At this point, I would not recommend ignoring Parakeet TDT 0.6b v2/v3 (english-only versus multilingual). Those models have been out for a year, give or take, and they are both accurate and fast. I would choose Parakeet over Whisper in almost all situations these days. Parakeet works great on my iPhone 15 Pro Max, so if an app is going to ship a dedicated model, I strongly recommend investigating Parakeet.
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?
The dictation speech recognition button on iOS/macOS is pretty weak compared to the newer on device portion of SpeechAnalyzer referenced in this article. I'm not even sure SFSpeechAnalyzer was used for the OS dictation. They're pretty ram constrained. I agree either is hard to use if you need any level of preciseness/consistency though.
Im hoping Apple gets the new Siri working better on older phones. I was excited to use it but the latest beta / Siri runs too slow on my iPhone Pro Max 15.
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!!! Anyone else tired of that UX?
Yeah, ChatGPT voice is great experience vs. Siri on that phone. In case you haven't done something like this already:
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)
This is a pretty slick integration, but yeah, if it were baked in that would be all the better.
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.
this is amazing. if i had a mac i would try to reverse engineer the code, extract the weights and port it to something that works on linux/windows like torch or burn. then put the code on github and weights on a torrent site. lifes too short to let apple keep their models exclusive.
Aside from the legality of it, I think you are underestimating how complex it can be to do that. It is possible in theory but not something that will be a fun side quest like you are making it seem.
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.
Whisper small/tiny/base are almost four years old (they were not updated for Whisper v2 or v3). Is there really nothing better to benchmark against by now?
Just ran it against Whisper-Large-V2 on a math lecture (my primary use case for ASR is subtitling math lectures), and it was substantially faster and only slightly worse. Very usable for live transcription though I'll probably stick with whisper for the time being since I don't really need the subtitles to be generated in real time.
Been using it for a podcast app I have been developing for half a year lol (I hope I publish it by version 27) and I can confirm it’s real fast.
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.
You could perhaps run over the segment splitting points (plus a few seconds back and forward) in a second batch then merge the results in the end so you don't miss anything.
If this isn't open source/weights and can't run locally, I don't see how this is a replacement for Whisper or other open models, e.g. within Home Assistant.
It's a local model so it's essentially open weight such that you could feasibly export it somehow since it's already on the laptop somewhere. Apfel is a wrapper app like ChatGPT but using Apple Foundation Models, I assume something similar will happen with this transcription model.
It's not open weight, but the point is to be an on device (and thus local, privacy preserving) option. The article mentions that as the caveat
> 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
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
Whisper is the wrong model to benchmark against, or rather, there are better models that are state of the art now like Nemotron and Parakeet both by Nvidia, as well as Mistral's Voxtral and Cohere Transcribe.
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.
Also, this test is English-only, while a strong point of other models is to understand different languages without first having to say which one (so you don't need 3 different keyboard shortcuts if you wanna dictate in 3 languages day-to-day)
As an Australian, Apples voice models have always sucked. I've tried using stt (again) more recently and its improved, but i'm so tired of having to Americanize my voice to get it to figure out what the hell i'm saying.
The Two Yoots problem. Do you use d's in place of t's such as dees/dems/dose/dere? I have a heavy queens accent so you'll hear me say things like "deres tree uh dem ova dere."
As a Texan first, American second, I sympathize with this statement. Siri can't understand me probably 25% of the time. I use STT for iMessage while in the car, and half the time it will take 3+ times to either get it right or me give up, and hope to remember to text them by hand when I next stop.
I use Systran/faster-whisper-medium for real-time subtitling, but you need to get used to the context it's used it and the weirdness it translates into. Parakeet has great mandarin>CN text, but running that + a translation model has been tricky and I never got it fast.
Reminds me of the time my neighbours must have wondered if I was having some kind of a breakdown when trying out really basic MacOS voice recognition back in the early 2000s. There was a keyboard shortcut and you could say something like "phone number for firstname lastname" and it would theoretically show you that phone number. Thing is it didn't seem to like a British accent, so I spent a good hour trying out different accents, rotating through various US accents, Australian, South African, Canadian and so on. It seemed to respond best to some kind of a melange of Californian / Australian.
Not too far off what happened, although thankfully I wasn't actually trying to do anything other than test it. Going to take the opportunity afforded by Scottish TV comedy here, and make a very tenuous link to intercultural exchange so I can post my favourite Rab C Nesbitt scene, hands across the sea indeed: https://www.youtube.com/watch?v=uKxPH_QH940
Interesting - I don’t think I’ve ever seen anyone from the UK refer to talking in a “British accent” before since we are normally aware of the wild regional variations.
Fair point! I think it's a tic from being English and having lived in Scotland for quite a while so I autocorrect "English" to "British", but I've over-corrected here. (Also perhaps something to do with "British English").
From my experience, Speech-to-text falls way short of Wispr flow and I would assume the ones that are said to be better than that. It lacks context awareness and formatting
Not if your app is a Web wrapper, which so many of these are.
If you use SwiftUI (the native recommendation by Apple), it severely penalizes you, if you want to paint outside the lines (which is a big reason that I don't use SwiftUI for shipping apps). It's insanely easy to write an app that is 100% in line with HIG.
