  In this work, we take on the challenging task of building a single
text-to-speech synthesis system that is capable of generating speech in over
7000 languages, many of which lack sufficient data for traditional TTS
development. By leveraging a novel integration of massively multilingual
pretraining and meta learning to approximate language representations, our
approach enables zero-shot speech synthesis in languages without any available
data. We validate our system's performance through objective measures and human
evaluation across a diverse linguistic landscape. By releasing our code and
models publicly, we aim to empower communities with limited linguistic
resources and foster further innovation in the field of speech technology.
