Textually Pretrained Speech Language Models
Michael Hassid, Tal Remez, Tu Anh Nguyen, Itai Gat, Alexis Conneau, Felix Kreuk, Jade Copet, Alexandre Défossez
Abstract
Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. We make speech samples, code and models publicly available.
BibTeX
@inproceedings{
hassid2023textually,
title={Textually Pretrained Speech Language Models},
author={Michael Hassid and Tal Remez and Tu Anh Nguyen and Itai Gat and Alexis Conneau and Felix Kreuk and Jade Copet and Alexandre D{\'e}fossez and Gabriel Synnaeve and Emmanuel Dupoux and Roy Schwartz and Yossi Adi},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=UlHueVjAKr}
}