  This paper introduces a high-quality open-source speech synthesis dataset for
Kazakh, a low-resource language spoken by over 13 million people worldwide. The
dataset consists of about 93 hours of transcribed audio recordings spoken by
two professional speakers (female and male). It is the first publicly available
large-scale dataset developed to promote Kazakh text-to-speech (TTS)
applications in both academia and industry. In this paper, we share our
experience by describing the dataset development procedures and faced
challenges, and discuss important future directions. To demonstrate the
reliability of our dataset, we built baseline end-to-end TTS models and
evaluated them using the subjective mean opinion score (MOS) measure.
Evaluation results show that the best TTS models trained on our dataset achieve
MOS above 4 for both speakers, which makes them applicable for practical use.
The dataset, training recipe, and pretrained TTS models are freely available.
