alt Hacker NewsI don't know anything about these models, but I've been trying Nvidia's Parakeet and it works great. For a model like this that's 9GB for the full model, do you have to keep it loaded into GPU memory at all times for it to really work realtime? Or what's the delay like to load all the weights each time you want to use it?
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Personally I run an ollama server. Models load pretty quickly.
There's a distinction between tokens per second and time to first token.
Delays come for me when I have to load a new model, or if I'm swapping in a particularly large context.
Most of the time, since the model is already loaded, and I'm starting with a small context that builds over time, tokens per second is the biggest impactor.
It's worth noting I don't do much fancy stuff, a tiny bit of agent stuff, I mainly use qwen-coder 30a3b or qwen2.5 code instruct/base 7b.
I'm finding more complex agent stuff where multiple agents are used can really slow things down if they're swapping large contexts. ik_llama has prompt caching which help speed this up when swapping between agent contexts up until a point.
tldr: loading weights each time isn't much of a problem, unless you're having to switch between models and contexts a lot, which modern agent stuff is starting to.
Same here. I haven’t found an ASR/STT/transcription setup that beats Parakeet V3 on the speed/accuracy tradeoff spectrum: transcription is extremely fast (near instant for a couple sentences, 1-3 seconds for long ramblings), and the slight accuracy drop relative to heavier/slower models is immaterial for the use case of talking to AIs that can “read between the lines” (terminal coding agents etc).
I use Parakeet V3 in the excellent Handy [1] open source app. I tried incorporating the C-language implementation mentioned by others, into Handy, but it was significantly slower. Speed is absolutely critical for good UX in STT.
[1] https://github.com/cjpais/Handy