Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice
Abstract
Simultaneous Interpretation (SI) represents one of the most daunting frontiers in the translation industry, with product-level automatic systems long plagued by intractable challenges: subpar transcription and translation quality, lack of real-time speech generation, multi-speaker confusion, and translated speech inflation, especially in long-form discourses. In this study, we introduce Seed-LiveInterpret 2.0, an end-to-end SI model that delivers high-fidelity, ultra-low-latency speech-to-speech generation with voice cloning capabilities. As a fully operational product-level solution, Seed-LiveInterpret 2.0 tackles these challenges head-on through our novel duplex speech-to-speech understanding-generating framework. Experimental results demonstrate that through large-scale pretraining and reinforcement learning, the model achieves a significantly better balance between translation accuracy and latency, validated by human interpreters to exceed 70% correctness in complex scenarios. Notably, Seed-LiveInterpret 2.0 outperforms commercial SI solutions by significant margins in translation quality, while slashing the average latency of cloned speech from nearly 10 seconds to a near-real-time 3 seconds, which is around a near 70% reduction that drastically enhances practical usability.
Keywords
Cite
@article{arxiv.2507.17527,
title = {Seed LiveInterpret 2.0: End-to-end Simultaneous Speech-to-speech Translation with Your Voice},
author = {Shanbo Cheng and Yu Bao and Zhichao Huang and Yu Lu and Ningxin Peng and Lu Xu and Runsheng Yu and Rong Cao and Yujiao Du and Ting Han and Yuxiang Hu and Zeyang Li and Sitong Liu and Shengtao Ma and Shiguang Pan and Jiongchen Xiao and Nuo Xu and Meng Yang and Rong Ye and Yiming Yu and Jun Zhang and Ruofei Zhang and Wanyi Zhang and Wenhao Zhu and Liehao Zou and Lu Lu and Yuxuan Wang and Yonghui Wu},
journal= {arXiv preprint arXiv:2507.17527},
year = {2025}
}
Comments
Seed-LiveInterpret 2.0 Technical Report