KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset
This provides a valuable resource for researchers and developers working on Kazakh TTS, addressing a gap for a low-resource language spoken by over 13 million people.
The authors tackled the lack of resources for Kazakh text-to-speech by creating a high-quality open-source dataset with 93 hours of transcribed audio from two speakers, and baseline models achieved a mean opinion score above 4, making them suitable for practical use.
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.