  Large-scale text-to-speech (TTS) models have made significant progress
recently.However, they still fall short in the generation of Chinese dialectal
speech. Toaddress this, we propose Bailing-TTS, a family of large-scale TTS
models capable of generating high-quality Chinese dialectal speech. Bailing-TTS
serves as a foundation model for Chinese dialectal speech generation. First,
continual semi-supervised learning is proposed to facilitate the alignment of
text tokens and speech tokens. Second, the Chinese dialectal representation
learning is developed using a specific transformer architecture and multi-stage
training processes. With the proposed design of novel network architecture and
corresponding strategy, Bailing-TTS is able to generate Chinese dialectal
speech from text effectively and efficiently. Experiments demonstrate that
Bailing-TTS generates Chinese dialectal speech towards human-like spontaneous
representation. Readers are encouraged to listen to demos at
\url{https://c9412600.github.io/bltts_tech_report/index.html}.
