by mnk47 on 11/3/24, 11:30 PM with 56 comments
by reissbaker on 11/4/24, 7:10 AM
I suppose someone could hack their way around the problem by finetuning it to essentially replay Piper (or whatever) output, only with more natural prosody and intonation. And then have the text LLM pipe to Piper, and Piper pipe to Hertz-dev. But it would be pretty useful to have it accept text natively!
by blixt on 11/4/24, 7:24 AM
by wwwlouishinofun on 11/4/24, 4:26 AM
Additionally, I’ve been exploring an idea about voice interaction systems. Currently, most voice interactions are processed by converting voice input into text, generating a text-based response, and then turning this text back into audio. But what if we could train the system to respond directly in voice, without involving text at all? If developed to maturity, this model could produce responses that feel more natural and spontaneous, possibly diverging from traditional text-to-speech outputs. Natural speech has unique syntax and rhythm, not to mention dialect and tone variations, which could make a purely voice-trained system fascinating and more human-like.
Could you let me know if your current voice interaction model follows the standard speech-to-text-to-speech process, or if there is exploration in voice-to-voice processing?
by BrandiATMuhkuh on 11/4/24, 3:35 AM
I might be a bit biased (did my PhD exploring how VUI can persuade humans), but I think VUI is "the future" of computer interaction. If it's not the future, than at least it adds a new group of people (kids + elderly people) as potential users.
by jcims on 11/4/24, 12:16 PM
by wg0 on 11/4/24, 12:55 AM
by m11a on 11/4/24, 8:23 PM
Is this idea (‘collapse of their generation distributions’) a researched topic? If so, under what name?
Sounds interesting and maybe related to the whole continual learning / how to finetune properly line of work
by nitizaz on 11/14/24, 11:41 AM
by codedokode on 11/4/24, 9:56 AM
by mazoza on 11/4/24, 6:13 PM
hertz-vae: a 1.8 billion parameter transformer decoder which acts as a learned prior for the audio VAE. The model uses a context of 8192 sampled latent representations (17 minutes) and predicts the next encoded audio frame as a mixture of gaussians. 15 bits of quantized information from the next token act as semantic scaffolding to steer the generation in a streamable manner.
by mnk47 on 11/3/24, 11:30 PM
by zachthewf on 11/4/24, 5:35 PM
Even the large open source TTS models (see F5 TTS, Mask GCT) are mostly trained on very small audio datasets (say 100k hours) relative to the amount of audio available on the internet, so it's cool to see an open source effort to scale up training significantly.
by briansm on 11/4/24, 9:39 AM
by lordofgibbons on 11/4/24, 2:04 AM
by xarope on 11/4/24, 7:43 AM
And is the interactive generation just doing an ELIZA? i.e. "P: tell us about how AI will be interesting", "A: Yeah AI will, yeah, be interesting".
by kunley on 11/4/24, 2:54 PM
by Jayakumark on 11/4/24, 1:21 PM
by nitizaz on 11/7/24, 11:02 AM
by awinter-py on 11/4/24, 6:08 AM
by Dawny33 on 11/4/24, 5:59 AM
Does Hertz support multi-lingual audio right now?
by timnetworks on 11/6/24, 4:12 AM
by ryukoposting on 11/4/24, 1:19 PM
With SD and LLMs, there's a lot you can do to debug it by studying the way it responds to small changes in the prompt. But, since Hertz-dev is using sound as its input, it would be hard to discern which token you should tweak. Of course, if it's meant to be used in real time, that kind of fiddling isn't an option at all. How would you go about systematically studying Hertz-dev's behavior?
by blixt on 11/4/24, 7:16 AM