---
license: apache-2.0
pipeline_tag: text-to-speech
library_name: zonos2
tags:
- text-to-speech
- voice-cloning
- zonos3
- zyphra
---
# ZONOS3-Mini
---
ZONOS3-Mini is Zyphra’s compact, open-source text-to-speech model, delivering natural reliable speech and high-fidelity voice
cloning with just 100 million active parameters. It runs in real time on phones and laptops, including directly in the browser.
Clone a voice from an audio sample, or design a character by shaping age, accent and vocal texture. Steer emotion, pace and
delivery down to individual words for games, narration and voice assistants.
Run ZONOS3-Mini locally with our browser and native runtimes, or access it through [Zyphra Cloud](https://cloud.zyphra.com/models/zyphra/ZONOS3-Mini). Model weights are released
under Apache 2.0.
**Model details, samples and evaluations are in the [blog post](https://www.zyphra.com/our-work/zonos3-mini).**
**A hosted version is available at [Zyphra Cloud](https://cloud.zyphra.com/models/zyphra/ZONOS3-Mini).**
## Quick start
### In your browser
ZONOS3-Mini runs entirely on your own GPU through WebGPU: no install and no server. The first visit downloads the model, and
the browser keeps it cached for later visits. You need a browser with WebGPU support.
- [Zonos Voice Studio](https://zyphra.com/zonos3-mini/localplayground#cast): the full studio.
- [Lite](https://zyphra.com/zonos3-mini/localplayground/lite): quick voice cloning. Pick, upload or record a voice and
speak with it.
### With SGLang
[SGLang-Omni](https://github.com/sgl-project/sglang-omni) serves ZONOS3-Mini on NVIDIA GPUs behind the OpenAI-compatible
`/v1/audio/speech` endpoint, with streaming and voice cloning from a reference clip (no transcript needed). Install SGLang-Omni
by following its [installation guide](https://github.com/sgl-project/sglang-omni/blob/main/docs/get_started/installation.md),
then start a server:
```bash
sgl-omni serve --model-path Zyphra/ZONOS3-Mini --port 8000
```
Once `curl http://localhost:8000/health` reports `"healthy"`, generate speech with the default voice from another terminal:
```bash
curl -X POST http://localhost:8000/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{"input": "Hello from ZONOS3-Mini!"}' \
--output output.wav
```
Add `"references": [{"audio_path": ""}]` to clone a voice, or `"stream": true, "response_format": "pcm"` to
stream 44.1 kHz audio as it is generated. The [ZONOS3 cookbook](https://github.com/sgl-project/sglang-omni/blob/main/docs/cookbook/zonos3.md)
covers voice cloning, streaming and the sampling options.
### Native runtimes
The [ZONOS3-Mini GitHub repository](https://github.com/Zyphra/ZONOS3-Mini) holds Zyphra's own runtimes, which share one HTTP
API and serve the Zonos Voice Studio: a CUDA server for NVIDIA GPUs, an MLX server for Apple Silicon, a C++/ggml server and CLI
(CPU, CUDA, Metal or Vulkan), and the in-browser WebGPU build.
They load the GGUF files in [`gguf/`](https://huggingface.co/Zyphra/ZONOS3-Mini/tree/main/gguf) and the WebGPU shards in
[`web/`](https://huggingface.co/Zyphra/ZONOS3-Mini/tree/main/web); the 27 default voices, grouped by language, are in
[`gguf/voices/`](https://huggingface.co/Zyphra/ZONOS3-Mini/blob/main/gguf/voices/README.md).
## Architecture (rough outline)
Text is NeMo-normalized (English, Mandarin, Japanese, Korean, German, Spanish, French, Italian, Portuguese; other languages
pass through as written) and encoded with an 8,192-entry SuperBPE tokenizer. The language model autoregressively generates 15 Hz
dual-codec frames (10 codebooks of 4,096 entries each: one semantic codebook distilled from the Wav2Vec2-BERT teacher plus nine
acoustic codebooks), conditioned on the text, a speaker prompt and three bucketed acoustic controls. A causal multi-resolution
iSTFT head renders the frames to 44.1 kHz audio as they are produced.
*Inference pipeline. Speaker conditioning (raw reference codes or a speaker-embedding row), control rows and SuperBPE text enter
the hybrid GDN/attention MoE backbone, which predicts all ten codebooks of the next frame under the delay pattern (four drawn);
as soon as a frame's last codebook is generated, the codec's causal iSTFT head renders that frame to audio.*
| | ZONOS3-Mini |
|---|---|
| Backbone | 11 layers, model dim 1,024; 800M parameters total, about 100M active per token (latent MoE: 64 experts, top-2 routing, from layer 1) |
| Sequence mixer | pattern `gdn3_global1`: 9 Gated DeltaNet (GDN) layers + 2 global attention layers (16 heads x 32 dims, 4 KV heads), attention residuals (`full_attnres`); context 32,768 positions |
| Text | SuperBPE, 8,192 entries (+4 specials), NeMo text normalization at inference |
| Audio tokens | 15 Hz, 44.1 kHz dual-encoding codec, 10 x 4,096 codebooks (1 semantic + 9 acoustic); hop 2,940 samples; up to 10 minutes of audio per generation |
| Speaker prompt | raw reference clip (encoded by the codec into a token prefix, up to 60 s, with speaker CFG) **or** a single 192-d MECT-B2 speaker embedding (`ownmect_s2`) |
| Controls | loudness (LUFS, 10 buckets), bandwidth f_max (Hz, 10 buckets), **speaking rate (UTF-8 bytes per second, 30 buckets)**; each row has an "unknown" index; bucket edges in `params.json` (`condition_edges`) |
| Decoder | causal multi-resolution iSTFT head: `full_mr` (default) or `keep2_plain` (pruned, about 3x faster decode) |
| Precision | bf16 weights (`model.pth`); decoder heads run in fp32 |
*ZAC 1 (Zyphra Audio Codec 1).*
## Evaluations
Full results and the ZTTS2 capability index are in the [blog post](https://www.zyphra.com/our-work/zonos3-mini#evaluation).
Quality against speed on one H100 80GB. Quality combines macro speaker similarity and macro WER over the 55 language subsets of
[ZTTS2-Eval](https://zyphra.github.io/ZTTS2-Eval/) (benchmark version 2: FLEURS-R read speech in 35 languages and VoxBlink2 in-the-wild recordings in 20 languages) as
½ z(similarity) − ½ z(WER) across the systems shown; ZONOS3-Mini is measured with the speaker-embedding prompt. Left: median
time to first audio with 8 concurrent requests (80 ms for ZONOS3-Mini). Right: peak throughput at each model's best concurrency
(457 seconds of audio per second for ZONOS3-Mini).
## Citation
If you find this model useful in an academic context, please cite the release post:
```
@misc{zyphra2026zonos3mini,
title = {{ZONOS3-Mini Technical Report}},
author = {Gabriel Clark and Sofian Mejjoute and George Close and Beren Millidge},
year = {2026},
month = nov,
howpublished = {\url{https://www.zyphra.com/our-work/zonos3-mini}},
}
```