  Recent work has shown that it is possible to resynthesize high-quality speech
based, not on text, but on low bitrate discrete units that have been learned in
a self-supervised fashion and can therefore capture expressive aspects of
speech that are hard to transcribe (prosody, voice styles, non-verbal
vocalization). The adoption of these methods is still limited by the fact that
most speech synthesis datasets are read, severely limiting spontaneity and
expressivity. Here, we introduce Expresso, a high-quality expressive speech
dataset for textless speech synthesis that includes both read speech and
improvised dialogues rendered in 26 spontaneous expressive styles. We
illustrate the challenges and potentials of this dataset with an expressive
resynthesis benchmark where the task is to encode the input in low-bitrate
units and resynthesize it in a target voice while preserving content and style.
We evaluate resynthesis quality with automatic metrics for different
self-supervised discrete encoders, and explore tradeoffs between quality,
bitrate and invariance to speaker and style. All the dataset, evaluation
metrics and baseline models are open source
