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.

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.

Then you need to fix your TTS not the system that interprets transcribed words.

If you do you should use speech-to-phrase instead of whisper. I've found it to be more reliable if you're using it with automation commands instead of an LLM. In your automations you can setup phrases as a trigger. It has support for aliases as well and I believe that it also supports templating for sentences.

Looks to be 20gb of vram