I'd been using MacWhisper for meeting notes, which is great and actually very powerful, but before every call I had to set it up, and half the time I forgot and walked out of an hour-long meeting with nothing written down. I wanted what I was using MacWhisper for, but launchable in one shortcut that just starts in the background and then leaves me alone.
You press a global shortcut and a small bar shows up at the bottom of the screen (there's another shortcut to hide it if you'd rather not see it working), and it records your mic and the system audio. The system side is captured with a Core Audio process tap, so no bot joins the meeting and it works with any app, or just a conversation in the same room.
Transcription runs on-device, with two engines to choose from. A fast one (Parakeet-TDT) and a slower, more accurate one (Whisper large-v3-turbo via WhisperKit), both through Core ML on the Neural Engine. The first time you transcribe it pulls the models from Hugging Face, and after that it works fully offline. Audio never leaves the Mac, it's sandboxed, there's no account and it collects no telemetry.
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 quick optional note. It shows up inline in the transcript at that timestamp. I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down, which for me defeated the point of having notes taken automatically. I use it all the time. When I paste the transcript into an LLM afterwards, the important moments are already flagged so it doesn't gloss over them, which is more noticeable in longer meetings with lots of topics. 2. While it's recording there's a rough live recap so you can glance at what's been said so far, again behind a global shortcut.
Each session is just a folder on disk containing mic.wav, system.wav, transcript.json and transcript.md. The system track gets speaker labels (Speaker 1, Speaker 2, and so on) and your mic track is labelled Microphone. You can't rename the detected speakers yet, but I'm working on it.
It won't summarise for you (you get a clean transcript plus the key-moment markers, then bring your own LLM), and it's Apple Silicon only, because the models run on the Neural Engine.
There's also an optional Google Calendar connection that reads your events to auto-name sessions.
If you've used Granola, Otter or Fathom, the difference is they put a bot in your call or do the work in the cloud, sometimes both, and I wanted neither.
I built Trace for myself to fix a pain point I had with existing tools, and it's done that for me, so I wanted to share in case it helps someone else. Open to thoughts, roasting, or any other feedback.
It's £9.99 on the App Store.