  This paper introduces Opencpop, a publicly available high-quality Mandarin
singing corpus designed for singing voice synthesis (SVS). The corpus consists
of 100 popular Mandarin songs performed by a female professional singer. Audio
files are recorded with studio quality at a sampling rate of 44,100 Hz and the
corresponding lyrics and musical scores are provided. All singing recordings
have been phonetically annotated with phoneme boundaries and syllable (note)
boundaries. To demonstrate the reliability of the released data and to provide
a baseline for future research, we built baseline deep neural network-based SVS
models and evaluated them with both objective metrics and subjective mean
opinion score (MOS) measure. Experimental results show that the best SVS model
trained on our database achieves 3.70 MOS, indicating the reliability of the
provided corpus. Opencpop is released to the open-source community WeNet, and
the corpus, as well as synthesized demos, can be found on the project homepage.
