I'm the developer of Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app. Please bear with me though, I'm confident that this is at least a little novel.

I primarily built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You activate Trace by pressing a global shortcut (configurable), which reveals a small bar at the bottom of your screen (there's also a keystroke and/or option to hide it entirely if you'd rather not see it at all).

As I was building it I wanted to bake in a couple of workflows I'd wished for in other transcription apps.

1. Mid-meeting you can press another global shortcut to mark a "key moment" and type a note. The note shows up in the resulting transcript inline at that timestamp. I wanted to add this because I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down in a separate app like Obsidian, which I then needed to add context to, which took me out of the meeting. I use it all the time. If I paste the transcript into an LLM afterwards (which I find myself doing more and more these days) the important moments are flagged so it doesn't gloss over them. This is more noticeable in longer meetings with lots of topics. 2. With another keyboard shortcut you can summon a rough live recap (subtitles, basically) to quickly recap what's just been said.

Trace uses standard macOS microphone and system recording APIs to capture both sides of the conversation as two separate tracks and then runs the system side through on-device diarization to identify speakers. Right now we only label them as "Speaker 1", "Speaker 2", etc but there are plans for speaker labelling in the future. You can also show a "live recap" as the call is happening to review what someone just said.

All transcription models run on your machine. To be clear though, Trace doesn't do any of the summarising itself, it just produces a markdown transcript, so if you want summaries then you need to pass the output to an AI.

The app is sandboxed and your audio/transcripts are never uploaded anywhere - they just exist as audio files and markdown on disk. The only network call Trace is required to make is on the first run to download the speech and speaker models (around 500MB) from Hugging Face, and after that it can be used fully offline. If enabled, a Google Calendar integration can auto-name sessions but that needs a network connection.

The app is £9.99 on the macOS App Store. I've been using it every day for months now and I'm super happy with how it's improved my workflow. Feedback very welcome.

Nice! I really like how many variations on this idea are coming out. MacWhisper used to be great, but is kinda of a buggy mess now.

I'm making my own, for personal use. I did a survey of many and they all (that I could find) skip the fundamentals.

The major issues that I've run into:

- Crash recovery. Most of these apps are incredibly buggy and crash all the time, taking the recorded audio with them. Macwhisper is incredibly bad at this.

- Disk space. Many of these apps save wav files to disk. After a few hours of meetings, you may end up with gigabytes eaten.

- Microphone bleed. People don't always use headphones, the system mic will pick up the speaker sounds, causing duplicate (approximately) transcriptions.

I've yet to find a solution that handles all these correctly, let alone having high quality transcriptions.

Anyway, most of these apps are built around https://github.com/FluidInference/FluidAudio, if anyone is curious. Their readme has a big list of similar apps as well.

This is an excellent product and exactly what I've been looking for. But most of my meetings are done on my company Mac, and they definitely won't let me install this kind of software, even though I'd be willing to pay for it myself.

The key moments feat is neat. Been working on a free opensource offline transcriber that runs fast on CPU and does diarization too

https://github.com/kouhxp/yapsnap

I don’t have this particular use case right now but if anything it feels like LLMs and their distilled on prem models are starting to kill SaaS simply because it becomes more and more tenable to build a “complete software” in a short time frame. That’s freaking awesome. Good idea and love the return of the good old you buy, you own it mentality

Those transcription times are fast fast. What model/library do you use?

I'd love to have a purchase option not tied to the App Store if possible. I don't use an Apple account with my Mac, but I would love to try Trace.

This looks sick. I was going to download it but for $10 I am more willing to attempt asking Claude to implement something like it, than to purchase.

I would be more willing to purchase if it was open source and I could build from source to try it first.

Which Speech-to-Text is used? Is it possible to configure it? This might be crucial for supporting languages other than English - the model that comes built-in with macOS fails completely for German.

I will be happy to spend £10 on this. One feature question though -- does it continue transcribing the meeting even if I've turned my volume down / muted it?

This looks like a good approach, though I would expect this to be a native macOs feature within 12 months -- this seems totally like it fits into their product roadmap.

Agreed with JohnBiz, the moment flagging is interesting and unusual, and a nice contrast to passive transcription. I only recently learned about MacWhisper (I'm Windows primarily) and was floored to learn how expensive the Pro option is. Nowadays it's not so hard to have some-level of DIY transcription, so crazy that it's priced with a premium.

What's your diarization pipeline? Pyannote?

I'd taken a different approach that used a LLM clean-up pass to summarize and progressively compress the transcript for ultra-long content, but I like the idea of targeted "pay attention here" flags.

Super interesting! How accurate is the local model to transcribe audio compared to other cloud services? E.g. Google Meet, Otter, Granola, etc.

I've been looking for this exact thing!

Does it support multiple languages?

I don't see how this is different to literally the dozens of other offline transcription apps, many open source even unlike this one.