Text-to-Audio Generation

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2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
May 1, 2026Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim +8Text-to-Audio Generation One-Step Generative Modeling
National Taiwan University · Amazon AGI
AMAVA: Adaptive Motion-Aware Video-to-Audio Framework for Visually-Impaired Assistance
Apr 26, 2026Benjamin Klein, Kazi Ruslan Rahman, Sanchita GhoseText-to-Audio Generation
Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA · Department of Mathematics, San Francisco State University, San Francisco, CA 94132, USA · Department of Computer Engineering, San Francisco State University, San Francisco, CA 94132, USA
UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions
Apr 24, 2026Chunyu Qiang, Xiaopeng Wang, Kang Yin +11Text-to-Music Generation Text-to-Audio Generation
1Tianjin University, Tianjin, China · 2Kling Team, Kuaishou Technology, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China
Evaluating Prompt Robustness in Text-to-Audio Systems for Adaptive Virtual Agents and Game Soundtracks
Mar 14, 2026Jiahui Wu, Mei SiMusic Generation Evaluation Text-to-Audio Generation
Rensselaer Polytechnic Institute Troy, New York, USA
An Empirical Analysis of Task-Induced Encoder Bias in Fréchet Audio Distance
Feb 27, 2026Wonwoo JeongText-to-Audio Generation Audio Representation Learning
Dept. of Computer Science and Engineering, Sogang University, South Korea
SemanticAudio: Audio Generation and Editing in Semantic Space
Jan 29, 2026Zheqi Dai, Guangyan Zhang, Haolin He +5Text-to-Audio Generation Audio Editing
The Chinese University of Hong Kong · LIGHTSPEED · Shanghai Jiao Tong University +1

May 1, 2026, cs.SDJ/K move · Enter open · S save

Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation

Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim, Mihee Lee+7

National Taiwan University · Amazon AGI

Text-to-Audio Generation One-Step Generative Modeling

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Autoregressive (AR) models with diffusion heads have recently achieved strong text-to-audio performance, yet their iterative decoding and multi-step sampling process introduce high-latency issues. To address this bottleneck, we propose a one-step sampling framework that combines an energy-distance training objective with representation-level distillation. An energy-scoring head maps Gaussian noise directly to audio latents in one step, eliminating the need for a costly recursive diffusion sampling process, while distillation from a masked autoregressive (MAR) text-to-audio model preserves the strong conditioning learned during diffusion training. On the AudioCaps benchmark, our method consistently outperforms prior one-step baselines such as ConsistencyTTA, SoundCTM, AudioLCM and AudioTurbo, on both objective and subjective metrics, while substantially narrowing the quality gap to AR diffusion systems with multi-step sampling. Compared to the state-of-the-art AR diffusion system, IMPACT, our approach achieves up to

8.5

x faster batch inference with highly competitive audio quality. These results demonstrate that combining energy-distance training with representation-level distillation provides an effective recipe for fast, high-quality text-to-audio synthesis.

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Related topics

Latest papers 36

Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation

AMAVA: Adaptive Motion-Aware Video-to-Audio Framework for Visually-Impaired Assistance

UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions

Evaluating Prompt Robustness in Text-to-Audio Systems for Adaptive Virtual Agents and Game Soundtracks

An Empirical Analysis of Task-Induced Encoder Bias in Fréchet Audio Distance

SemanticAudio: Audio Generation and Editing in Semantic Space