This is an automated archive made by the Lemmit Bot.
The original was posted on /r/selfhosted by /u/MLwhisperer on 2025-07-05 18:28:43+00:00.
Scriberr
Scriberr is a self-hostable offline AI audio transcription app. It leverages the open-source Whisper models from OpenAI, utilizing the high-performance WhisperX transcription engine to transcribe audio files locally on your hardware. Scriberr also allows you to summarize transcripts using Ollama or OpenAI's ChatGPT API, with your own custom prompts. Scriberr supports offline speaker diarization with significant improvements. This beta introduces the feature to chat with your transcripts using Ollama or OpenAI.
Github repo: https://github.com/rishikanthc/Scriberr App website: https://scriberr.app/
Call for Beta Testers
Hi all, It's been several months since I started this project. The project has come a long way since then and has amassed over 900 stars on Github. Now, I'm about to release the first stable release v1.0.0. In light of this, I am releasing a beta version for seeking feedback before the release to smooth out any bugs. I request anyone interested to please try out the beta version and provide quality feedback.
Updates
The stable version brings a lot of updates to the app. The app has been rebuilt from the ground up to make it fast and responsive and also introduces a bunch of cool new features.
Under the hood
The app has been rebuilt with Go for the backend and Svelte5 for the frontend and runs as a single binary file. The frontend is compiled to static website (plain HTML and JS) and this static website is embedded into the Go binary to provide a fast and highly responsive app. It uses Python for the actual AI transcription by leveraging the WhisperX engine for running Whisper models. This release is a breaking release and moves to using SQLite for the database. Audio files are stored to disk as is. With the Go app, users should see noticable differences in responsiveness of the UI and UX.
New Features and improvements
- Fast transcription with support for all model sizes
- Automatic language detection
- Uses VAD and ASR models for better alignment and speech detection to remove silence periods
- Speaker diarization (Speaker detection and identification)
- Automatic summarization using OpenAI/Ollama endpoints
- Markdown rendering of Summaries (NEW)
- AI Chat with transcript using OpenAI/Ollama endpoints (NEW)
- Multiple chat sessions for each transcript (NEW)
- Built-in audio recorder
- YouTube video transcription (NEW)
- Download transcript as plaintext / JSON / SRT file (NEW)
- Save and reuse summarization prompt templates
- Tweak advanced parameters for transcription and diarization models (NEW)
- Audio playback follow (highlights transcript segment currently being played) (NEW)
- Stop or terminate running transcription jobs (NEW)
- Better reactivity and responsiveness (NEW)
- Toast notifications for all actions to provide instant status (NEW)
- Simplified deployment - single binary (Single container) (NEW)
- New simple, uncluttered UI for better UX (NEW)
Screenshots
You can checkout screenshots in the app website https://scriberr.app/ or in this folder on the git repo https://github.com/rishikanthc/Scriberr/tree/v1.0.0/screenshots
Requesting feedback
I'm excited about the first stable release for this project. I am soliciting feedback for the beta, so that I can smooth out any issues before the first stable release. I request interested folks to please try the beta version and provide me quality feedback either on this post thread or by opening an issue on Github. All feedback and feature requests are most welcome :)
If you like the project, please consider leaving a star on the Github page. It would mean a lot to me. A big thanks to the community for your interest and support in this project :)