Model card: cite the release post (no technical report yet); ZONOS3 naming
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README.md
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ZONOS3-Mini runs entirely on your own GPU through WebGPU: no install and no server. The first visit downloads the model, and
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the browser keeps it cached for later visits. You need a browser with WebGPU support.
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- [Zonos Voice Studio](https://zyphra.com/
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- [Lite](https://zyphra.com/
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speak with it.
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### With SGLang
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```
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Add `"references": [{"audio_path": "<reference clip>"}]` to clone a voice, or `"stream": true, "response_format": "pcm"` to
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stream 44.1 kHz audio as it is generated. The [
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covers voice cloning, streaming and the sampling options.
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### Native runtimes
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acoustic codebooks), conditioned on the text, a speaker prompt and three bucketed acoustic controls. A causal multi-resolution
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iSTFT head renders the frames to 44.1 kHz audio as they are produced.
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 (benchmark version 2: FLEURS-R read speech in 35 languages and VoxBlink2 in-the-wild recordings in 20 languages) as
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½ z(similarity) − ½ z(WER) across the systems shown;
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time to first audio with 8 concurrent requests (80 ms for
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(457 seconds of audio per second for
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: the full studio.
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- [Lite](https://zyphra.com/zonos3-mini/localplayground/lite): quick voice cloning. Pick, upload or record a voice and
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speak with it.
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### With SGLang
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```
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Add `"references": [{"audio_path": "<reference clip>"}]` to clone a voice, or `"stream": true, "response_format": "pcm"` to
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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)
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covers voice cloning, streaming and the sampling options.
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### Native runtimes
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acoustic codebooks), conditioned on the text, a speaker prompt and three bucketed acoustic controls. A causal multi-resolution
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iSTFT head renders the frames to 44.1 kHz audio as they are produced.
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*Inference pipeline. Speaker conditioning (raw reference codes or a speaker-embedding row), control rows and SuperBPE text enter
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the hybrid GDN/attention MoE backbone, which predicts all ten codebooks of the next frame under the delay pattern (four drawn);
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as soon as a frame's last codebook is generated, the codec's causal iSTFT head renders that frame to audio.*
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| | ZONOS3-Mini |
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|---|---|
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| 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) |
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| 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 |
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Quality against speed on one H100 80GB. Quality combines macro speaker similarity and macro WER over the 55 language subsets of
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[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
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½ z(similarity) − ½ z(WER) across the systems shown; ZONOS3-Mini is measured with the speaker-embedding prompt. Left: median
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time to first audio with 8 concurrent requests (80 ms for ZONOS3-Mini). Right: peak throughput at each model's best concurrency
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(457 seconds of audio per second for ZONOS3-Mini).
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## Citation
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If you find this model useful in an academic context, please cite the release post:
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```
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@misc{zyphra2026zonos3mini,
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title = {{ZONOS3-Mini}},
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author = {Gabriel Clark and Sofian Mejjoute and George Close and Beren Millidge},
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year = {2026},
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month = nov,
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howpublished = {\url{https://www.zyphra.com/our-work/zonos3-mini}},
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}
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```
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