r/ProgrammerHumor • u/PokeRestock • 2d ago

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u/audigex 1d ago

Yeah speech recognition doesn't require a full scale frontier level cloud LLM

You can run something like Whisper https://github.com/openai/whisper locally to transcribe the speech in about 1 second on a MiniPC, then check that for keywords

Then you can use something like Piper https://github.com/OHF-Voice/piper1-gpl to convert the reply back to speech. It's not quite as pretty sounding as something from eg ElevenLabs, but it works - on a mini PC it can handle voice in real time

I use both of those locally to run my own voice assistant for my smart home (indeed, Piper is made by Home Assistant for that exact purpose)

Obviously for a game they'd need more than one Mini PC - but you can scale it up for a lot less than $1k/day!

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u/Tristan_Cole 1d ago

If you want to interpret language, LLMs are much better than keyword scanning. The same keywords can indicate opposite things with a single “not” keyword, and there are about 50,000 generally well-known English words to consider.

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u/audigex 1d ago

That's true but beside the point here

Nobody is saying keyword detection is as good as an LLM. We're just saying a frontier cloud LLM is very expensive overkill for this kind of project

Keyword detection or basic local transcribing and a MUCH smaller local LLM, would do the job just as well

Whisper + a very small model could handle this without $1000/day in OpenAI API costs

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u/Tristan_Cole 1d ago

How are you going to implement keyword detection with enough logic to accurately parse the meaning behind all of the possible things that could be said to describe or correct someone’s driving?

That is literally a quintessential Natural Language Processing problem, for which LLMs are the best solution.