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.

通过大规模多语言预训练和元学习，我们构建了一个能够在超过 7000 种语言中生成语音的单一文本转语音合成系统，并通过客观评估和人工评价验证了该系统在多样化语言环境下的性能。通过公开发布我们的代码和模型，我们旨在为语言资源有限的社区提供支持，并在语音技术领域促进进一步的创新。