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arxiv: 2606.31259 · v1 · pith:Y2ZK2JQV · submitted 2026-06-30 · cs.SD · cs.AI· cs.MM· eess.AS

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

Binh Mai , Tran Quoc Bao Le , Hung Dinh , Cong Tran This is my paper

Reviewed by Pith2026-07-01 03:41 UTCgrok-4.3pith:Y2ZK2JQVopen to challenge →

classification cs.SD cs.AIcs.MMeess.AS
keywords text-to-audiodiffusion distillationone-step generationvariational score distillationaudio generationcaption-only training
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The pith

SwiftAudio distills a one-step text-to-audio model from a diffusion teacher using only text captions.

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

The paper demonstrates a method to train a fast, single-step text-to-audio generator by distilling from an existing multi-step diffusion model. It avoids the need for any paired text-audio examples and instead uses only text captions as supervision. The approach adapts variational score distillation to the audio setting and adds a regularization term that promotes smooth changes across time in the latent audio. With this setup the student model can be trained on roughly 45,000 captions and reaches the best reported results among strict one-step systems while reducing the performance gap to slower multi-step diffusion models.

Core claim

By adapting Variational Score Distillation to the audio domain and introducing a temporal smoothness regularization objective, the student model inherits the teacher's generative prior without requiring paired audio supervision and allows effective training with only approximately 45K captions.

What carries the argument

Adaptation of Variational Score Distillation to audio together with a temporal smoothness regularization objective that transfers the diffusion teacher's prior from captions alone.

If this is right

  • No paired audio data is required for training the one-step generator.
  • Inference requires only a single forward pass instead of iterative denoising steps.
  • State-of-the-art results are obtained among strict one-step methods on AudioCaps and Clotho.
  • Training succeeds with a modest set of approximately 45K text captions.

Where Pith is reading between the lines

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

  • The same caption-only distillation pattern could be tested on text-to-video or text-to-image tasks.
  • Increasing the caption count beyond 45K might further reduce the remaining gap to multi-step systems.
  • Single-step generation would lower the compute needed for on-device or real-time audio synthesis.

Load-bearing premise

The combination of adapted variational score distillation and temporal smoothness regularization transfers the teacher's generative capability using only text captions.

What would settle it

Training the proposed student model on the 45K captions and measuring that its audio quality metrics on AudioCaps or Clotho remain below those of prior one-step baselines.

Figures

Figures reproduced from arXiv: 2606.31259 by Binh Mai, Cong Tran, Hung Dinh, Tran Quoc Bao Le.

Figure 1
Figure 1. Figure 1: Conceptual illustration of existing TTA paradigms with respect to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed SwiftAudio framework. The student fθ is trained using the total joint loss Ltotal, integrating the VSD guidance (LVSD) from the teacher models and the temporal smoothness constraint (Ltemp) on the synthesized latents. The LoRA teacher ϵϕ is alternately updated via LLoRA to accurately estimate the student score. TABLE I COMPARISON OF REPRESENTATIVE ONE-STEP GENERATION FRAMEWORKS. T2… view at source ↗
Figure 3
Figure 3. Figure 3: Conceptual illustration of temporal regularization. Compared with VSD-only generation, adding temporal TV encourages a piecewise-smooth latent [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results of the SwiftAudio framework. The figure illustrates [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Semantic Controllability and Latent Disentanglement in SwiftAudio. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The assessment interface for rating audio quality and relevance based [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Instruction page for participants, defining the rating criteria and [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualizing semantic control via word swapping. SwiftAudio precisely modifies specific sound events and acoustic properties (e.g., source, environment, [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Effect of attention reweighting. Increasing the weight of specific tokens enhances the intensity and presence of the target sound in the synthesized [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Word refinement results in one-step text-to-audio generation. Starting from a base prompt (left), additional semantic phrases are appended (right), [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
read the original abstract

