  Zero-shot multi-speaker text-to-speech (ZS-TTS) systems have advanced for
English, however, it still lags behind due to insufficient resources. We
address this gap for Arabic, a language of more than 450 million native
speakers, by first adapting a sizeable existing dataset to suit the needs of
speech synthesis. Additionally, we employ a set of Arabic dialect
identification models to explore the impact of pre-defined dialect labels on
improving the ZS-TTS model in a multi-dialect setting. Subsequently, we
fine-tune the
XTTS\footnote{https://docs.coqui.ai/en/latest/models/xtts.html}\footnote{https://medium.com/machine-learns/xtts-v2-new-version-of-the-open-source-text-to-speech-model-af73914db81f}\footnote{https://medium.com/@erogol/xtts-v1-techincal-notes-eb83ff05bdc}
model, an open-source architecture. We then evaluate our models on a dataset
comprising 31 unseen speakers and an in-house dialectal dataset. Our automated
and human evaluation results show convincing performance while capable of
generating dialectal speech. Our study highlights significant potential for
improvements in this emerging area of research in Arabic.
