Just made a TTS tool based on Kitten TTS, fully browser based, no Python server backend: https://quickeditvideo.com/tts/
A tts model of this size should be industry standard!
The people calling it "OK" probably tried it for themselves. Whatever model is being demoed in that video is not the same as the 25MB model they released.
It doesn't sound so good. Excellent technical achievement and it may just improve more and more! But for now I can't use it for consumer facing applications.
Speech speed is always a tunable parameter and not something intrinsic to the model.
The comparison to make is expressiveness and correct intonation for long sentences vs something like espeak. It actually sounds amazing for the size. The closest thing is probably KokoroTTS at 82M params and ~300MB.
The voices sound artificial and a bit grating. The male voices especially are lacking, especially in depth: only the ultimate voice has any depth at all, while the others sound like teenagers who haven't finished puberty. None of the voices sound quite human, but they're all very annoying, and part of that is that they sound like they're acting.
The only real questions are which Chinese gacha game they ripped data from and whether they used Claude Code or Gemini CLI for Python code. I bet one can get a formant match from output this much overfit to whatever data. This isn't going to stay up for long.
Impressive technical achievement, but in terms of whether I'd use it: oof, that male voice is like one of these fake-excited newsreaders. Like they're always at the edge of their breath. The female one is better but still someone reading out an advertisement for a product they were told they must act extra excited for. I assume this is what the majority of training data was like and not an intentional setting for the demo. Unsure whether I could get used to that
I use TTS on my phone regularly and recently also tried this new project on F-Droid called SherpaTTS, which grabs some models from Huggingface. They're super heavy (the phone suspends other apps to disk while this runs) and sound good, but in the first news article there were already one or two mispronunciations because it's guessing how to say uncommon or new words and it's not based on logical rules anymore to turn text into speech
Google and Samsung have each a TTS engine pre-installed on my device and those sound and work fine. A tad monotonous but it seems to always pronounce things the same way so you can always work out what the text said
Espeak (or -ng) is the absolute worst, but after 30 seconds of listening closely you get used to it and can understand everything fine. I don't know if it's the best open source option (probably there are others that I should be trying) but it's at least the most reliable where you'll always get what is happening and you can install it on any device without licensing issues
anyone else wants to try sherpaOnnx you can try this.. https://github.com/willwade/tts-wrapper we recently added in the kokoro models which should sound a lot better. There are a LOT of models to choose from. I have a feeling the Droid app isnt handling cold starts very well.
Somebody should create a AI interviewer for VC funding. VCs are swamped with so many funding requests. All the founders should first convince AI why they need funding.
And it’s not even because you don’t want to. It’s just because that’s how things work. I spent years talking directly to users and then I started working for a multinational, and I haven’t seen a user in 7 years…
I would argue that Google is even better place for advertising. All they need to do is enable advertising in Gemini. There is a whole ecosystem already in place for Google advertising.
Not that someone wouldn't try, but wtf would advertisements even be in ai chats but a giant distraction and interruption? (Moreso than web or video now). They are usually a call to link to something else.
I think it will be really sneaky. You ask for information and LLM will say "<product> does what you want and it is also cheaper with these additional features."
Yeah its like a team where the task is switched between developers. In the end everybody provides different point of view to the problem and the whole team learns about the codebase.
