  Prior works have demonstrated zero-shot text-to-speech by using a generative
language model on audio tokens obtained via a neural audio codec. It is still
challenging, however, to adapt them to low-latency scenarios. In this paper, we
present LiveSpeech - a fully autoregressive language model-based approach for
zero-shot text-to-speech, enabling low-latency streaming of the output audio.
To allow multiple token prediction within a single decoding step, we propose
(1) using adaptive codebook loss weights that consider codebook contribution in
each frame and focus on hard instances, and (2) grouping codebooks and
processing groups in parallel. Experiments show our proposed models achieve
competitive results to state-of-the-art baselines in terms of content accuracy,
speaker similarity, audio quality, and inference speed while being suitable for
low-latency streaming applications.
