  Lyrics generation is a well-known application in natural language generation
research, with several previous studies focusing on generating accurate lyrics
using precise control such as keywords, rhymes, etc. However, lyrics imitation,
which involves writing new lyrics by imitating the style and content of the
source lyrics, remains a challenging task due to the lack of a parallel corpus.
In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics
imitation system that can generate new lyrics based on the text of source
lyrics. To address the issue of lacking a parallel training corpus for lyrics
imitation, we propose a novel framework to construct a parallel corpus based on
a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new
lyrics, source lyrics)} are used to train the lyrics imitation model. During
the inference process, we utilize a post-processing module to filter and rank
the generated lyrics, selecting the highest-quality ones. We incorporated audio
information and aligned the lyrics with the audio to form the songs as a bonus.
The human evaluation results show that our framework can perform better lyric
imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the
system is available at
\href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and
\href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.
