Text-to-Speech (TTS) synthesis for low-resource languages is an attractive
research issue in academia and industry nowadays. Mongolian is the official
language of the Inner Mongolia Autonomous Region and a representative
low-resource language spoken by over 10 million people worldwide. However,
there is a relative lack of open-source datasets for Mongolian TTS. Therefore,
we make public an open-source multi-speaker Mongolian TTS dataset, named
MnTTS2, for the benefit of related researchers. In this work, we prepare the
transcription from various topics and invite three professional Mongolian
announcers to form a three-speaker TTS dataset, in which each announcer records
10 hours of speeches in Mongolian, resulting 30 hours in total. Furthermore, we
build the baseline system based on the state-of-the-art FastSpeech2 model and
HiFi-GAN vocoder. The experimental results suggest that the constructed MnTTS2
dataset is sufficient to build robust multi-speaker TTS models for real-world
applications. The MnTTS2 dataset, training recipe, and pretrained models are
released at: https://github.com/ssmlkl/MnTTS2

本文介绍了一个名为 MnTTS2 的开源多说话者蒙古语文本转语音数据集，旨在为相关研究人员提供帮助。作者使用现代技术构建了这个 30 小时语音数据库，并开发了基于 FastSpeech2 模型的基线系统，实验结果表明该数据集可以用于构建真实世界的鲁棒多说话者 TTS 模型。

MnTTS2: 一个开源的多说话人蒙古语文本到语音合成数据集

MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset

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}https://github.com/walker-hyf/MnTTS}.

这篇论文介绍了一个高质量的蒙古文开源文本到语音（TTS）综合数据集，这是第一个公开可用的数据集，为蒙古 TTS 应用在学术界和工业界的推广做出了贡献。该数据集共包括了约 8 小时的由 22 岁的专业女性蒙古语播音员朗读并进行转录的音频记录。论文中描述了数据集开发的过程、面临的挑战以及构建基于 FastSpeech2 模型和 HiFi-GAN 声码器非自回归基线系统的经验和评估结果，该系统在 MOS 评分上达到 4 以上且 RTF 约为 $3.30	imes10^{-1}$，可以实际应用。