Its so good that I'm not sure that it's possible to get any better. Speech to text seems like basically a solved problem, if not now then definitely in 5 years. I don't know if any of these speech to text businesses will work in the long run, but for consumers they are great. My guess is the 2030 version of Apple's SpeechAnalyzer will be so good that nobody will need to use 3rd party software.
https://github.com/cjpais/Handy
It's great for me when configured to use Parakeet v3.
Kind of a bait and switch. How can we test the product with such short time limits and what, exactly are you offering if all the processing is done on device by Apple?
Supports SRT/TXT/VTT or JSON-with-optional-word-level-timestamps output and progress meter.
Also it can transcribe live system audio.
This is useless test and benchmark when you have these day Whisper-V3-Large and Whisper V3-Turbo that you can faster than realtime on 5 years old macbook on apple sillicon (ANE). They didn't even compared to parakeet v2 or parakeet v3. And only english language...
However, what's funny is, RIP to a lot of the paid apps that simply wrap Whisper, I'm sure Apple will make a native GUI such as a recorder app for macOS that obviates the need for these wrappers, which everyone seems to be vibe coding these days.
> The new API cuts word error rate by 3.5 to 4x on the same audio: from 9.02% to 2.12% on clean speech
Shouldn't they have said "cuts error rate by 78%" or something?
Im looking for the same experience I have when talking to chatGPT. As for past two years or more talking to GPT within it's app and on my iPhone Pro Max 15 it runs smooth as butter :-). This is the experience I was and still am hoping with Apple, but Im thinking all the extra layers of privacy and security might be slowing them down?
Overall, Apple who is suing Open AI should just buy them and let me have the best conversational AI out there baked into my old ass iPhone. Because as so far the new Siri on my old phone (tho again GPT works great talking to it and for years) doesnt come close. It's the same old "Could you try that again," Siri. BOO!!!
Apple published no accuracy numbers for SpeechAnalyzer (or for SFSpeechRecognizer, ever, as far as I can tell), so the migration question has been guesswork. Short version: the new API cuts WER 3.5-4x vs the old one (2.12% vs 9.02% on test-clean), and it also beat Whisper Small on both splits at about 3x the speed. The old API came in last on clean speech, behind even Whisper Tiny.
On "why should I trust a vendor benchmark": the Whisper column reproduces OpenAI's published LibriSpeech WERs within +0.11 to +0.42 on all six measurements (same corpus, same normalizer, same scorer for every engine), and the raw per-utterance transcripts are downloadable from the article if anyone wants to rescore with their own normalizer.
Limitations worth stating up front: English only, read speech rather than meeting audio, one machine. Precise per-engine timing isn't in the article yet because the accuracy runs shared the machine with a dev workload; WER is load-independent, timing isn't.
Two things that might interest people migrating: SFSpeechRecognizer sends audio to Apple's servers unless you set requiresOnDeviceRecognition, and with SpeechAnalyzer, finishing your input stream is not enough to end a session. If you never call finalizeAndFinishThroughEndOfInput(), the results sequence never terminates and your await hangs forever. I found that one because it was shipping in my own app.
Happy to answer questions about the harness or the normalizer.
If I start typing and the existing text is in Spanish, then a sensible default is to select the Spanish keyboard I have installed and let me adjust otherwise.
App developers should also be allowed to supply mini-dictionaries within a context to allow autocorrect to work correctly in that context, so for example in this thread [SpeechAnalyzer, API, Whisper, Parakeet, Nemotron] should be supplied so that these terms are autocorrected.
- it implies that error could be increased n-times, but a 15x _increase_ in 9% error would be an error rate of 135%, which is nonsensical.
- a reduction from 90% error to 20% error is clearly a bigger improvement in rightness to a reduction from 9% to 2%. One is “almost all wrong to almost all right”, the other is “more right”, but they are both a 4.5x reduction in error which means that the 4.5 quantity doesn’t have a constant meaning.
The answer is something like log odds ratios, but that introduces the additional need for a reader to know what that is, and that would be unusual.
1. In Shortcuts app, make shortcut named "AI Voice Mode" (or whatever you want, YMMV)
2. Set it to run the ChatGPT action "Voice Mode" (requires at least the minimum paid tier, I think)
3. To trigger, say "Hey Siri, AI Voice Mode" (or whatever you called the shortcut)
New Siri is impressive in that it answers satisfactorily now 80% of the time vs 10% with old Siri.
