Hey r/LocalLLaMA,

I wanted to share an update for my open-source project, Speakr. My goal is to build a powerful transcription and note-taking app that can be run completely on your own hardware, keeping everything private.

The whole pipeline is self-hosted. It uses a locally-hosted Whisper or ASR model for the transcription, and all the smart features (summarization, chat, semantic search, etc.) are powered by a local LLM.

**Newest Feature: LLM-Powered Event Extraction**

The newest feature I've added uses the LLM to parse the transcribed text for any mention of meetings or appointments, pulling them out as structured data, and it is smart enough to understand relative dates like "next Wednesday at noon" based on when the recording was made. [You can then export these found events as normal `.ics` files for your calendar](https://murtaza-nasir.github.io/speakr/user-guide/transcripts/#event-extraction).

It is designed to be flexible. It works with any OpenAI-compatible API, so you can point it to whatever you have running. I personally use it with a model hosted with vLLM for really fast API-like access, but it works great with Ollama and other inference servers as well.

**Customizable Transcript Exports**

To make the actual transcript data more useful, I also added a templating system. This allows you to format the output exactly as you want, for meeting notes, SRT subtitles, or just a clean text file.

It has been a lot of fun building practical tools that can actually use a full end-to-end local AI stack. I'd love to hear your thoughts on it.

[GitHub Repo](https://github.com/murtaza-nasir/speakr) | [Documentation](https://murtaza-nasir.github.io/speakr) | [Screenshots](https://murtaza-nasir.github.io/speakr/screenshots)

My self-hosted app uses local Whisper for transcription and a local LLM for summaries & event extraction