• 6 Posts
  • 303 Comments
Joined 1 year ago
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Cake day: April 27th, 2024

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  • If you travel to Japan, honestly just… Skip Kyoto. It is so full of tourists (national and international), you cannot possibly imagine unless you’ve seen it.

    Sure, there’s a lot of impressive temples there. But so is the rest of the country.

    We were lucky enough to spend 4 weeks in Japan earlier this year, and if I could do the trip again, I would straight-up skip Kyoto and Osaka.

    Rent a car, drive in some random direction. You’ll he a lot happier, it it will actually be your trip. By far the best memories coke from places not in any travel guide.









  • Thanks, had not heard of this before! From skimming the link, it seems that the integration with HASS mostly focuses on providing wyoming endpoints (STT, TTS, wakeword), right? (Un)fortunately, that’s the part that’s already working really well 😄

    However, the idea of just writing a stand-alone application with Ollama-compatible endpoints, but not actually putting an LLM behind it is genius, I had not thought about that. That could really simplify stuff if I decide to write a custom intent handler. So, yeah, thanks for the link!!


  • Thanks for your input! The problem with the LLM approach for me is mostly that I have so many entities, HASS exposing them all (or even the subset of those I really, really want) is already big enough to slow everything to a crawl, and to get bad results from all models I’ve tried. I’ll give the model you mentioned another shot though.

    However, I really don’t want to use an LLM for this. It seems brittle and like overkill at the same time. As you said, intent classification is a wee bit older than LLMs.

    Unfortunately, the sentence template matching approach alone isn’t sufficient, because quite frequently, the STT is imperfect. With HomeAssistant, currently the intent “turn off all lights” is, for example, not understood if STT produces “turn off all light”. And sure, you can extend the template for that. But what about

    • turn of all lights
    • turn off wall lights
    • turnip off all lights
    • off all lights
    • off all fights

    A human would go “huh? oh, sure, I’ll turn off all lights”. An LLM might as well. But a fuzzy matching / closest Levensthein distance approach should be more than sufficient for this, too.

    Basically, I generally like the sentence template approach used by HASS, but it just needs that little bit of additional robustness against imperfections.


  • Thanks for sharing your experience! I have actually mostly been testing with a good desk mic, and expect recognition to get worse with room mics… The hardware I bought are seeed ReSpeaker mic arrays, I am somewhat hopeful about them.

    Adding a lot of alternative sentences does indeed help, at least to a certain degree. However, my issue is less with “it should recognize various different commands for the same action”, and more “if I mumble, misspeak, or add a swear word on my third attempt, it should still just pick the most likely intent”, and that’s what’s currently missing from the ecosystem, as far as I can tell.

    Though I must conceit, copying your strategy might be a viable stop-gap solution to get rid of Alexa. I’ll have to pay around with it a bit more.

    That all said, if you find a better intent matcher or another solution, please do report back as I am very interested in an easier solution that does not require me to think of all possible sentence ahead of time.

    Roger.