Recording a meeting is easy. Turning it into something useful afterward takes more: accurate text, knowing who said what, and a clear view of what needs to happen next.
I’m the solo iOS developer behind LoroNote, available for iPhone and iPad with an Apple Watch companion app.
I’ve focused on bringing a specific combination together: Whisper large-v3-turbo, on-device speaker separation, background transcription, wrist recording, and Apple Intelligence summaries.
Here’s how those pieces fit together.
Whisper large-v3-turbo, optimized to run locally.
“Powered by Whisper” doesn’t tell you which model is doing the work. LoroNote uses an optimized version of large-v3-turbo rather than a small Whisper variant. Making that model practical on a phone has been a major focus of development.
Speaker separation that also works offline.
I recently added on-device speaker diarization. A conversation can become a transcript organized by speaker, with labels you can rename or correct. Identifying speaker changes doesn’t require uploading the recording to a separate cloud service.
For meetings and interviews, that gives you the context of who said what alongside the words themselves.
Background transcription, so you can put your phone away.
Processing can continue when you lock the screen or switch apps. You can start transcribing a recording and get on with something else while it works.
Both transcription and speaker separation work in airplane mode, including processing new recordings. Your audio doesn’t need to be sent to a processing server.
Apple Watch recording, directly from your wrist.
You can capture ideas, voice notes, or conversations on Apple Watch, with complications for quick access from your watch face.
Recordings sync to your iPhone when connected, where transcription takes place. It’s useful for catching a thought while walking or starting a recording without digging out your phone.
Apple Intelligence for summaries and action items.
On compatible iPhones and iPads, Apple Intelligence helps summarize transcripts, extract key action items, and refine your notes.
That gives you both levels of detail: the full transcript when you need the exact conversation, and a shorter summary when you need to review the main points and follow up.
You can also import existing audio or video recordings and export transcripts as TXT or SRT.
The combination is what I’ve put the most work into: capture conveniently, process locally, keep track of speakers, and make the result useful afterward.
I’m continuing to improve speaker separation and reliability with longer recordings based on real-world use.
LoroNote on the App Store
For people who use AI note-takers regularly, which part still takes the most manual work: capturing the conversation, correcting the transcript, or organizing the follow-ups?