  This paper presents fairseq S^2, a fairseq extension for speech synthesis. We
implement a number of autoregressive (AR) and non-AR text-to-speech models, and
their multi-speaker variants. To enable training speech synthesis models with
less curated data, a number of preprocessing tools are built and their
importance is shown empirically. To facilitate faster iteration of development
and analysis, a suite of automatic metrics is included. Apart from the features
added specifically for this extension, fairseq S^2 also benefits from the
scalability offered by fairseq and can be easily integrated with other
state-of-the-art systems provided in this framework. The code, documentation,
and pre-trained models are available at
https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
