Show HN: Kitten TTS – 25MB CPU-Only, Open-Source TTS Model

And a quick video with all of the different voices:

https://www.youtube.com/watch?v=60Dy3zKBGQg

The reddit video is awesome. I don't understand how people are calling it an OK model. Under 25MB and cpu only for this quality is amazing.

Sounds very clear. For a non native english speaker like me, it's easy to understand.

Sounds slow and like something from an anine

was it cross trained on futurama voices?

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

Dedicated single-purpose hardware with models would be even less energy-intensive. It's theoretically possible to design chips which run neural networks and alike using just resistors (rather than transistors).

Such hardware is not general-purpose, and upgrading the model would not be possible, but there's plenty of use-cases where this is reasonable.

This is what Apple is envisioning with their SLMs, like having a model specifically for managing calendar events. It doesn't need to have the full knowledge of all humanity in it - just what it needs to manage the calendar.

Hmm. A pay once (or not at all) model that can run on anything? Or a subscription model that locks you in, and requires hardware that only the richest megacorps can afford? I wonder which one will win out.

This is our goal too.

That is our vision too!

yeah totally. the quality of these tiny models are only going to go up.

Thanks for running the benchmarks. Currently the models are not optimized yet. We will optimize loading etc when we release an SDK meant for production :)

on my Intel(R) Celeron(R) N4020 CPU @ 1.10GHz it takes 6 seconds to import/load and text generation is roughly 1x realtime on various lengths of text.

Does anybody find it funny that sci-fi movies have to heavily distort "robot voices" to make them sound "convincingly robotic"? A robotic, explicitly non-natural voice would be perfectly acceptable, and even desirable, in many situations. I don't expect a smart toaster to talk like a BBC host; it'd be enough is the speech if easy to recognize.

I got an error when I tried the demo with 6 sentences, but it worked great when I reduced the text to 3 sentences. Is the length limit due to the model or just a limitation for the demo?

I tried to replicate their demo text but it doesn't sound as good for some reason.

If anyone else wants to try:

> Kitten TTS is an open-source series of tiny and expressive text-to-speech models for on-device applications. Our smallest model is less than 25 megabytes.

Doesn't work here. Backend module returns 404 :

https://clowerweb.github.io/node_modules/onnxruntime-web/dis...

> Error generating speech: failed to call OrtRun(). ERROR_CODE: 2, ERROR_MESSAGE: Non-zero status code returned while running Expand node. Name:'/bert/Expand' Status Message: invalid expand shape

Doesn't seem to work with thai.

Thanks, I was looking for that. While the reddit demo sounds ok, even though on a level we reached a couple of years ago, all TTS samples I tried were barley understandable at all

On PC it's a python dependency hell but someone managed to package it in self contained JS code that works offline once it loaded the model? How is that done?

It feels like it doesn't handle punctuation well. I don't hear sentence boundaries and commas. It sounds like continuous stream of words.

yeah, this is just a preview model from an early checkpoint. the full model release will be next week which includes a 15M model and an 80M model, both of which will have much higher quality than this preview.

Using male voice 2 at 48kHz at 0.5x speed sounds a lot like Madeline's voice lines in Celeste. Seemed funny to me.

This doesn’t seem to work on Safari. Works great on Chrome, though

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yeah, we are super excited to build tiny ai models that are super high quality. local voice interfaces are inevitable and we want to power those in the future. btw, this model is just a preview, and the full release next week will be of much higher quality, along w another ~80M model ;)

> It’s that KittenTTS is Apache-2.0

Have you seen the code[1] in the repo? It uses phonemizer[2] which is GPL-3.0 licensed. In its current state, it's effectively GPL licensed.

[1]: https://github.com/KittenML/KittenTTS/blob/main/kittentts/on...

[2]: https://github.com/bootphon/phonemizer

Edit: It looks like I replied to an LLM generated comment.

A Festival's English model, festvox-kallpc16k, is about 6 MB, and it is a large model; festvox-kallpc8k is about 3.5 MB.

eSpeak NG's data files take about 12 MB (multi-lingual).

I guess this one may generate more natural-sounding speech, but older or lower-end computers were capable of decent speech synthesis previously as well.

> KittenTTS is Apache-2.0

What about the training data? Is everyone 100% confident that models are not a derived work of the training inputs now, even if they can reproduce input exactly?

I play around with a nvidia jetson orin nano super right now and its actually pretty usuable with gemma3:4b and quite fast - even image processing is done in like 10-20 seconds but this is with GPU support. When something is not working and ollama is not using the GPU this calls take ages because the cpu is just bad.

Iam curious how fast this is with CPU only.

It depends on espeak-ng which is GPLv3

This opens up voice interfaces for medical devices, offline language learning tools, and accessibility gadgets for the visually impaired - all markets where cloud dependency and proprietary licenses were showstoppers.

But Pi Zero has a GPU, so why not make use of it?

The github just has a few KB of python that looks like an install script. How is this used from C++ ?

