textless-lib: a Library for Textless Spoken Language Processing
This library simplifies research for speech and NLP communities by providing tools for processing spoken language in languages with few or no textual resources, though it is incremental as it builds on existing methods.
The authors introduced textless-lib, a PyTorch-based library to facilitate textless spoken language processing research, enabling tasks like speaker probing, speech resynthesis, and speech continuation without relying on textual resources.
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/ .