MoonCast: High-Quality Zero-Shot Podcast Generation
Abstract
Recent advances in text-to-speech synthesis have achieved notable success in generating high-quality short utterances for individual speakers. However, these systems still face challenges when extending their capabilities to long, multi-speaker, and spontaneous dialogues, typical of real-world scenarios such as podcasts. These limitations arise from two primary challenges: 1) long speech: podcasts typically span several minutes, exceeding the upper limit of most existing work; 2) spontaneity: podcasts are marked by their spontaneous, oral nature, which sharply contrasts with formal, written contexts; existing works often fall short in capturing this spontaneity. In this paper, we propose MoonCast, a solution for high-quality zero-shot podcast generation, aiming to synthesize natural podcast-style speech from text-only sources (e.g., stories, technical reports, news in TXT, PDF, or Web URL formats) using the voices of unseen speakers. To generate long audio, we adopt a long-context language model-based audio modeling approach utilizing large-scale long-context speech data. To enhance spontaneity, we utilize a podcast generation module to generate scripts with spontaneous details, which have been empirically shown to be as crucial as the text-to-speech modeling itself. Experiments demonstrate that MoonCast outperforms baselines, with particularly notable improvements in spontaneity and coherence.
Keywords
Cite
@article{arxiv.2503.14345,
title = {MoonCast: High-Quality Zero-Shot Podcast Generation},
author = {Zeqian Ju and Dongchao Yang and Jianwei Yu and Kai Shen and Yichong Leng and Zhengtao Wang and Xu Tan and Xinyu Zhou and Tao Qin and Xiangyang Li},
journal= {arXiv preprint arXiv:2503.14345},
year = {2025}
}