But it’s slow as shit. GPT, Claude, and Gemini can answer me in 5-10 seconds. Google AI Mode can answer in 2 seconds.
New Siri usually takes 25 seconds to respond to me. This morning it timed out (with strong network connection) when asked a simple multiplication question.
Apple would never do that, if anything they did not offer their Siri with the most advanced AI on iPhone 16 Pro Max, which is one year-old only.
On the more cutting edge front, Granite Speech 4.1 has proven to be a reliable workhorse for me, but it is larger than Parakeet. Cohere Transcribe is interesting, but how strong it is seems to vary more from task to task.
Parakeet Unified 0.6B came out a few months ago, combining both online streaming and offline transcription into one model, and that is one that I need to test more, but it seems promising.
As others have mentioned macOS 27/iOS 27 is supposed to have a new model, particularly on devices with 12GB of RAM or more. I have not actually seen the option to enable that new model yet, though, despite being on the beta on a device that meets the requirements. Maybe a benchmark would reveal that it is already active?
Edit: Getting downvoted by Apple fanboys for telling the truth is a badge of honor.
Is parakeet state of the art? It always transcribes speech fragments for me, like if I stutter and say "m-m-m-map" parakeet will dutifully transcribe "m m m map". Which I guess could be a good thing or a bad thing depending on what you want. Whisper does not do that however.
I do like cohere transcribe a lot.
I started using a few open source apps for transcription and eventually subscribed to a paid one...
On paper, it's not hard to compete, but for this use case, a few rough edges make it really frustrating to use. Like a keyboard that sometimes doubles the letter "e"
Automatic dictionary, seamless language switch, no issues with accents, etc... Putting the effort in the last mile makes a world of difference.
If anyone has better options, I'm willing to have a look. The best open source solution I found was Handy, and I currently use Wispr Flow
It can struggle with proper nouns but will return something phonetically similar.
My main gripe is that it requires a separate model download per language. I understand the why they did this (to save disk space). But it makes multi-lingual audio hard to transcribe unless you know ahead of time the languages in the audio.
As an app developer the biggest win from using Apple's model is I don't have to bundle it in my app so my app looks much smaller. If a user has many transcription apps each one could have their own model. If Apple's model is used only one copy is needed.
Also, just out of curiosity, seems like everyone and their mother is making Whisper wrappers, how is your app different?
MOSS-Transcribe-Diarize
Looks like Voxtral and Nvidia's Nemotron are best.
[0] https://artificialanalysis.ai/speech-to-text/non-streaming
The appeal is that users only have to download it once across all apps that use it. Instead of convincing a user to give a couple gigs for just your one app
Splitting the audio in multiple segments and firing it up without hitting the maximum limit of concurrent decoding streams makes it blazing fast. Fair enough you loose the cut, but it’s good enough for just podcast. In one minute it chews through one hour of audio. This on an iPhone 17 Pro.
> What this means if you just want good transcription
> If you are on a current iPhone or Mac, the best on-device transcription engine for English is already in the operating system, and the private option is no longer the compromise option
Cloud models are usually protected by trade secret laws, leaking them would get you in trouble. However if the model is made available publicly, as long as you don't break the law to get them, anything after that would be fair game unless Apple can prove that humans have significant authorship over the weights, which hasn't been tested and is a significant burden to prove/disprove.
The Jedi Hand Wave-y nature of the way people talk about AI these days is going to make reigning in the AI superpowers nearly impossible. Because there are people out here who believe models of this quality are easily replicated or reverse engineered. Neither is really doable on any reasonable timeline by people who are not AI experts. Real AI experts. Not TF/PyTorch monkeys or Agent Slop Slingers.
And those people are already highly incentivized to not make anything performing better than SOTA models open source.
With whisper v3 turbo, I can almost always live with the few mistakes because the overall stream-of-thought word-salad I provide is still clear at a high level. The bits and pieces of context seem to help, that I might leave out if typing and focused more on traditional conciseness / clean writing. With parakeet, I needed to do frequent editing even for shorter bits of speech.
I realize some applications prioritize the latency.
Thanks for the tip and if Im not mistaken it's similar to asking Siri to ask chatGPT to ask XYZ?
How can you be sure this isn't leaking data or metadata to Apple? Can Apple really be trusted?
Effectively, it sort of does that, but really it just listens to the wakeword and opens/switches to the requested app & modality.
