  In singing voice synthesis (SVS), generating singing voices from musical
scores faces challenges due to limited data availability. This study proposes a
unique strategy to address the data scarcity in SVS. We employ an existing
singing voice synthesizer for data augmentation, complemented by detailed
manual tuning, an approach not previously explored in data curation, to reduce
instances of unnatural voice synthesis. This innovative method has led to the
creation of two expansive singing voice datasets, ACE-Opencpop and ACE-KiSing,
which are instrumental for large-scale, multi-singer voice synthesis. Through
thorough experimentation, we establish that these datasets not only serve as
new benchmarks for SVS but also enhance SVS performance on other singing voice
datasets when used as supplementary resources. The corpora, pre-trained models,
and their related training recipes are publicly available at ESPnet-Muskits
(\url{https://github.com/espnet/espnet})
