Title: Gogo: Group-wise granularity-ordered codec for stable and efficient speech generation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: speech codec, speech language model, speech generation, text-to-speech synthesis

Abstract: Current speech language models require their core component, the speech codec, to discretize continuous speech signals into tokens that not only capture high-level cues for autoregressive modeling but also preserve sufficient acoustic details for perceptual quality. To address this need, we propose Gogo, a group-wise granularity-ordered codec that quantizes each group of frames into tokens arranged from coarse to fine, where coarse tokens encode high-level abstractions and fine tokens progressively recover low-level details. Building on the granularity-ordering property of Gogo, we introduce GogoSpeech, a two-stage speech language model that performs speech generation by first constructing a coarse speech backbone at an extremely low token rate and then enriching the backbone with fine-grained acoustic details. Considering the inherently non-uniform information distribution in speech signals, we further design a Group Relative Policy Optimization (GRPO)-trained token allocator that adaptively allocates token budgets to groups based on group-wise complexity. Experimental results demonstrate that Gogo delivers state-of-the-art reconstruction performance across most metrics at a token rate of 47. Moreover, evaluations on zero-shot text-to-speech tasks show that GogoSpeech enables efficient generation by adaptively reducing the average token rate, and attains state-of-the-art results in long-form speech generation.