tiny-voca: A 4.5M-parameter text-to-speech model that runs entirely in your browser

What's going on

Over on Reddit's r/LocalLLaMA, a developer posting as "nato_nob" (n4ze3m on GitHub and Hugging Face) has shown off a very small speech model called tiny-voca. It's a text-to-speech (TTS) model, meaning an AI that turns written text into spoken audio. What makes it stand out is its size. At 4,537,291 parameters, tiny-voca is minuscule. Large language models have thousands of times more. Even so, it's supposed to produce intelligible English in two voices.

You can try it right now in your browser at tiny-voca.n4ze3m.com. The model runs locally on your device, and the project page says nothing you type ever leaves your machine. Everything else is public too: the code on GitHub, the weights on Hugging Face, the training dataset at huggingface.co/datasets/n4ze3m/tiny-voca and the custom phonemizer at github.com/n4ze3m/dizione. Code and weights are released under Apache-2.0, so commercial use is allowed.

The specs

The two size figures don't quite match. That's probably down to different file formats, or one number being in MB and the other in MiB. The developer hasn't cleared it up. Either way, the model is smaller than a lot of smartphone photos.

"70x real time" means ten seconds of speech is ready in about a seventh of a second. Four cores of an ordinary CPU are enough for that, no GPU required. When you try the browser demo, it shows you how long generation took, how fast it was and how long the conversion to phonetic notation took.

Why it's so fast

Most modern TTS systems work in two stages. First, one model computes a kind of acoustic picture of the speech. Then a second neural network, the vocoder, turns that into the actual audio. Vocoders are often pretty compute-hungry.

tiny-voca deliberately skips that step. Instead it uses a small source-filter synthesizer. The idea mirrors the human voice: a source produces a base tone, like your vocal cords, and a filter shapes that tone into vowels and consonants, like your mouth and throat. It's an old, well-understood speech synthesis technique, and it needs far less compute than a neural vocoder. According to the developer, that's exactly where the speed comes from. The trade-off: the voice can sound a bit artificial.

On top of that, there's a custom phonemizer, a program that converts written text into phonetic notation. That's tricky in English, where spelling and pronunciation often have little to do with each other. Think "though", "through" and "tough". The phonemizer, called "dizione", is a separate project, and the demo displays the phonemes it produces.

Trained on voices from another AI

tiny-voca was trained from scratch, without starting from an existing model. Training took 7.7 hours on a single RTX 4090, a GPU aimed at gamers and hobbyists. No data center required.

The 17.6 hours of training audio didn't come from real speakers. The developer generated it with Qwen3-TTS, a much larger speech model from Alibaba. That's what's known as synthetic training data. It's convenient: you get consistent voices without a recording studio and without legal questions around voice recordings. But there's a clear ceiling, and the developer names it himself: tiny-voca can never sound better than its teacher, Qwen3-TTS. This is essentially a form of distillation, where a small model learns to imitate a big one.

What doesn't work well yet

The developer is upfront about the weaknesses:

• English only, and only the two bundled voices. It can't read other languages, like German, in any usable way.
• No voice cloning, so it can't imitate someone else's voice from a short recording.
• Foreign names and unusual spellings are often mispronounced.
• The sound can come across as somewhat synthetic.
• Because the training data is synthetic, quality tops out at Qwen3-TTS level.

He explicitly asks people to test it with tricky text: numbers, dates, names and long sentences. If you find a failure, he wants you to post the text and the voice you used. There are no independent benchmarks or listening tests yet. All performance figures come from the developer.

What you need to try it

Practically nothing. The easiest route is the browser demo: open the page, wait a moment for the model to load (about 20 MB), type some English text and hit play. It works on any recent laptop, including Macs, and probably on many smartphones.

If you want to run it locally inside your own software, use PyTorch or ONNX. You don't need a GPU. According to the developer, 4 CPU threads are enough for 70x real time. RAM and disk space barely matter, a few hundred megabytes for the Python environment and model will do. That makes tiny-voca a candidate for weak hardware like a Raspberry Pi or older computers. Nobody has publicly tested that yet, though.

The bigger picture

tiny-voca isn't trying to be the best TTS out there. It shows how little you actually need. Well-known small open TTS models like Kokoro (around 82 million parameters) or Piper are already lean. tiny-voca is significantly smaller still, and fully reproducible, right down to the dataset. That's rare: many "open" models only publish weights, not the training data or recipe.

That makes the project valuable for tinkerers, educators and anyone who wants to understand how speech synthesis works. You can retrain it yourself on a single gaming GPU in a working day. A model that runs entirely in the browser is also interesting for apps that need to work offline or where privacy matters.

The limits are obvious, though. English only, two voices, an audibly artificial sound and trouble with names make tiny-voca more of a tech demo than an alternative to commercial voices for now. It's also unclear whether Qwen3-TTS's terms of use restrict how training data generated with it can be reused. The source doesn't say.