Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation

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

arXiv · 2407.05361

Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation

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

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Abstract

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/.

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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. 2024-07-07. Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation. https://arxiv.org/abs/2407.05361

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