Kitten-TTS 2 runs a 1.7B speech model with 1.58-bit weights on a plain CPU

What this is about

KittenML has released Kitten-TTS 2, a text-to-speech (TTS) model: text goes in, spoken audio comes out. Code and weights are on GitHub and Hugging Face. According to the announcement on r/LocalLLaMA, it is the first TTS model with 1.58-bit weights, and it runs in real time on a CPU alone. That's the maker's claim. The sources contain no independent measurements.

The model has 1.7 billion parameters (roughly, the adjustable values learned during training). It is a "speech language model": it reads text and writes codec tokens (discrete audio building blocks, here called "S3"), which a vocoder turns into 24 kHz audio. The architecture is completely new compared with version 0.8.

Where the 1.58 bits come from

Every weight in the linear layers of the model body has one of just three values: −scale, 0 or +scale. This is called ternary. Three states correspond to about 1.58 bits of information (log2 of 3). One full-precision scale factor is stored for each group of 128 weights.

A bf16 weight normally takes 16 bits. The repository packs five ternary values into one byte, or 1.6 bits per weight. The file shrinks from 3.47 GB to 0.95 GB (3.6x). According to the Model Card, this is exact: the audio output stays bit-identical.

The token embedding table, with 324M parameters, is not ternary. In the default variant it stays at bf16.

Weight variants and decoder

In "emb4" the transformer body is still exact. Only the embedding is imprecise (relative L2 error 0.118, about 4.21 bits per weight). That's why "packed" remains the default.

The audio decoder works in two stages. The first stage comes as "default" (459 MB, best quality) or as a distilled one-step student, "student_w4", with 4-bit weights (39 MB). According to the card, this saves memory, not compute time.

Features

• Voice cloning: From 5–30 seconds of a single speaker, with no fine-tuning. The maker says expression and accent are preserved.
• Voices: 115 built-in voices. Eight of them (Bella, Jasper, Luna, Bruno, Rosie, Hugo, Kiki, Leo) carry over from version 0.8. You choose the language through the voice that carries the accent, e.g. "German", "SpanishAbuela" or "JapaneseTrainConductor".
• Languages: Named voices for 16 languages, including German, English, French, Japanese, Chinese and Turkish. For non-English text, set normalize=False. The text normalizer is built for English and would otherwise garble numbers and dates.
• Expression control (beta): A leading emotion tag such as [joyful], [sad], [angry] or [tender]; event tags such as <laugh>, <sigh> and <gasp>; and (((emphasis))) for individual words. Exactly 20 tags are recognized. Any others are simply read aloud. According to the card, the effect depends on the voice and the sentence.

Usage

from kittenml import KittenTTS
m = KittenTTS("KittenML/kitten-tts-2")
audio = m.generate("Good morning.", voice="German", normalize=False)

You need Python 3.10+ and PyTorch. No Hugging Face login is required. CPU execution is automatic, but the fastest route is a llama.cpp fork (kitten-tts-2-cpp) with GGUF files in the repo's cpp/ folder.

What you need to try it

The default variant needs a download of roughly 1 GB (about 947 MiB of loaded weights including the decoder, according to the card). You don't need a graphics card. Real time on CPU is promised, especially with the C++ fork. For the "full" variant, plan on a good 3.5 GB. Realistically, 8 GB of RAM or more should do, but that's an estimate. Mac and Linux on CPU should work, and so should Google Colab, though the sources give no exact details.

License

The Reddit announcement calls the model "open source". But the weights are under the "Stellon Labs Community License": research, non-commercial and limited commercial use are free. Commercial permission ends once you or your affiliates exceed USD 1 million in annual revenue or cumulative funding. Attribution requirements apply when you redistribute. The speaker model (pyannote) is MIT-licensed, and the Python package is Apache 2.0. By the usual definition, that makes it "open weights" rather than free open-source software.

Context

Until now, ternary models have mostly been a language-model topic. A 1.7B speech language model that fits in under 1 GB and is meant to run on a CPU is attractive for local use, such as self-hosting without a GPU. The maker pitches it as an open-source alternative to ElevenLabs. The sources contain no quality comparisons, benchmarks or latency figures, so the "SOTA" label is unsupported. Caveats: emotion control is in beta, text normalization is only good for English, and voice cloning raises misuse questions that the sources don't address. You should also check the license threshold for any commercial project.