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EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer

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arxiv 2409.10819 v2 pith:4D2E762Q submitted 2024-09-17 eess.AS cs.SD

Jiarui Hai , Yong Xu , Hao Zhang , Chenxing Li , Helin Wang , Mounya Elhilali , Dong Yu This is my paper · ORCID
classification eess.AScs.SD
keywords audioezaudiogenerationconvergencedesigneddiffusionefficientenhancing
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We introduce EzAudio, a text-to-audio (T2A) generation framework designed to produce high-quality, natural-sounding sound effects. Core designs include: (1) We propose EzAudio-DiT, an optimized Diffusion Transformer (DiT) designed for audio latent representations, improving convergence speed, as well as parameter and memory efficiency. (2) We apply a classifier-free guidance (CFG) rescaling technique to mitigate fidelity loss at higher CFG scores and enhancing prompt adherence without compromising audio quality. (3) We propose a synthetic caption generation strategy leveraging recent advances in audio understanding and LLMs to enhance T2A pretraining. We show that EzAudio, with its computationally efficient architecture and fast convergence, is a competitive open-source model that excels in both objective and subjective evaluations by delivering highly realistic listening experiences. Code, data, and pre-trained models are released at: https://haidog-yaqub.github.io/EzAudio-Page/.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models

    cs.SD 2026-07 conditional novelty 6.5 of 10

    Hierarchical multi-prompt representation generation plus generalized flow matching yields high-quality single-stage waveform diffusion from 12.5 Hz latents and efficient LDM TTS.

  2. Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models

    eess.AS 2026-07 conditional novelty 6.0 of 10

    Using audio-aware LLMs to judge event presence and temporal order as DPO rewards improves multi-event text-to-audio instruction following.

  3. Unified Audio Intelligence Without Regressing on Text Intelligence

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A unified 30B MoE audio-text LLM achieves state-of-the-art audio understanding, generation, and speech tasks while preserving text reasoning comparable to its text-only backbone.

  4. IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling

    eess.AS 2025-05 conditional novelty 5.0 of 10

    On AudioCaps, IMPACT reports the best Fréchet Distance and Fréchet Audio Distance among the compared systems while generating audio faster than diffusion baselines.

  5. SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline

    eess.AS 2025-05 conditional novelty 5.0 of 10

    A cascaded pipeline of audio compression, latent diffusion extraction, and generative correction achieves state-of-the-art target speech extraction quality and intelligibility on Libri2Mix and out-of-domain data.

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