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license: apache-2.0
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---
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license: apache-2.0
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language:
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- zh
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- en
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- fr
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- es
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- ja
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- ko
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- it
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- ru
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- de
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---
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<p align="center">
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<b>🎬 Fun-CineForge: A Unified Dataset Pipeline and Model for Zero-Shot Movie Dubbing<br>
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in Diverse Cinematic Scenes</b>
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</p>
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<div align="center">
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<a href=""><img src="https://img.shields.io/badge/OS-Linux-orange.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/Python->=3.8-aff.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/Pytorch->=2.1-blue"></a>
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</div>
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[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)
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**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.
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