211–220 of 251 posts
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.
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> there are better models that are state of the art now like Nemotron and Parakeet both by Nvidia 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.
Parakeet is certainly faster on my machine (m3 max), but I can't stand using it over Whisper for dictating my prompts. It makes a lot more mistakes, possibly because (like you mentioned) large portions of the speech will pause / stutter as I think about what to include. 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…
For round two after a typo-laden transcript, I’m dictating and annunciating with great passion as I read the first transcript to make sure I don’t miss a beat. It’s kind of fun because it’s a little performance.
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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.
What Apple laptop has “jet-engine fan noise”?
So I ended up organically testing and ending up with SpeechAnalyzer because it was not only fast and accurate enough, but you also see live results as you talk. It also has speaker identification and people can register their voices. And it does all processing on device.
It also had the best model for Indian accented English, given she lives in India.
So I was quite impressed, but the holy grail to me is transcription that does speaker identification but also works in a standard family conversation, where multiple people interrupt each other all the time.
I will say though, I'm really curious as to what Claude Code Desktop uses for their voice mode, because it seems even better than Apple's, and it provides realtime feedback. Maybe they're using apple's model?
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 a…
I’m on iPhone 17 Pro Max, 27 beta 3. 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.
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Is that copyright infringement?
This hasn't been tested in court. But there's a high chance that model weights are not copyrightable, only the code to generate them is. 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…
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 a…
Why is my Siri still so horrible at dictating text then?
In my own tests a few months ago, it was faster than both small/large Whisper models I compared it to with accuracy competitive with both of them (each model had different quirks).