FWIW, I get a very different functional result using the Shortcut method vs. asking Siri to delegate natively. To compare, I asked Siri (non-beta here) now to "ask ChatGPT <x>" and I got a top-card with some fairly low quality SEO-ranked weblinks.
Edit: all that said, the app is irrelevant. What I want to say is that live transcripts on iOS using Apples frameworks works very well. Only thing I miss is diarization support.
My initial Mac version actually had three additional steps that you could toggle, obviously at the cost of some speed. That is what the website talks about, although nowadays for my own use I've reduced that to just one step and found that it's pretty great. I've got a new version in test to tidy that up, but still lets you define as many steps as you want.
Apparently MOSS-Transcribe-Diarize is quite good too as it released only a few days ago.
Superwhisper does a lot more than just provide a whisper/parakeet UI so I’m not sure Apple will destroy them so easily
- parakeet usually runs on Bfloat16. NPU doesn't support that
- CPU is not as fast as the NPU for these ops on A-series, and even on modern CPUs, there's a latency delay
- Parakeet latency is fine but "fine" may not be good enough for Apple's UX team.
- CPU increases power consumption over dedicated float blocks
So I would say that Parakeet was a non-option for Apple to ship, although it should be in the benchmarks anyways!
And yea, Nvidia's Parakeet v3 is good enough for my own just local transcription most of the time.
When I need local transcription to be more reliable and I don't have the energy to proof read a long ramble, I still often just pop open chatGPT, dictate, cut, paste.
But we're pretty much already to the point where local transcription models can replace cloud ones for personal use. They're still a bit rough around the edges in terms of polish and latency, but plenty of people are fine with that to avoid yet another app subscription and not having to worry about wondering what's potentially happening with their data.
[0] https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize
[1] https://huggingface.co/spaces/OpenMOSS-Team/MOSS-transcribe-...
Apple's new SpeechAnalyzer is the most accurate on-device speech engine we tested. It beat every Whisper model we ship, including Whisper Small, on both the clean and the noisy half of LibriSpeech, while running roughly three times faster than Small. And the API it replaces, SFSpeechRecognizer, came last on clean speech: behind even Whisper Tiny, a 40MB model.
| Engine | test-clean WER | test-other WER | Model size |
|---|---|---|---|
| Apple SpeechAnalyzer (iOS/macOS 26) | 2.12% | 4.56% | system |
| Whisper Small (WhisperKit CoreML) | 3.74% | 7.95% | ~460MB |
| Whisper Base | 5.42% | 12.51% | ~140MB |
| Whisper Tiny | 7.88% | 17.04% | ~40MB |
| Apple SFSpeechRecognizer (legacy) | 9.02% | 16.25% | system |
Lower is better: WER is word error rate, the percentage of words an engine substitutes, drops, or invents. LibriSpeech test-clean is 2,620 utterances of clean read speech; test-other is 2,939 harder, noisier utterances. Every engine ran fully on-device on an Apple M2 Pro (32GB, macOS 26.5.1).
Apple SpeechAnalyzer2.12%
Whisper Small3.74%
Whisper Base5.42%
Whisper Tiny7.88%
SFSpeechRecognizer (legacy)9.02%
With iOS 26 and macOS 26, Apple replaced SFSpeechRecognizer with a new API, SpeechAnalyzer and SpeechTranscriber. It published no accuracy figures for either one. So every developer deciding whether to migrate, and everyone comparing Apple's built-in recognition against Whisper, has been guessing.
We ship both Apple engines and three Whisper models side by side in Inscribe, a private on-device AI workspace, which puts us in an unusual position: we can run all five through identical production code paths on the same machine and the same audio. So we did.
Yes. This is the clearest result in the data. The new API cuts word error rate by 3.5 to 4x on the same audio: from 9.02% to 2.12% on clean speech, and from 16.25% to 4.56% on noisy speech. There is no accuracy trade-off to weigh; the new API wins everywhere we measured, and it produces punctuated, cased text where the legacy engine's output is rougher.
Put differently: an hour-long meeting transcribed with the legacy API contains roughly four times as many wrong words as the same meeting through SpeechAnalyzer. If your app still uses SFSpeechRecognizer for anything longer than a voice command, the migration is worth it on accuracy alone.
