VoxtLM:

Unified decoder-only models for consolidating speech recognition, synthesis and speech, text continuation tasks

[paper] [code]

Soumi Maiti1, Yifan Peng1, Shukjae Choi2, Jee-weon Jung1, Xuankai Chang1, Shinji Watanabe1

1Carnegie Mellon University, USA        242dot Inc., Republic of Korea

Abstract. We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning. Compared to a single-task model, VoxtLM exhibits a significant improvement in speech synthesis, with improvements in both speech intelligibility from 28.9 to 5.6 and objective quality from 2.68 to 3.90. VoxtLM also improves speech generation and speech recognition performance over the single-task counterpart. VoxtLM is trained with publicly available data and training recipes and model checkpoints will be open-sourced to make fully reproducible work.

Overview

VoxtLM is an autoregressive decoder-only LM incorporating speech and text within a single vocabulary. Two additional modules are used: the speech tokenizer for converting continuous signals into discrete speech tokens and the speech token decoder used to generate speech waveforms from speech tokens.

Text-to-Speech

We demonstrate the capability of VoxtLM to generate speech conditioned on text. [samples]

Text-LM

We demonstrate the capability of VoxtLM to generate text conditioned on text. [samples]