VioLA: Unified Codec Language Models for Speech Recognition, Synthesis,
  and Translation
Tianrui Wang Long Zhou Ziqiang Zhang Yu Wu Shujie Liu Yashesh Gaur Zhuo Chen Jinyu Li Furu Wei
Recent research shows a big convergence in model architecture, training
objectives, and inference methods across various tasks for different
modalities. In this paper, we propose VioLA, a single auto-regressive
Transformer decoder-only network that unifies various cross-modal tasks
involving speech and text, such as speech-to-text, text-to-text,
text-to-speech, and speech-to-speech tasks, as a conditional codec language
model task via multi-task learning framework. To accomplish this, we first
convert all the speech utterances to discrete tokens (similar to the textual
data) using an offline neural codec encoder. In such a way, all these tasks are
converted to token-based sequence conversion problems, which can be naturally
handled with one conditional language model. We further integrate task IDs
(TID) and language IDs (LID) into the proposed model to enhance the modeling
capability of handling different languages and tasks. Experimental results
demonstrate that the proposed VioLA model can support both single-modal and
cross-modal tasks well, and the decoder-only model achieves a comparable and
even better performance than the strong baselines.