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?
I couldn't locate how to run it on a GPU anywhere in the repo.
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'}
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.
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.
Also:
I want to be my own personal assistant...
EDIT: I can provide it a RTX 3080ti.
Qwen 3 TTS is good for voice cloning but requires GPU of some sort.
(That's using the example as-is. If you switch it to the smaller model, modify the above with +57 MiB of models from HuggingFace, or =727 MiB.)
So I toyed with this a bit + the Rust library "ort", and ort is only 224M in release (non-debug) mode, and it was pretty simple to run this model with it. (I did not know ort before just now.) I didn't replicate the preprocessing the Python does before running the model, though. (You have to turn the text into an array of floats, essentially; the library is doing text -> phonemes -> tokens; the latter step is straight-forward.)
The iOS version is Swift-based.
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.
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.
This is a mind numbing task that requires workers to make hundreds of calls each day with only minor variations, sometimes navigating phone trees, half the time leaving almost the exact same message.
Anyway, I believe almost all such businesses will be automated within months. Human labour just cannot compete on cost.
Tldr: generate human-like voice based on animal sound. Anyway maybe it doesn't make sense.