7 papers
DELTA-TTS: Adapting Autoregressive Model into Diffusion Language Model for Text-to-Speech
Junwon Moon, Seungbeom Kim, Yejin Lee +4
Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness, since local errors propagate to…
TLDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech
Yejin Lee, Junwon Moon, Hyoeun Kim +3
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 pretrai…
GLASS: GRPO-Trained LoRA for Acoustic Style Steering in Zero-Shot Text-to-Speech
Jaehoon Kang, Yejin Lee, Kyuhong Shim
We propose GLASS, a framework for composable acoustic style control in zero-shot autoregressive text-to-speech (TTS) that learns controls from post-generation rewards rather than s…
Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
Jaehoon Kang, Yejin Lee, Yoonji Park +1
While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control and apply a single global styl…
DialBGM: A Benchmark for Background Music Recommendation from Everyday Multi-Turn Dialogues
Joonhyeok Shin, Jaehoon Kang, Yujun Lee +4
Selecting an appropriate background music (BGM) that supports natural human conversation is a common production step in media and interactive systems. In this paper, we introduce d…
Efficient Speculative Decoding for Llama at Scale: Challenges and Solutions
Bangsheng Tang, Carl Chengyan Fu, Fei Kou +35
Speculative decoding is a standard method for accelerating the inference speed of large language models. However, scaling it for production environments poses several engineering c…