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arXiv · 2609.31525

TinyAudio: Compact and Efficient Text-to-Audio Generation for Low-Resource Deployment

Junxi Liu·Xiquan Li·Wenhao Guan·Yifan Duan·Zhikang Niu·Yanru Huo·Ziyang Ma·Xie Chen
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Abstract

Text-to-audio (TTA) generation has advanced rapidly in generation quality and instruction following. However, representative systems often require around a billion parameters, limiting deployment on resource-constrained devices. This paper introduces TinyAudio, a compact flow-matching-based TTA model for low-resource deployment. At its core, TinyAudio uses TA-DiT, a 35M single-stream flow-matching Transformer. TinyAudio also includes TA-CLAP, a 32M audio-aligned text encoder, and TA-VAE, whose 20M decoder reconstructs 44.1 kHz audio from compressed latents. TinyAudio has only 87M parameters in total, over 90% fewer than representative billion-parameter pipelines, and uses 0.48 GB peak GPU memory. TinyAudio achieves competitive generation quality on AudioCaps and TTA-Bench. We further introduce TinyAudio-MF, a MeanFlow-accelerated model that enables real-time generation with a four-core CPU quota. Our results demonstrate a practical quality-footprint trade-off for low-resource TTA deployment.

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Junxi Liu, Xiquan Li, Wenhao Guan, Yifan Duan, Zhikang Niu, Yanru Huo, Ziyang Ma, Xie Chen. 2026-09-25. TinyAudio: Compact and Efficient Text-to-Audio Generation for Low-Resource Deployment. https://arxiv.org/abs/2609.31525

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