AudioX: A Unified Framework for Anything-to-Audio Generation

Liumeng Xue; Qifeng Chen; Ruibin Yuan; Wei Xue; Xu Tan; Yike Guo; Yizhu Jin; Zeyue Tian; Zhaoyang Liu

arxiv: 2503.10522 · v4 · submitted 2025-03-13 · 💻 cs.MM · cs.CV· cs.LG· cs.SD· eess.AS

AudioX: A Unified Framework for Anything-to-Audio Generation

Zeyue Tian , Zhaoyang Liu , Yizhu Jin , Ruibin Yuan , Liumeng Xue , Xu Tan , Qifeng Chen , Wei Xue

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Yike Guo

This is my paper

Pith reviewed 2026-05-23 00:57 UTC · model grok-4.3

classification 💻 cs.MM cs.CVcs.LGcs.SDeess.AS

keywords audio generationmultimodal fusiontext-to-audiotext-to-musicunified frameworkmultimodal controlaudio synthesis

0 comments

The pith

AudioX unifies text, video and audio inputs into one model for generating sound and music.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents AudioX as a single framework that accepts any mix of text, video, and audio as control signals to produce audio output. Its central design is a fusion module that aligns these different inputs before generation, supported by a new dataset of over seven million annotated examples. If the approach holds, it would let users direct audio creation with flexible combinations of prompts rather than one modality at a time. The authors report stronger results than prior systems on text-to-audio and text-to-music tasks.

Core claim

AudioX integrates varied multimodal conditions through a Multimodal Adaptive Fusion module and trains on the IF-caps dataset of more than seven million samples to enable audio generation from text, video, and audio signals, outperforming prior methods especially on text-to-audio and text-to-music benchmarks.

What carries the argument

Multimodal Adaptive Fusion module that combines text, video, and audio inputs to improve cross-modal alignment before audio synthesis.

If this is right

The model achieves superior performance on text-to-audio and text-to-music generation compared with existing methods.
Audio generation becomes possible under mixed multimodal control signals rather than single-modality prompts.
The system exhibits strong instruction-following behavior across the tested control combinations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same fusion approach could be tested on longer or streaming audio outputs to check consistency over time.
Adding image or motion-capture signals might extend the framework without changing the core module design.
Downstream tools for video editing or game audio could directly use the multimodal control capability.

Load-bearing premise

The Multimodal Adaptive Fusion module enables effective fusion of diverse multimodal inputs (text, video, audio), enhancing cross-modal alignment and improving overall generation quality.

What would settle it

A controlled test showing that removing or replacing the fusion module produces no measurable gain in alignment or generation quality on the same dataset would falsify the central design claim.

read the original abstract

Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a unified multimodal modeling framework, and 2) large-scale, high-quality training data. As such, we propose AudioX, a unified framework for anything-to-audio generation that integrates varied multimodal conditions (i.e., text, video, and audio signals) in this work. The core design in this framework is a Multimodal Adaptive Fusion module, which enables the effective fusion of diverse multimodal inputs, enhancing cross-modal alignment and improving overall generation quality. To train this unified model, we construct a large-scale, high-quality dataset, IF-caps, comprising over 7 million samples curated through a structured data annotation pipeline. This dataset provides comprehensive supervision for multimodal-conditioned audio generation. We benchmark AudioX against state-of-the-art methods across a wide range of tasks, finding that our model achieves superior performance, especially in text-to-audio and text-to-music generation. These results demonstrate our method is capable of audio generation under multimodal control signals, showing powerful instruction-following potential. The code and datasets will be available at https://zeyuet.github.io/AudioX/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

AudioX adds a new fusion module and 7M-sample dataset for multimodal audio generation, but the superiority claims rest on experiments not visible in the abstract.

read the letter

The punchline is that AudioX provides a unified framework for audio generation from multiple modalities along with a large new dataset, but the performance superiority is asserted without supporting numbers or details in the abstract. The paper proposes AudioX, which uses a Multimodal Adaptive Fusion module to integrate text, video, and audio conditions. They created the IF-caps dataset with over 7 million samples via a structured annotation process. This addresses two main challenges: having a single model for varied inputs and having enough training data. What stands out is the scale of the dataset and the effort to make it high-quality. That kind of resource can help others in the field move forward on multimodal audio tasks. The benchmarking against state-of-the-art methods and the claim of better results in text-to-audio and text-to-music show the intended use case. The instruction-following potential is highlighted as a strength. On the soft side, the absence of any metrics, baselines, or experimental setup in the summary makes it hard to evaluate if the fusion module really delivers the claimed improvements in cross-modal alignment. The full paper would need to show ablations or comparisons to confirm that. No signs of circular reasoning or reliance on self-defined quantities. This paper is aimed at the generative audio community, particularly those working on flexible control signals for sound and music synthesis. Readers who value new datasets and code releases will get the most from it. It deserves a serious referee because the contributions in data and modeling are substantial enough to warrant detailed review of the methods and results. I recommend sending it to peer review rather than desk rejecting it.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces AudioX, a unified framework for generating audio from arbitrary multimodal control signals (text, video, audio). Its core component is the Multimodal Adaptive Fusion module for cross-modal alignment. The authors construct the IF-caps dataset (>7M samples) via a structured annotation pipeline and train the model on it. They benchmark against prior methods and claim superior performance, especially on text-to-audio and text-to-music tasks, while highlighting instruction-following capability. Code and datasets are promised to be released.

Significance. If the performance claims are substantiated, the work would advance unified multimodal audio generation by addressing both modeling and data challenges. The construction and release of the large-scale IF-caps dataset constitutes a concrete community contribution, and the commitment to open-source code and data supports reproducibility.

major comments (1)

[Abstract] Abstract: the central claim that AudioX 'achieves superior performance' on benchmarks is stated without any metrics, baselines, error bars, or experimental details. This absence leaves the primary empirical assertion without visible support and must be remedied by explicit quantitative results (tables, figures, statistical tests) in the experimental section.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and the opportunity to clarify and strengthen the manuscript. We address the major comment point by point below.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that AudioX 'achieves superior performance' on benchmarks is stated without any metrics, baselines, error bars, or experimental details. This absence leaves the primary empirical assertion without visible support and must be remedied by explicit quantitative results (tables, figures, statistical tests) in the experimental section.

Authors: We agree that the abstract's high-level claim of superior performance benefits from explicit empirical grounding. The experimental section (Section 4) already contains benchmark tables comparing AudioX to prior methods on text-to-audio and text-to-music tasks using standard metrics. To fully address the concern, we will expand the experimental section with a consolidated summary table that directly lists key metrics, baselines, error bars, and any statistical significance tests supporting the abstract claim. We will also add a cross-reference from the abstract to these results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes a new model architecture (AudioX with Multimodal Adaptive Fusion) and constructs a new dataset (IF-caps) to support multimodal audio generation. Performance claims rest on empirical benchmarking against external SOTA baselines rather than any internal reduction of predictions to fitted parameters, self-definitions, or author-prior self-citations. No load-bearing step equates outputs to inputs by construction; the central claims remain independent of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the fusion module is presented as the core design without further decomposition.

pith-pipeline@v0.9.0 · 5776 in / 1020 out tokens · 36243 ms · 2026-05-23T00:57:15.211354+00:00 · methodology

discussion (0)

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