I opened a couple of PRs to fix this situation:

https://github.com/KittenML/KittenTTS/pull/21 https://github.com/KittenML/KittenTTS/pull/24 https://github.com/KittenML/KittenTTS/pull/25

If you have `uv` installed, you can try my merged ref that has all of these PRs (and #22, a fix for short generation being trimmed unnecessarily) with

    uvx --from git+https://github.com/akx/KittenTTS.git@pr-21-22-24-25 kittentts --output output.wav --text "This high quality TTS model works without a GPU"

Install it with uvx that should solve the python issues.

https://docs.astral.sh/uv/guides/tools/

uv installation:

https://docs.astral.sh/uv/getting-started/installation/

Yeah some people have a problem and think "I'll use Python". Now they have like fifty problems.

I had the too new.

This package is the epitome of dependency hell.

Seriously, stick with piper-tts.

Easy to install, 50MB gives you excellent results and 100MB gives you good results with hundreds of voices.

It doesn't work on Fedora because of the lack of g++ having the right version.

We are working to fix that. Thanks

Python man

You're getting a lot of comments along the lines of "Why don't you just ____," which only shows how Stockholmed the entire Python community is.

With no other language are you expected to maintain several entirely different versions of the language, each of which is a relatively large installation. Can you imagine if we all had five different llvms or gccs just to compile five different modern C projects?

I'm going to get downvoted to oblivion, but it doesn't change the reality that Python in 2025 is unnecessarily fragile.

There are still people who use machine wide python installs instead of environments? Python dependency hell was already bad years ago, but today it's completely impractical to do it this way. Even on raspberries.

Such an ignorant thing to say for something that requires 25MB RAM.

system python is for system applications that are known to work together. If you need a python install for something else, there's venv or conda and then pip install stuff.

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You're supposed to use venv for everything but the python scripts distributed with your os

That's a great point about the dependencies.

To make the setup easier and add a few features people are asking for here (like GPU support and long text handling), I built a self-hosted server for this model: https://github.com/devnen/Kitten-TTS-Server

The goal was a setup that "just works" using a standard Python virtual environment to avoid dependency conflicts.

The setup is just the standard git clone, pip install in a venv, and python server.py.

It mentions ONNX, so I imagine an ONNX model is or will be available.

ONNX runtime is a single library, with C#'s package being ~115MB compressed.

Not tiny, but usually only a few lines to actually run and only a single dependency.

Usually pulling in lots of libraries helps develop/iterate faster. Then can be removed later once the whole thing starts to take shape.

Nvidia's parakeet https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2 appears to be state of the art for english: 10x faster than Whisper.

My mid-range AMD CPU is multiple times faster than realtime with parakeet.

>Aside: Are there any models for understanding voice to text, fully offline, without training?

OpenAI's whisper is a few years old and pretty solid.

https://github.com/openai/whisper

Voice to text fully offline can be done with whisper. A few apps offer it for dictation or transcription.

"The brown fox jumps over the lazy dog.."

Average duration per generation: 1.28 seconds

Characters processed per second: 30.35

--

"Um"

Average duration per generation: 0.22 seconds

Characters processed per second: 9.23

--

"The brown fox jumps over the lazy dog.. The brown fox jumps over the lazy dog.."

Average duration per generation: 2.25 seconds

Characters processed per second: 35.04

--

processor : 0

vendor_id : AuthenticAMD

cpu family : 25

model : 80

model name : AMD Ryzen 7 5800H with Radeon Graphics

stepping : 0

microcode : 0xa50000c

cpu MHz : 1397.397

cache size : 512 KB

Any idea what factors play into latency in TTS models?

We have released just a preview of the model. We hope to get the model much better in the future releases.

Fish Speech says its weights are for non-commercial use.

Also, what are the two's VRAM requirents? This model has 15 million parameters which might run on low-power, sub-$100 computers with up-to-date software. Your hardware was an out-of-date 6GB GPU.

> Hmm the quality is not so impressive. [...] And I don't mind throwing GPU power at it.

This isn't for you, then. You should evaluate quality here based on the fact you don't need a GPU.

Back in the pre-Tacotron2 days, I was running slim TTS and vocoder models like GlowTTS and MelGAN on Digital Ocean droplets. No GPU to speak of. It cost next to nothing to run.

Since then, the trend has been to scale up. We need more models to scale down.

In the future we'll see small models living on-device. Embedded within toys and tools that don't need or want a network connection. Deployed with Raspberry Pi.

Edge AI will be huge for robotics, toys and consumer products, and gaming (ie. world models).

The best open one I've found so far is Dia - https://github.com/nari-labs/dia - it has some limitations, but i think it's really impressive and I can run it on my laptop.

You should give try to https://pinokio.co/

Imho chatterbox is the current open weight SOTA model in terms of quality: https://huggingface.co/ResembleAI/chatterbox

Chatterbox is also worth a try.

