Yuning Wu, Jiatong Shi, Yifeng Yu, Yuxun Tang, Tao Qian, Yueqian Lin, Jionghao Han, Xinyi Bai, Shinji Watanabe, Qin Jin
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引用次数: 0
Abstract
This research presents Muskits-ESPnet, a versatile toolkit that introduces
new paradigms to Singing Voice Synthesis (SVS) through the application of
pretrained audio models in both continuous and discrete approaches.
Specifically, we explore discrete representations derived from SSL models and
audio codecs and offer significant advantages in versatility and intelligence,
supporting multi-format inputs and adaptable data processing workflows for
various SVS models. The toolkit features automatic music score error detection
and correction, as well as a perception auto-evaluation module to imitate human
subjective evaluating scores. Muskits-ESPnet is available at
\url{https://github.com/espnet/espnet}.
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