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Which HA voice assistant ollama model are you using? (feddit.online)
submitted 1 year ago by to c/homeassistant@lemmy.world
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What is everyone using for the LLM model for HA voice when selfhosting ollama? I've tried llama and qwen with varying degrees of understanding my commands. I'm currently on llama as it appears a little better. I just wanted to see if anyone found a better model.

Edit: as pointed out, this is more of a speech to text issue than llm model. I'm looking into the alternatives to whisper

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[–] 1 point 1 year ago (5 children)

I don't use a HA so not familiar with the details of what's out there, but where are you having the problems? Is voice recognition fine and the model just isn't always following the directions precisely? If not, what are you using, whisper or something else? (I'm in search myself of a better voice to text that's local). Certainly by now there are local models fine tuned for being HA, which would work better than a general purpose that could drift or misunderstand common commands.

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  • [–] [S] 1 point 1 year ago* (4 children)

    The issue is mainly voice recognition. Even if I pronounce stuff clear it thinks I've said something else.
    Using whisper in HA.

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  • [–] 2 points 1 year ago*

    The model isn't going to help there, then. I've been messing with some of the whisper variants like faster-whisper, also tried an older one called nerd-dictation, haven't yet found one that doesn't creep in garbage from time to time. And of course you have to make sure the data the VR is getting is clean of noise and a good level. It's tough to troubleshoot. The advantage is that LLMs might be able to pick through the crap and figure out what you really want, if there's enough trigger words there. I even had an uncensored one once call me out on a typo I made, which I thought was hilarious. But getting 100% accuracy with so many places that can error is a challenge. It's why I suggested finding or making (!) a fine tuned version that self limits what it responds to, to help put another filter to catch the problems. Ironic that the dumber things work better by just not doing anything when the process breaks.

    Having used Voice Attack on the Windows side, the same thing applied. It wasn't that VA or Windows were better at picking up a voice command, but a matter of setting the probability of a match for a command low enough to catch a partial hit, while high enough to weed out the junk. So that's probably the goal here, but that gets into the coding for the voice recognition models, and I'm not good enough to go that deep.

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  • [–] 2 points 1 year ago* (last edited 1 year ago) (1 child)

    That's not going to be fixed with a different LLM model though. I'm experiencing similar problems. If my stt is bad then, then the LLM just gets even more confused or requires a big model that doesnt run efficiently on my local GPU. won't trigger my custom automations because the tools don't consider custom automations phrases.

    Speech2phrase improves accuracy for utterances that are basic like turn on X, or anything specified in an automation, but then struggles for other speech.

    My next project is to implement a router that forwards the utterance to both speech2phrase and whisper and try to estimate which is correct.

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