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DiffRhythm: Fast End-to-End Full-Length Song Generation with Latent Diffusion
Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion Ziqian Ning 1, Huakang Chen 1, Yuepeng Jiang 1, Jixun Yao 1, Chunbo Hao 1, Guobin Ma 1, Shuai Wang 2, Lei Xie 1 Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science, Northwestern Polytechnical University, Xi'an, China Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China 1. Abstract Recent advancements in music generation have garnered significant attention, yet existing approaches face critical limitations.
Ziqian Ning 1, Huakang Chen 1, Yuepeng Jiang 1, Jixun Yao 1, Chunbo Hao 1, Guobin Ma 1, Shuai Wang 2, Lei Xie 1 Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science, Northwestern Polytechnical University, Xi'an, China Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China Recent advancements in music generation have garnered significant attention, yet existing approaches face critical limitations. While designed for positive use cases, potential risks include unintentional copyright infringement through stylistic similarities, inappropriate blending of cultural musical elements, and misuse for generating harmful content. To ensure responsible deployment, users must implement verification mechanisms to confirm musical originality, disclose AI involvement in generated works, and obtain permissions when adapting protected styles.
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