Diffusion-based text-to-audio (TTA) models achieve impressive synthesis quality but suffer from high inference latency due to iterative multi-step denoising. Existing one-step approaches alleviate this issue but still rely on paired text--audio data during distillation. To address these limitations, we propose SwiftAudio, a one-step TTA framework that performs audio-free distillation from a pretrained diffusion teacher using only text captions. Specifically, we adapt Variational Score Distillation (VSD) to the audio domain and introduce a temporal smoothness regularization objective to encourage coherent latent audio representations. This design enables the student model to inherit the teacher's generative prior without requiring paired audio supervision and allows effective training with only approximately 45K captions. Experiments on AudioCaps and Clotho demonstrate that SwiftAudio achieves state-of-the-art performance among strict one-step methods and substantially narrows the gap to multi-step diffusion systems. Project page: https://swiftaudio.org/

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.

Referee Report

2 major / 1 minor

Summary. The paper proposes SwiftAudio, a one-step text-to-audio (TTA) framework for caption-only distillation from a pretrained diffusion teacher. It adapts Variational Score Distillation (VSD) to the audio domain and introduces a temporal smoothness regularization objective, enabling training on ~45K captions without paired audio data. Experiments on AudioCaps and Clotho are claimed to show SOTA performance among strict one-step methods while narrowing the gap to multi-step diffusion systems.

Significance. If the experimental claims hold with proper validation, the work would advance data-efficient and low-latency TTA generation by removing the need for paired audio supervision during distillation. The caption-only setting and regularization approach address practical barriers in deploying diffusion-based audio models.

major comments (2)
  1. [Abstract] Abstract: the claim of achieving SOTA among one-step methods and substantially narrowing the gap to multi-step systems is asserted without any reported metrics, baselines, error bars, ablation studies, or quantitative results. This prevents verification of the central empirical claim.
  2. [Method] Method description: the adaptation of VSD to audio together with the temporal smoothness term is presented at a high level; without explicit loss equations or analysis showing that the student inherits the teacher's generative prior (rather than collapsing to a trivial or teacher-copying solution), it is unclear whether the approach is load-bearing for the no-paired-audio claim.
minor comments (1)
  1. [Abstract] The project page URL is given but the manuscript should be self-contained with at least a summary table of key metrics and comparisons.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of achieving SOTA among one-step methods and substantially narrowing the gap to multi-step systems is asserted without any reported metrics, baselines, error bars, ablation studies, or quantitative results. This prevents verification of the central empirical claim.

    Authors: We agree that the abstract would benefit from explicit quantitative support for its claims. In the revised version we will incorporate representative metrics from the experimental section (including comparisons to one-step and multi-step baselines on AudioCaps and Clotho) together with a brief mention of error bars and the scale of the caption-only training set. revision: yes

  2. Referee: [Method] Method description: the adaptation of VSD to audio together with the temporal smoothness term is presented at a high level; without explicit loss equations or analysis showing that the student inherits the teacher's generative prior (rather than collapsing to a trivial or teacher-copying solution), it is unclear whether the approach is load-bearing for the no-paired-audio claim.

    Authors: We acknowledge that the current method presentation remains high-level. We will expand Section 3 to include the full loss equations for the audio-adapted VSD objective and the temporal smoothness regularizer. We will also add a dedicated analysis subsection (with supporting ablations) that examines the student’s behavior under the caption-only regime and demonstrates that the combined objective prevents collapse to trivial or teacher-copying solutions while transferring the teacher’s generative prior. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation relies on external VSD adaptation and empirical regularization

full rationale

The paper adapts Variational Score Distillation (VSD) to audio and adds a temporal smoothness term for caption-only training of a one-step student. No equations or derivation steps are shown that reduce a claimed prediction to a fitted input or self-citation by construction. The central claim (student inherits teacher prior via adapted losses on ~45K captions) is presented as an empirical outcome of the loss design rather than a definitional identity. No self-citation load-bearing, uniqueness theorem, or ansatz smuggling is referenced. This is the common case of a method paper whose validity rests on external benchmarks rather than internal tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are described.

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discussion (0)

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Reference graph

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