| --- |
| license: apache-2.0 |
| datasets: |
| - FunAudioLLM/CineDub-Example |
| language: |
| - zh |
| - en |
| - fr |
| - es |
| - ja |
| - ko |
| - it |
| - ru |
| - de |
| tags: |
| - Dubbing |
| --- |
| |
| <p align="center"> |
| <b>🎬 Fun-CineForge: A Unified Dataset Pipeline and Model for Zero-Shot Movie Dubbing<br> |
| in Diverse Cinematic Scenes</b> |
| </p> |
|
|
| <div align="center"> |
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|  |
| <a href=""><img src="https://img.shields.io/badge/OS-Linux-orange.svg"></a> |
| <a href=""><img src="https://img.shields.io/badge/Python->=3.8-aff.svg"></a> |
| <a href=""><img src="https://img.shields.io/badge/Pytorch->=2.1-blue"></a> |
| </div> |
|
|
| [Demos](https://funcineforge.github.io/); [Paper](https://arxiv.org/pdf/2601.14777); [Modelscope](https://www.modelscope.cn/models/FunAudioLLM/Fun-CineForge); [HuggingFace](https://huggingface.co/FunAudioLLM/Fun-CineForge) |
|
|
| **Fun-CineForge** contains an end-to-end dataset pipeline for producing large-scale dubbing datasets and an MLLM-based dubbing model designed for diverse cinematic scenes. Using this pipeline, we constructed the first large-scale Chinese television dubbing dataset CineDub-CN, which includes rich annotations and diverse scenes. In monologue, narration, dialogue, and multi-speaker scenes, our dubbing model consistently outperforms state-of-the-art methods in terms of audio quality, lip-sync, timbre transition, and instruction following. |