> RIP to a lot of the paid apps that simply wrap Whisper
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
I built my own because I was frustrated with a lot of the free options. Largely because a lot of them had an upsell to be able to do the secondary post-processing step with an LLM. And it wouldn't pick up things like emojis properly or say numbers. Because of that, I left quite a lot of options in there for customising and adding additional steps, etc. Feel free to take a look: dictator.robgough.net
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.
Yeah, apple will be optimizing a model to work on ANE and then turn it into a native app. My only hope is that it has a reasonable api so that I can use that as a generic input source across iOS / macOS that’s equivalent to the ubiquity of the keyboard.
One would hope, though I suspect they may want to make things a bit flashier than “we made the audio transcription on the keyboard not terrible” in the changelog given the amount of work that’s gone in.
I don’t know how Apple divides computation between the GPU and the Neural Engine, but one major benefit, especially for real-time transcription on laptops, is the improved power and thermal efficiency. I noticed better accuracy after switching my app to SpeechAnalyzer, and I suspect part of that improvement for me came from the microphone no longer having to compete with jet-engine fan noise.
This particular product used Whisper, so that was obviously the right model to compare it against. Further this is explicitly on device, and Nemotron 3.5, as one example, is 2.5GB for the model.
And if someone were broadly comparing all on-device models (instead of just looking at how this new on-device ones compares to what a specific product uses), Nemotron 3.5's WER are actually a bit higher than what they report for SpeechAnalyzer, for both tests.
The canadian government will provide lots of historical data for curious citizens, many of which are recordings of interviews from decades and decades ago. For a book project this allows me to make a hours of audio searchable through a GUI application I have developed that has a voxtral backend.
Whisper v3 is still the best (by far) when it comes to poor quality input (say background audio from a security camera), though remains more susceptible to hallucination so it's a bit of a tradeoff.
Any chance you can benchmark against whisper large and large v3 turbo? These run comfortably on older Macbooks and are still far more accurate in real life dictation compared to even the parakeet models( despite ASR leaderboards) with an RTF < 1.
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.
Vs Voxtral would be a better comparison. No other model, open or closed, has been able to hit such a low AER (Acronym Error Rate ;)) for my meeting transcripts. Seems to understand/infer all the technobabble I use at work. Never have to edit anything. Whisper was catastrophically bad.
I typically disable autocorrect on Apple products because of this, cautiously optimistic about their improved speech models, but definitely worried that it's going to 'correct' technical jargon to more common words.
I stopped reading after seeing they compared only with Whisper Small, Base, Tiny
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...
For my current purposes, I need a speech-to-text model/API to also emit word-level timestamps - for now, that makes ElevenLabs's Scribe v2 the best multiplatform, multi-language choice though it does look like this SpeechAnalyzer API provides them (although only for English).
Every single asr model I tested so far did not support timestamps properly though. Some use external aligner to create timestamp, but the accuracy is still much inferior than whipser in case the audio is noisy.
Finally. I‘d be delighted though if they actually implemented language autodetection (like everywhere else) though. There’s little more frustrating in my day to day than having dictated half a page to find that it‘s complete gibberish because Apple forces you to select the right language first…
Same with the keyboard. Apple is completely incapable of taking context into account for the input mechanisms of the operating system.
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.
125 comments
[ 2.9 ms ] story [ 29.5 ms ] threadOn 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?
Also, just out of curiosity, seems like everyone and their mother is making Whisper wrappers, how is your app different?
MOSS-Transcribe-Diarize
The dictation speech recognition button on iOS/macOS is pretty weak compared to the newer on device portion of SpeechAnalyzer referenced in this article. I'm not even sure SFSpeechAnalyzer was used for the OS dictation. They're pretty ram constrained. I agree either is hard to use if you need any level of preciseness/consistency though.
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!!! Anyone else tired of that UX?
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.
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.
Looks like Voxtral and Nvidia's Nemotron are best.
[0] https://artificialanalysis.ai/speech-to-text/non-streaming
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.
https://apfel.franzai.com/
> 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
If trust Apple, then no need for privacy from Apple
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
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.
[0] https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize
[1] https://huggingface.co/spaces/OpenMOSS-Team/MOSS-transcribe-...
If you use SwiftUI (the native recommendation by Apple), it severely penalizes you, if you want to paint outside the lines (which is a big reason that I don't use SwiftUI for shipping apps). It's insanely easy to write an app that is 100% in line with HIG.
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
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.
Listen and transcribe felt like the easiest thing to do.
Distavo.com
The source is open for anyone to use, and the builds are in github.
I found quite interesting that claude didn't help too much on how to publish to SetApp until Fable.
(Genuine question - I'm a happy Whisper user but am always looking for improvements).
And if someone were broadly comparing all on-device models (instead of just looking at how this new on-device ones compares to what a specific product uses), Nemotron 3.5's WER are actually a bit higher than what they report for SpeechAnalyzer, for both tests.
Anytime I’m talking to an Indian on the other end, I have to have them repeat everything 2 or 3 times.
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.
https://huggingface.co/spaces/hf-audio/open_asr_leaderboard
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...
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.