Try https://github.com/Picovoice/orca

Well, speech synthesizers are pretty much famous for speaking all sorts of things wrong. But what I find very concerning about LLM based TTS is that some of them cant really speak numbers greater then 100. They try, but fail a lot. At least tts-1-hd was pretty much doing this for almost every 3 or 4 digit number. Especially noticeable when it is supposed to read a year number.

They're often trained from video subtitles, and humans writing subtitles make that kind of mistake too.

You probably mean "e.g." as "for example", not "i.e."?

This might be on purpose and part of the training data because "for example" just sounds much better than "e.g.". Presumably for most purposes, linguistic naturalness is more important than fidelity.

thanks, we're going to release many more models in the future, that can run on just CPUs.

would love to see how that turns out. the full model release next week will be more expressive and higher quality than this one so we're excited to see you try that out.

They posted a demo on reddit[0]. It sounds amazing given the tiny size.

[0] https://old.reddit.com/r/LocalLLaMA/comments/1mhyzp7/kitten_...

We change our tone based on personal style, emotion, context, and other factors. An accurate generator might need to encode all that information in the model. It will be larger than a model that doesn't do all of that.

Probably "metallicity" is due to lack of details and cannot be fixed that easy.

Try Unmute by Kyutai - https://unmute.sh/

thanks, but keep in mind that this model is just a preview checkpoint that is only 10% trained. the full release next week will be of much higher quality and it will include a 15M model and an 80M model.

Whisper and the many variants. Here's a good implementation.

https://github.com/ggml-org/whisper.cpp

This one is a whisper-based Python package

https://github.com/primaprashant/hns

Does your Eloquence installation include multiple languages? The one I have is only 1876 KB for US English only. And classic DECtalk is even smaller; I have here a version that's only 638 KB (again, US English only).

If you're looking for other languages, Piper has been around in this scene for much longer and they have open-source training code and a lot of models (they're ~60MB instead of 25MB but whatever...) https://huggingface.co/rhasspy/piper-voices/tree/main

Actually I found it irritating that the readme does not mention the language at all. I think it is not good practice to deduce it from the language of the readme itself. I would not like to have German language tts models with only a German readme...

I tried on some Japanese for the kicks of it, it reads... "Chinese letter chinese letter japanese letter chinese letter..." :D

But yeah, if it's like any of the others we'll likely see a different "model" per language down the line based on the same techniques

TTS is generally not multilingual. One might think a well-annotated phonetic descriptions of voices would suffice, but that's not quite how languages work nor how TTS work.

(but somehow LLMs handle multilingual input perfectly fine! that's a bit strange, if you think about that)

Yes. The FAQ says that multilingual capabilities are in the works.

It's not the same feature, but at least that's not several orders of magnitude away from "run anywhere"

That’s not a far comparison. Say just calls the speech synthesis APIs that have been around since at least Mac OS 8.

That being said, the ‘classical’ (pre-AI) speech synthesisers are much smaller than kitten, so you’re not wrong per se, just for the wrong reason.

SAM on Commodore 64 was only 6K:

https://project64.c64.org/Software/SAM10.TXT

Obviously it's not fair to compare these with ML models.

And what dynamic libraries s it linked to? And what other data are they pulling in?

`say` sounds terrible compared to modern neural network based text to speech engines.

Tried that on 26 beta, and the default voice sounds a lot smoother than it used it.

Running `man say` reveals that "this tool uses the Speech Synthesis manager", so I'm guessing the Apple Intelligence stuff is kicking in.

If you make a shell script that calls say, that script will be even smaller!

Yes, XTTS2 has been reasonably performant for me and the cloning is acceptable.

I'm doing this now with Home Assistant voice. All the TTS, STT, and LLMs involved run locally on my network. It's absurdly superior to every other voice assistant product. (Would be nice if it was just a pure multi-modal model though)

On Mac OS you can "speak" a text in almost every app, using built in voice (like the Siri voice or some older voices). All offline, and even from the terminal with "say".

Can't most people read faster than they can hear? Isn't this why phone menus are so awful?

> But there seems to be no good way to output spoken text without doing round-trips to a server and generate the audio there

As people have been pointing out, we've had mediocre TTS since the 80s. If it was a real benefit people would be using even the inadequate version.

Love the idea, but the text it produces is way too flowery for my taste

"A new tool is stirring up excitement and debate in the programming community"

Just give me the facts without American style embellishments. You're not trying to sell me anything =)

It looks like it's Python, so it might be possible to use via https://github.com/livebook-dev/pythonx ? But the parallel huggingface/bumblebee idea was also good, hadn't seen or thought of, that definitely works for a lot of other models, curious if you get working! Some chance I'll play with this myself in a few months, so feel free to report back here or DM me!

It's not possible so far via Bumblebee, unfortunately[1].

[1] https://github.com/elixir-nx/bumblebee/issues/209

We will release an STT model as well.

no, but whisper has a 39M model: https://github.com/openai/whisper

thanks a lot, this model is just a preview checkpoint. the full release next week will be of much higher quality.

Yup it runs on the web browser. https://clowerweb.github.io/kitten-tts-web-demo/

Thanks