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Zyphra/ZONOS2-GGUF

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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ZONOS2 — GGUF

GGUF weights for **Zyphra/ZONOS2**, ready to run with **zonos2.cpp** — a standalone ggml/GGUF C++ port of ZONOS2. The entire pipeline (ECAPA speaker encoder → MoE backbone → DAC vocoder) runs as native C++ linking only libggml + gguf: no Python, no PyTorch, no CUDA-only kernels at inference time. CPU and CUDA use the same files.

ZONOS2 is Zyphra's latest text-to-speech model, trained on more than 6 million hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers at low latency with MoE. ZONOS2 excels at high-fidelity and naturalistic voice cloning.

For model details and speech samples, check out our [blog](https://www.zyphra.com/our-work/zonos2). A hosted version is available at [cloud.zyphra.com/audio-playground](https://cloud.zyphra.com/audio-playground).

Language support is as follows. | Tier | Languages | | ------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Tier 1 | English, Mandarin Chinese, Japanese | | Tier 2 | Korean, Russian, Italian, Portuguese, French, Spanish, Vietnamese, German, Hebrew, Dutch | | Tier 3 | Swedish, Hindi, Tamil, Telugu, Thai, Norwegian, Bengali, Tagalog, Arabic, Danish, Indonesian, Polish, Ukrainian, Romanian, Finnish, Hungarian, Lithuanian, Estonian, Slovak, Croatian, Latvian |

Files

File	Size	Description
`zonos2-f16.gguf`	15.3 GB	F16 backbone — lossless from the bf16 checkpoint
`zonos2-q8_0.gguf`	8.5 GB	Q8_0 MoE experts, F16 spine — recommended; effectively lossless
`zonos2-q6_k.gguf`	6.8 GB	Q6_K MoE experts, F16 spine
`zonos2-q5_k.gguf`	5.9 GB	Q5_K MoE experts, F16 spine
`zonos2-q4_k.gguf`	4.9 GB	Q4_K MoE experts, F16 spine — smallest
`dac.gguf`	254 MB	DAC-44 kHz decoder (codes → waveform)
`spk-encoder.gguf`	24 MB	ECAPA-TDNN speaker encoder (wav → x-vector, for voice cloning)

All quants keep the spine (attention + router + embeddings + speaker projection) at F16 and quantize only the MoE expert matrices — the layout that holds quality far below the usual quant floor. The full-precision spine is the single biggest quality lever; pick the expert precision (Q8 → Q4) that fits your VRAM.

Benchmarks

Build	Size	bpw	KLD ↓	Top-1 ↑	WER ↓	SpkSim ↑	UTMOS ↑
F16 (ref)	15.3 GB	16.0	—	—	2.79	66.75	4.40
Q8_0	8.5 GB	8.50	0.002	96.5%	2.87	66.30	4.40
Q6_K	6.8 GB	6.56	0.007	92.9%	3.07	66.12	4.40
Q5_K	5.8 GB	5.50	0.025	86.3%	2.98	66.30	4.40
Q4_K	4.9 GB	4.50	0.072	76.9%	3.00	64.54	4.36

—KLD (mean) and Top-1 measure how closely each quant tracks the F16 backbone's per-frame logits, scored with the zonos2-perplexity tool in zonos2.cpp.
—WER (Qwen3-ASR word-error rate), SpkSim (clone speaker similarity), and UTMOS (predicted MOS) are end-to-end on the `Zyphra/ZTT1-Eval` Clean English set; lower WER and higher SpkSim/UTMOS are better.

Although the logit metrics (KLD, Top-1) degrade steadily as the experts shrink, the audio quality holds nearly flat down to Q4_K — WER, speaker similarity, and UTMOS stay within eval noise of F16. The F16 spine keeps the model on-distribution, so the smaller quants spend their error budget on inaudible logit jitter rather than audible artifacts. Q8_0 is the recommended default (effectively lossless); Q4_K is a strong choice when VRAM is tight.

Quick Start

Build zonos2.cpp (CPU, or -DGGML_CUDA=ON for NVIDIA), then turn text into a waveform with one command:

bash

zonos2-cli zonos2-q8_0.gguf --tts "Hello, world." out.wav \
    --dac dac.gguf --gpu --seed 1

Add --spk voice.mp3 (with spk-encoder.gguf) to clone a voice from a reference clip.

HTTP server

zonos2-server mirrors the reference FastAPI — low-latency streaming PCM, an OpenAI /v1/audio/speech endpoint, in-process voice cloning, and a browser UI:

bash

zonos2-server zonos2-q8_0.gguf --dac dac.gguf --spk-encoder spk-encoder.gguf --gpu

See the zonos2.cpp README for build instructions, quantization (quantize-cli), batching, and the full CLI reference.

Citation

If you find this model useful in an academic context please cite as:

@misc{zyphra2025zonos,
  title     = {Zonos V2 Technical Report},
  author    = {Gabriel Clark, Sofian Mejjoute, Mohamed Osman, George Close, Beren Millidge},
  year      = {2026},
}