  We present a multilingual end-to-end Text-To-Speech framework that maps byte
inputs to spectrograms, thus allowing arbitrary input scripts. Besides strong
results on 40+ languages, the framework demonstrates capabilities to adapt to
various new languages under extreme low-resource and even few-shot scenarios of
merely 40s transcribed recording without the need of lexicon, extra corpus,
auxiliary models, or particular linguistic expertise, while retains
satisfactory intelligibility and naturalness matching rich-resource models.
Exhaustive comparative studies are performed to reveal the potential of the
framework for low-resource application and the impact of various factors
contributory to adaptation. Furthermore, we propose a novel method to extract
language-specific sub-networks for a better understanding of the mechanism of
multilingual models.
