andrew-w

We have not released the weights, but it is fully available to use in your websites or applications. I can see how our wording there could be misconstrued -- sorry about that.

So glad you enjoyed it! We've been able to significantly reduce those text hallucinations with a few tricks, but it seems they haven't been fully squashed. The /imagine command only works with the image at the moment, but we'll think about ways to tie that into the personality and voice. Thanks for the feedback!

Not something we had thought to do tbh, but would definitely enhance the experience. And, should be reasonable to do. Thanks!

We have not released the weights, but it is fully available to use in your websites or applications. I can see how our wording there could be misconstrued -- sorry about that. You can absolutely create a vTuber persona. The link in the post is still live if you want to create one (as simple as uploading an image, selecting a voice, and defining the personality). We even have a prebuilt UI you can embed in a website, just like a youtube video.

Haha, I kind of get that reaction. Convincing the world "this was hard to do" is generally not easy. Re: user uploads, we're operating in good faith at the moment (no built-in IP moderation). This hasn't been an issue so far. Current pricing reflects our operating costs. Each end-user gets a dedicated GPU for the duration of a call, which is expensive. Advancements on the model-side should eventually allow us to parallelize this.

I wonder how it would come across with the right voice. We're focused on building out the video layer tech, but at the end of the day, the voice is also pretty important for a positive experience.

Thanks for the feedback. The current avatars use a STT-LLM-TTS pipeline (rather than true speech-to-speech), which limits nuanced understanding of pronunciations. Speech-to-speech models should solve this problem. (The ones we've tried so far have counterintuitively not been fast enough.)

This isn't natively supported -- we are continuously streaming frames throughout the conversation session that are generated in real-time. If you were building your own conversational AI pipeline (e.g. using our LiveKit integration), I suppose it would be possible to route things like this with your own logic. But it would probably include jump cuts and not look as good.

Thanks! And sorry! I can see how our wording there could be misconstrued. With a real-time model, the streaming infrastructure matters almost as much as the weights themselves. It will be interesting to see how easily they can be commoditized in the future.

Yep, the model is running on Hopper architecture. Anything less was not sufficient in our experiments.

Thanks for trying it out! character.ai has put their model behind a waitlist, so it's hard to compare. As far as I can tell, they don't appear to make any specific claims about speed or interactivity in their press release.

thanks for trying us out!

glad to bring a little joy into the world :)