  This paper introduces a high-quality open-source text-to-speech (TTS)
synthesis dataset for Mongolian, a low-resource language spoken by over 10
million people worldwide. The dataset, named MnTTS, consists of about 8 hours
of transcribed audio recordings spoken by a 22-year-old professional female
Mongolian announcer. It is the first publicly available dataset developed to
promote Mongolian TTS applications in both academia and industry. In this
paper, we share our experience by describing the dataset development procedures
and faced challenges. To demonstrate the reliability of our dataset, we built a
powerful non-autoregressive baseline system based on FastSpeech2 model and
HiFi-GAN vocoder, and evaluated it using the subjective mean opinion score
(MOS) and real time factor (RTF) metrics. Evaluation results show that the
powerful baseline system trained on our dataset achieves MOS above 4 and RTF
about $3.30\times10^{-1}$, which makes it applicable for practical use. The
dataset, training recipe, and pretrained TTS models are freely available
\footnote{\label{github}\url{https://github.com/walker-hyf/MnTTS}}.
