ZONOS2-GGUF

ZONOS2 — GGUF

<p align="center"> <img src="https://huggingface.co/Zyphra/ZONOS2/resolve/main/assets/ZONOS2BlogThumbnail.png" alt="ZONOS2 title card" width="750" /> </p> <div align="center"> <a href="https://discord.gg/gTW9JwST8q" target="_blank"> <img src="https://img.shields.io/badge/Join%20Our%20Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"> </a> </div>

---

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. A hosted version is available at cloud.zyphra.com/audio-playground.

Language support is as follows.

---

Files

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

• 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:

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:

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},
}