JVjv22222·Jun 14, 2026
I'm seeing a lot right here: https://github.com/FluidInference/FluidAudio
In Show HN: Trace – Offline Mac meeting transcripts you can flag mid-callFluidInference
FluidAudio
Swift2025–2026
FluidInference / FluidAudio
FluidAudio
Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.
11Distinct people
14Mentions
0Explicit recommenders
3.3KGitHub stars
What to know before exploring
What it is
Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.
GitHub
Where it is used
- Resonant — Resonant | ✓ | macOS voice workspace for dictation, meetings, and ambient work context. Uses FluidAudio for local transcription and speaker diarization. | Source
- Local Narrator — Local Narrator | — | Privacy-first iOS audiobook reader that reads EPUB and PDF books aloud with on-device text-to-speech. Uses FluidAudio Kokoro TTS for local English and Spanish narration. | Source
- 4 more documented examples
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 8:03:49 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month14 dated mentions
Observed mentions: 2025-07-13 01:58:39 UTC to 2026-07-14 08:31:59 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jul 2025 | 2025-07-01 00:00:00 UTC | 2025-08-01 00:00:00 UTC | 1 | 1 | 0 |
| Aug 2025 | 2025-08-01 00:00:00 UTC | 2025-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2025 | 2025-09-01 00:00:00 UTC | 2025-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 1 | 0 | 1 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 3 | 1 | 2 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 1 | 0 | 1 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 1 | 0 | 1 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 3 | 1 | 2 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 3 | 0 | 3 |
What Hacker News says
3 sourcesNice! 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
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apps are built around https://github.com/FluidInference/FluidAudio, if anyone is curious. Their readme has a big list of similar apps as well.
Show HN: On-device transcriber that's 97% accurate at identifying speakers
I’ve spent the last seven months building a tool I wish I’d had in my previous roles. MimicScribe is a macOS menu bar app that fits the "AI notetaker" category. It has accurate on-device speaker identification (a first possibly?), real-time meeting talking points for discovery calls, and a fully keyboard- and voice-driven interface.
I believe the accuracy of the speaker ID system is its biggest strength. I used fluid audio’s port of (https://github.com/fluidInference/FluidAudio) Pyannote's community-1 as a base. To improve accuracy, the system uses grammar structure cues from the Parakeet STT to mask by sentence. By taking a second set of samples within that mask for cluster assignment, it leverages the fact that most people don’t finish each other's… sandwiches in business meetings. It tends to slightly oversegment, as I’ve found it much
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easier to merge segments or reassign a speaker than it is to untangle an incorrect merge. https://github.com/MimicScribe/benchmarks/blob/main/diarizat...
The app provides in-meeting talking points using a prompt tuned for discovery type calls. It can suggest probing questions to help you extract more detail or helps you refocus on the big picture with “magic wand” type questions (e.g. “how would your ideal system work”). Getting low latency models to provide novel, relevant, and totally not hallucinated information is a bit of a reach and it tends to restate the transcript frequently but little gems do come from it sometimes so it’s best to think of it as a source of inspiration and be a vigilant gatekeeper.
It’s set up so recording can be started and ended via holding a keyboard shortcut instead of connecting to your calendar service. I prefer this for privacy and to keep transcript history from getting cluttered. Tapping the shortcut shows and hides an always-on-top overlay on your active screen regardless of whether you have other apps full-screen or not. Beyond simple navigation, you can also use voice commands to make post-meeting corrections or additions, for instance, you can simply say "merge this speaker with that speaker" to clean up the transcript.
It also has push-to-talk/dictate functionality with LLM cleanup - what the app started as but that tool was developer catnip, soo many of them.
A developer friend who’s worked in finance reviewed the site and said he’d bounce because the privacy story wasn’t strong enough so I added a completely on-device mode and a bring-your-own-key option. Using cloud models does add a lot to the experience, including context aware speaker merging and fragment cleanup, summary items during meetings, action items attributed, etc. On-device mode is completely free and the speaker identification is still very useful.
The privacy story is my biggest worry with the app, particularly since its target audience is more technical people. I’d love to get people's thoughts on it and any feedback would be super helpful.