes? The AI is smart enough to decipher what I mean and I can speak to it like a smarter rubber ducky, helping me work through the problem.

Well, from my testing back then speak more than 5 minutes and they all start to crap out. And you feel doubly stupid because not only did you get your transcription but you also wasted 10 minutes talking to the wall.

Moreover, there's the privacy issue. They already collect a ton of text data, giving them my voice feels like too much.

So I first looking at any existing solutions, but couldn't find any decent option that could run locally. Then I came across [RealtimeSTT](https://github.com/KoljaB/RealtimeSTT), an extremely impressive and efficient Python project that offered real-time transcription. It's more of a library or framework with only sample implementations.

So I started building around that package, stripping it down to its barest of bones in order to understand how it works so that I could modify it. This whole project grew out of that idea.

I built this project to satisfy my needs. I thought about releasing it only when it was decent enough where someone who doesn't know anything about it can just download a thing and run it. That's why I chose to Dockerize the server portion of the code.

The project was originally written in pure Python. Essentially a fancy wrapper around `faster-whisper`. At some point I implemented a *server-client* architecture and added a notebook mode (think of it like calendar for your audio notes).

And recently I decided to upgrade the frontend UI from Python to React + Typescript. Built all in Google AI Studio - App Builder mode for free believe it or not. No need to shell out the big bucks for Lovable, daddy Google's got you covered.

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Don't hesitate to contact me here or open an issue on GitHub for any technical issues or other ideas!

I 100% agent-coded a fully featured, local, private & open source audio transcription & diarization app - TranscriptionSuite