TLDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech
Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrained backbones. However, this token-level formulation creates a structural efficiency bottleneck: speech-token sequences are much longer than text sequences, requiring the AR backbone to perform causal computation at every token position and maintain a KV cache that grows with the sequence length. We introduce TLDR, a patch-based autoregressive framework that accelerates codec-based AR-TTS by shifting the causal modeling from token-level speech sequences to patch-level sequences. TLDR groups consecutive codec tok...
| Authors | Yejin Lee, Junwon Moon, Hyoeun Kim et al. |
|---|---|
| Primary category | cs.SD |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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