The more surprising result: Apple's new engine also beat Whisper Small, the largest model we ship, by a comfortable margin on both splits, at roughly a third of Whisper Small's compute time per second of audio. For English, on Apple hardware, the built-in engine is now the strongest on-device option we can measure.
Whisper keeps two real advantages. It covers far more languages (SpeechTranscriber supports around 30 locales), and it runs anywhere, not just on Apple platforms with OS 26. But for English transcription on a current iPhone or Mac, the days of Whisper being the automatic accuracy pick are over.
We changed our own product on this result: Inscribe's Auto engine now prefers SpeechAnalyzer for the languages it supports, and Whisper for everything else. Shipping a benchmark and ignoring it in your own defaults would be a strange kind of honesty.
All five engines ran comfortably faster than real time: between roughly 12x and 40x on the M2 Pro, meaning an hour of audio transcribes in about 1.5 to 5 minutes on-device. SpeechAnalyzer was about 3x faster than Whisper Small per second of audio while beating it on accuracy. We are deliberately not printing a precise per-engine timing table yet: the accuracy runs shared the machine with a development workload, which does not affect WER but does add noise to timing. We will update this page with timings from a dedicated idle run.
A benchmark from a company that sells one of the engines should be treated with suspicion. Ours has two properties designed for that suspicion.
We used LibriSpeech precisely because OpenAI published Whisper's WER on it. If our harness measured Whisper correctly, our numbers should land on theirs. They do, on all six measurements:
| Engine / split | Ours | OpenAI published | Delta |
|---|---|---|---|
| Whisper Tiny, test-clean | 7.88% | 7.6% | +0.28 |
| Whisper Base, test-clean | 5.42% | 5.0% | +0.42 |
| Whisper Small, test-clean | 3.74% | 3.4% | +0.34 |
| Whisper Tiny, test-other | 17.04% | 16.9% | +0.14 |
| Whisper Base, test-other | 12.51% | 12.4% | +0.11 |
| Whisper Small, test-other | 7.95% | 7.6% | +0.35 |
The small, consistent positive offset (a slightly stricter text normalizer plus CoreML quantization) is what honest reproduction looks like; random error would scatter in both directions. Since the same corpus, normalizer, and scorer produced the Apple columns, the numbers nobody else can check inherit the validation from the numbers anyone can.
Every per-utterance hypothesis for both Apple engines is downloadable below, next to the reference text and per-utterance WER. Disagree with our normalization? Rescore it yourself.
The benchmark found a shipping bug in Inscribe. Our Apple-engine file import fed audio to SpeechAnalyzer and closed the input stream, but never called finalizeAndFinishThroughEndOfInput(). Without that call the analyzer never delivers its final results, and the import hangs forever. It had gone unnoticed because our Auto setting preferred Whisper. The fix shipped the same day, and it is part of why we publish the harness details: measuring your own product carefully has a way of finding the things you were not looking for.
If you are on a current iPhone or Mac, the best on-device transcription engine for English is already in the operating system, and the private option is no longer the compromise option. Inscribe uses exactly the engines measured here: SpeechAnalyzer where it supports your language, Whisper where it does not, all fully on-device, nothing uploaded. The benchmark is not separate from the product; it is how we decide what the product does.
> If you are on a current iPhone or Mac
Presumably if you don't trust apple you wouldn't purchase their products and even if you were for example forced to use it via work or something you wouldn't use this feature ... so it doesn't really change the calculus as presented by this article - IF you ALREADY HAVE a MODERN Mac (and trust apple) this is your best option
Also doesn't seem to be tailored to Apple hardware (i.e. no MLX or ANE variant/implementation)
Even for those sorts of apps, MacParakeet which I've been using is FOSS so no payment needed. In reality these days with AI the ability to spin up a free and/or OSS competitor falls to zero.
https://alternativeto.net/software/little-snitch/
https://www.g2.com/products/little-snitch/competitors/altern...
There are many alternatives for trying to find out what’s going on. If you don’t want to bother, and most people don’t, well, what else is there to say?
It is generally a good idea to know what software is phoning home, if you can pinpoint it.
If you have any software recommendations, I’d be happy to know.
A new VAD I found though is FireRedVAD which has better benchmark results than TEN and Silero by far
Generally labs don't release MLX or ANE versions and we must rely on finding someone who's converted it
Parakeet is not multilingual so not directly comparable
Where do you see 16GB? MOSS is smaller than Parakeet at 1.82GB