AudioNotes: Turn Audio and Video into Structured Notes, Locally

Turning a meeting recording into text usually means listening to it over and over. AudioNotes moves that whole process onto your own machine: upload an audio or video file, or record straight from the browser, and FunASR transcribes it locally first, then a local LLM running on Ollama organizes the transcript into a Markdown note. GitHubDaily recommended it in February 2025.

Core features

• Audio/video to transcript: upload a file to transcribe, or record directly in the browser; the first run needs an internet connection to download a roughly 2.15GB speech model.
• Notes and Q&A: the transcript is organized into structured Markdown by an LLM, and you can keep asking follow-up questions — who’s responsible, when, what was decided — with conversation history preserved.
• Hot words and speaker separation: domain-specific hot words improve accuracy; enabling speaker separation in .env produces SRT subtitles tagged by speaker.
• Local-first: recognition and Q&A run entirely on your machine, with no third-party AI calls by default, and the web service is only accessible locally by default.

Typical use cases

• Meeting summaries: turn a recording into notes, then directly ask who owns what, by when, and what was agreed.
• Interview and podcast archiving: journalists and researchers convert raw material into searchable Markdown archives.
• Lecture review: convert lecture videos or voice memos into transcripts for quick review later.

Quick start

First install and start Docker Desktop and Ollama, then run the following in an empty directory:

ollama pull qwen3.5:2b
curl -fsSL https://github.com/harry0703/AudioNotes/raw/main/docker-compose.yml -o docker-compose.yml
curl -fsSL https://github.com/harry0703/AudioNotes/raw/main/.env.example -o .env
docker compose up -d --remove-orphans

Once it’s up, visit http://localhost:15433/ — the default username and password are both admin. The default image runs fine on CPU alone; on Linux with an NVIDIA GPU, you can enable GPU acceleration with docker-compose.gpu.yml as described in the repo.

Summary

This suits anyone who regularly organizes meeting, interview, or lecture recordings but doesn’t want to hand that content to a cloud AI service. The default model combination is friendly to Chinese speech recognition, and Q&A runs entirely on local Ollama, so there’s no third-party AI API key to obtain. The project is MIT-licensed and still maintained, with its most recent update in August 2026. A few caveats: first use requires internet access to download images and models; Docker is recommended with at least 12GB of RAM (16GB for long recordings), plus around 10GB of free disk space; on a shared machine, change the default credentials in .env; and recognition quality is affected by background noise, accents, and overlapping speakers.