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MOSS-TTSD is a spoken dialogue generation model designed for expressive multi-speaker synthesis. It features long-context modeling, flexible speaker control, and multilingual support, while enabling zero-shot voice cloning from short audio references.
| Date | Stars |
|---|---|
| 2026-07-31 | 1371 |
| 2026-08-06 | 1375 |
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MOSS-TTSD: Text to Spoken Dialogue Generation
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<img src="asset/OpenMOSS_Logo.svg" alt="OpenMOSS Logo" width="300">
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<a href="https://mosi.cn/models/moss-ttsd"><img src="https://img.shields.io/badge/Blog-Read%20More-green" alt="blog"></a>
<a href="https://arxiv.org/abs/2603.19739"><img src="https://img.shields.io/badge/Paper-2603.19739%20-red" alt="paper"></a>
<a href="https://huggingface.co/OpenMOSS-Team/MOSS-TTSD-v1.0"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20MOSS%20TTSD%20-v1.0-yellow" alt="MOSS-TTSD-v1.0"></a>
<a href="https://huggingface.co/spaces/OpenMOSS-Team/MOSS-TTSD"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Huggingface%20%20-space-orange" alt="MOSS-TTSD-space"></a>
<a href=""><img src="https://img.shields.io/badge/AI Stuidio-Coming%20Soon-blue" alt="AI Studio"></a>
<a href="https://github.com/"><img src="https://img.shields.io/badge/Python-3.10+-orange" alt="version"></a>
<a href="https://github.com/OpenMOSS/MOSS-TTSD"><img src="https://img.shields.io/badge/PyTorch-2.0+-brightgreen" alt="python"></a>
<a href="https://github.com/OpenMOSS/MOSS-TTSD"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="mit"></a>
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# MOSS-TTSD🪐
[English](README.md) | [简体中文](README_zh.md)
<!-- **MOSS-TTSD** is a long-form spoken dialogue generation model that enables highly expressive multi-party conversational speech synthesis across multiple languages. It supports continuous long-duration generation, flexible multi-speaker dialogue control, and state-of-the-art zero-shot voice cloning with only short reference audio. MOSS-TTSD is designed for real-world long-form content creation, including podcasts, audiobook, sports and esports commentary, dubbing, crosstalk, and entertainment scenarios. （about）-->
## Overview
<p align="center">
<img src="asset/ttsd.png" alt="alt text" width="330">
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MOSS-TTSD is the long-form dialogue specialist within our open-source [MOSS‑TTS Family](https://github.com/OpenMOSS/MOSS-TTS). While foundational models typically prioritize high-fidelity single-speaker synthesis, MOSS-TTSD is architected to bridge the gap between isolated audio samples and cohesive, continuous human interaction.
The model represents a paradigm shift from "text-to-speech" to "script-to-conversation." By prioritizing the flow and emotional nuances of multi-party engagement, MOSS-TTSD transforms static dialogue scripts into dynamic, expressive oral performances. It is designed to serve as a robust backbone for creators and developers who require a seamless transition between distinct speaker personas without sacrificing narrative continuity.
Whether it is capturing the spontaneous energy of a live talk show or the structured complexity of a multilingual drama, MOSS-TTSD provides the stability and expressive depth necessary for professional-grade, long-form content creation in an open-source framework.
## Highlights
- **From Monologue to Dialogue**: Unlike traditional TTS which optimizes for reading, MOSS-TTSD masters the rhythm of conversation. It supports 1 to 5 speakers with flexible control, handling natural turn-taking, overlapping speech patterns, and distinct persona maintenance.
- **Extreme Long-Context Modeling**: moving beyond short-sentence generation, the model is architected for stability over long durations, supporting up to 60 minutes of coherent audio in a single session with consistent identity.
- **Diverse Scenario Adaptation**: fine-tuned for high-variability scenarios including conversational media (AI Podcasts), dynamic commentary (Sports/Esports), and entertainment (Audiobooks, Dubbing, and Crosstalk).
- **Multilingual & Zero-Shot Capabilities**: features state-of-the-art zero-shot voice cloning requiring only short reference audio, with robust cross-lingual performance across major languages including Chinese, English, Japanese, and EurExcerpt of 26,392 characters
Read on GitHub
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Qinyuan Cheng · Fudan University&Sun Yat-Sen University · China
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Yitian Gong · Fudan University · China
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singularity
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:60e57cbb492551db, topic:large-language-models