I'm still looking for the "perfect" setup in order to clone my voice and use it locally to send voice replies in telegram via openclaw. Does anyone have auch a setup?
What I love about OpenClaw is that I was able to send it a message on Discord with just this github URL and it started sending me voice messages using it within a few minutes. It also gave me a bunch of different benchmarks and sample audio.
I'm impressed with the quality given the size. I don't love the voices, but it's not bad. Running on an intel 9700 CPU, it's about 1.5x realtime using the 80M model. It wasn't any faster running on a 3080 GPU though.
yeah we'll add some more professional-sounding voices and also support for diy custom voices. we tried to add more anime/cartoon-ish voices to showcase the expressivity.
Regarding running on the 3080 gpu, can you share more details on github issues, discord or email? it should be blazing fast on that. i'll add an example to run the model on gpu too.
A lot of good small TTS models in recent times. Most seem to struggle hard on prosody though.
Kokoro TTS for example has a very good Norwegian voice but the rhythm and emphasizing is often so out of whack the generated speech is almost incomprehensible.
Haven't had time to check this model out yet, how does it fare here? What's needed to improve the models in this area now that the voice part is more or less solved?
small models struggle with prosody due to limited capacity. this version does much better than the precious one and is the best among other <25MB models. Kokoro is a really good model for its size, its competitive on artificial analysis too.
i think by the next release we should have something kokoro quality but a fifth of the size. Adding control for rhythm seems to be quite important too, and we should start looking at that for other languages.
yes. the current release of our model is english-only. so other languages are not expected to perform well. we'll try to look out for this in our multilingual release.
25MB is impressive. What's the tradeoff vs the 80M model — is it mainly voice quality or does it also affect pronunciation accuracy on less common words?
80M model is the highest quality while also being quite efficient. it is superior in terms of pronunciation accuracy for less common words, and also is more stable in terms of speed. its my fav model. i think the 40M is quite similar to 80M for most usecases. 15M is for resource cpus, loading onto a browser etc.
The new 15M is way better than the previous 80M model(v0.1). So we're able to predictably improve the quality which is very encouraging.
I think I tried on my Android everything I could try and 1. outside webpage reading, not many options; 2. as browser extensions, also not many (I don't like to copy URLs in your app) 3. they all insist reading every little shit, not only buttons but also "wave arrow pointing directly right" which some people use in their texts. So basically reading text aloud is a bunch of shitty options. Anyone jumping in this market opening?
Emotion based tagging control would be the most helpful narrowing it down. Tags like [sarcastically] [happily] [joyfully] [fearfully]: so a subsection of adverbs.
A stretch goal is 'arbitrary tags' from [singing] [sung to the tune of {x}] [pausing for emphasis] [slowly decreasing speed for emphasis] [emphasizing the object of this sentence] [clapping] [car crash in the distance] [laser's pew pew].
But yeah: instruction/control via [tags] is the deciding feature for me, provided prompt adherence is strong enough.
Also: a thought...
Everyone is using [] for different kinds of tags in this space: which is very simple. Maybe it makes sense to differentiate kinds of tags? I.E. [tags for modifying how text is spoken] vs {tags for creating sounds not specifically speech: not modifying anything... but instead it's own 'sound/word'}
There's a number of recent, good quality, small TTS models.
If the author doesn't describe some detail about the data, training, or a novel architecture, etc, I only assume they just took another one, do a little finetuning, and repackage as a new product.
shouldn't be hard. what backend/hardware are you interested in running this with? i'll add an example for using C++ onnx model. btw check out roadmap, our inference engine will be out 1-2 weeks and it is expected to be faster than onnx.
Is there any way to do a custom voice as a DIY? Or we need to go through you? If so, would you consider making a pricing page for purchasing a license/alternative voice? All but one of the voices are unusable in a business context.
thanks a lot for the feedback. yes, we're working on a diy way to add custom voices and will also be releasing a model with more professional voices in the next 2-3 weeks. as of now, we're providing commercial support for custom voices, languages and deployment through the support form on our github. can you share more about your business use-case? if possible, i'd like to ensure the next release can serve that.
yes, we're releasing an official mobile sdk and inference engine very soon. if you want to use something until then, some folks from the oss community have built ways to run kitten on ios. if you search kittentts ios on github you should find a few.
if you cant find it, feel free to ping me and i can help you set it up. thanks a lot for your support and feedback!
I'm thinking of giving "voice" to my virtual pets (think Pokemon but less than a dozen). The pets are made up animals but based on real animal, like Mouseier from Mouse (something like that). Is this possible?
Tldr: generate human-like voice based on animal sound. Anyway maybe it doesn't make sense.
as of now its english only. the training for multilingual model is underway and should be out in April! what languages are you most interested in? Right now, we are providing deployments for custom languages + voices through support form on the github.
I want to be my own personal assistant...
I'm impressed with the quality given the size. I don't love the voices, but it's not bad. Running on an intel 9700 CPU, it's about 1.5x realtime using the 80M model. It wasn't any faster running on a 3080 GPU though.
Regarding running on the 3080 gpu, can you share more details on github issues, discord or email? it should be blazing fast on that. i'll add an example to run the model on gpu too.
Kokoro TTS for example has a very good Norwegian voice but the rhythm and emphasizing is often so out of whack the generated speech is almost incomprehensible.
Haven't had time to check this model out yet, how does it fare here? What's needed to improve the models in this area now that the voice part is more or less solved?
The new 15M is way better than the previous 80M model(v0.1). So we're able to predictably improve the quality which is very encouraging.
Either in the form of the api via pitch/speed/volume controls, for more deterministic controls.
Or in expressive tags such as [coughs], [urgently], or [laughs in melodic ascending and descending arpeggiated gibberish babbles].
the 25MB model is amazingly good for being 25MB. How does it handle expressive tags?
A stretch goal is 'arbitrary tags' from [singing] [sung to the tune of {x}] [pausing for emphasis] [slowly decreasing speed for emphasis] [emphasizing the object of this sentence] [clapping] [car crash in the distance] [laser's pew pew].
But yeah: instruction/control via [tags] is the deciding feature for me, provided prompt adherence is strong enough.
Also: a thought...
Everyone is using [] for different kinds of tags in this space: which is very simple. Maybe it makes sense to differentiate kinds of tags? I.E. [tags for modifying how text is spoken] vs {tags for creating sounds not specifically speech: not modifying anything... but instead it's own 'sound/word'}
If the author doesn't describe some detail about the data, training, or a novel architecture, etc, I only assume they just took another one, do a little finetuning, and repackage as a new product.
The iOS version is Swift-based.
Is there any way to do a custom voice as a DIY? Or we need to go through you? If so, would you consider making a pricing page for purchasing a license/alternative voice? All but one of the voices are unusable in a business context.
Is there any way to get those running on iPhone ? I would love to have the ability for it to read articles to me like a podcast.
Tldr: generate human-like voice based on animal sound. Anyway maybe it doesn't make sense.