arXiv AI July 1, 2026 By Binh Mai, Tran Quoc Bao Le, Hung Dinh, Cong Tran

SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation

arXiv:2606. 31259v1 Announce Type: cross Abstract: Diffusion-based text-to-audio (TTA) models achieve impressive synthesis quality but suffer from high inference latency due to iterative multi-step denoising.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 8

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation

arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.

By Yun-Chen Cheng, Tzu-Hung Huang, Chih-Pin Tan

benchmarks safety

arXiv AI

Aug 11

CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents

arXiv:2608. 08638v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) now supports interactive assistants, personalized media, and accessibility tools.

By Yuqian Zhang, Yao Shi, Kexin Huang, Botian Jiang, Zhe Xu, Yiwei Zhao, Min Liang, Shuang Chen, Xipeng Qiu

diffusion efficiency multimodal

arXiv Machine Learning

4d ago

Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech

arXiv:2609.36324v1 Announce Type: cross Abstract: Flow-matching text-to-speech (TTS) models achieve high synthesis quality but require many neural function evaluations (NFEs) to integrate their gener...

By Yentl Collin, Evan Dufraisse, Amr Mohamed, Amine Khelif Khelif, Dani Bouch, Guokan Shang

efficiency multimodal safety

arXiv AI

Jul 1

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.

By Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

llms

arXiv AI

3d ago

MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion

MeanVoiceFlow2 is a new voice conversion framework that jointly optimizes a flow-based conversion module and a computationally efficient content encoder. It is trained via conversion distillation from MeanVoiceFlow and real data reconstruction, and further enhanced with diffusion-GAN training, sample mixing, and teacher-guided conditioning augmentation. Experiments on zero-shot voice conversion show that MeanVoiceFlow2 delivers higher perceptual quality and about nine times faster inference than its predecessor while preserving speaker similarity.

By Takuhiro Kaneko, Hirokazu Kameoka, Kou Tanaka, Yuto Kondo

diffusion efficiency

arXiv Machine Learning

Aug 6

Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation

arXiv:2608. 04902v1 Announce Type: cross Abstract: Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis.

By Zehua Chen, Junyou Wang, Yuxuan Jiang, Zhenying Fang, Yusheng Dai, Jianfei Chen, Ziwei Liu, Jun Zhu

diffusion multimodal benchmarks

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Related stories

Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation

CuteTTS: Efficient and High-Quality Speech Synthesis via Autoregressive Modeling of Continuous Latents

Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion

Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation