ZONOS2 Technical Report

2026-06-23

AI Summary

This work proposes a high-performance text-to-speech (TTS) system based on a sparse mixture-of-experts (MoE) architecture to address challenges in naturalness, prosody, and voice cloning fidelity. By constructing an 8-billion-parameter MoE model—of which only 900 million parameters are activated per inference—and training it on 6 million hours of speech data, the approach substantially increases model capacity while maintaining manageable computational costs through optimized conditioning and inference pipelines. The resulting system achieves state-of-the-art performance in naturalness, speaker similarity, word error rate, and on the newly introduced ZTTS1-Eval benchmark, all while supporting low-latency streaming inference. The implementation has been open-sourced under the Apache 2.0 license.

Abstract

We present ZONOS2 8B, our latest TTS model, which achieves state-of-the-art naturalness, prosody, and voice cloning fidelity. We improve upon Zonos-v0.1 across scale, data, and training recipe. We scale the model from 1.6B to 8B total parameters (900M active) with a novel mixture-of-experts (MoE) backbone, improving inference latency and throughput. We expand our training corpus from 200K to over 6M hours using a new data processing pipeline, and we simplify our post-training and conditioning recipes to improve naturalness and voice cloning fidelity. We evaluate ZONOS2 8B on quality, speaker similarity, WER, and ZTTS1-Eval, our novel TTS benchmark, where it performs competitively with state-of-the-art systems while maintaining good streaming latency. We release our model weights and example inference code under an Apache 2.0 license on GitHub and Hugging Face.

Problem

• text-to-speech
• naturalness
• prosody
• voice cloning
• speech synthesis

Innovation

• Mixture-of-Experts
• voice cloning
• large-scale TTS
• data processing pipeline
• ZTTS1-Eval