Low latency inference is very useful in voice-to-voice applications. You say it is a waste of power but at least their claim is that it is 10x more efficient. We'll see but if it works out it will definitely find its applications.
replyThis is not voice-to-voice though, end-to-end voice chat models (the Her UX) are completely different.
replyI haven't found any end-to-end voice chat models useful. I had much better results with separate STT-LLM-TTS. One big problem is the turn detection and having inference with 150-200ms latency would allow for a whole new level of quality. I would just use it with a prompt: "You think the user is finished talking?" and then push it to a larger model. The AI should reply within the ballpark of 600ms-1000ms. Faster is often irritating, slower will make the user to start talking again.
replyI think it's really useful for agent to agent communication, as long as context loading doesn't become a bottleneck. Right now there can be noticeable delays under the hood, but at these speeds we'll never have to worry about latency when chain calling hundreds or thousands of agents in a network (I'm presuming this is going to take off in the future). Correct me if I'm wrong though.
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