Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation
<|reference_start|>Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation: Recent advancements in speech generation models have been significantly driven by the use of large-scale training data. However, producing highly spontaneous, human-like speech remains a challenge due to the scarcity of large, diverse, and spontaneous speech datasets. In response, we introduce Emilia, the first large-scale, multilingual, and diverse speech generation dataset. Emilia starts with over 101k hours of speech across six languages, covering a wide range of speaking styles to enable more natural and spontaneous speech generation. To facilitate the scale-up of Emilia, we also present Emilia-Pipe, the first open-source preprocessing pipeline designed to efficiently transform raw, in-the-wild speech data into high-quality training data with speech annotations. Experimental results demonstrate the effectiveness of both Emilia and Emilia-Pipe. Demos are available at: https://emilia-dataset.github.io/Emilia-Demo-Page/.<|reference_end|>
@article{he2024emilia:,
    title={Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for
  Large-Scale Speech Generation},
    author={Haorui He, Zengqiang Shang, Chaoren Wang, Xuyuan Li, Yicheng Gu, Hua
  Hua, Liwei Liu, Chen Yang, Jiaqi Li, Peiyang Shi, Yuancheng Wang, Kai Chen,
  Pengyuan Zhang, Zhizheng Wu},
    journal={arXiv preprint arXiv:2407.05361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05361},
    primaryClass={eess.AS cs.CL}
}