  Textless spoken language processing research aims to extend the applicability
of standard NLP toolset onto spoken language and languages with few or no
textual resources. In this paper, we introduce textless-lib, a PyTorch-based
library aimed to facilitate research in this research area. We describe the
building blocks that the library provides and demonstrate its usability by
discuss three different use-case examples: (i) speaker probing, (ii) speech
resynthesis and compression, and (iii) speech continuation. We believe that
textless-lib substantially simplifies research the textless setting and will be
handful not only for speech researchers but also for the NLP community at
large. The code, documentation, and pre-trained models are available at
https://github.com/facebookresearch/textlesslib/ .
