Publications
カンファレンス (国際) NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit
Ryuichi Yamamoto (LINE/Nagoya University), Reo Yoneyama (Nagoya University), Tomoki Toda (Nagoya University)
2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
2023.6.4
This paper describes the design of NNSVS, an open-source soft-ware for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency models, and neural vocoders. Furthermore, NNSVS provides extensive documentation and numerous scripts to build complete singing voice synthesis systems. Experimental results demonstrate that our best system significantly outperforms our reproduction of Sinsy and other baseline systems. The toolkit is available at https://github.com/nnsvs/nnsvs.
Paper :
NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit
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