Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
Abstract
Autoregressive (AR) models with diffusion heads have recently achieved strong text-to-audio performance, yet their iterative decoding and multi-step sampling process introduce high-latency issues. To address this bottleneck, we propose a one-step sampling framework that combines an energy-distance training objective with representation-level distillation. An energy-scoring head maps Gaussian noise directly to audio latents in one step, eliminating the need for a costly recursive diffusion sampling process, while distillation from a masked autoregressive (MAR) text-to-audio model preserves the strong conditioning learned during diffusion training. On the AudioCaps benchmark, our method consistently outperforms prior one-step baselines such as ConsistencyTTA, SoundCTM, AudioLCM and AudioTurbo, on both objective and subjective metrics, while substantially narrowing the quality gap to AR diffusion systems with multi-step sampling. Compared to the state-of-the-art AR diffusion system, IMPACT, our approach achieves up to $8.5$x faster batch inference with highly competitive audio quality. These results demonstrate that combining energy-distance training with representation-level distillation provides an effective recipe for fast, high-quality text-to-audio synthesis.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a significant advancement in efficient generative media by introducing a one-step sampling framework that achieves substantially faster inference while maintaining high audio fidelity and semantic relevance. The innovative combination of energy-distance training and representation distillation represents a meaningful contribution to the field of machine learning, particularly in audio generation.
Methodology
The proposed methodology introduces a novel one-step sampling framework for text-to-audio generation that integrates an energy-distance training objective with representation-level distillation. This approach effectively reduces inference latency while maintaining audio quality, addressing a significant limitation in existing autoregressive models that rely on multi-step sampling. The use of energy-scoring to map Gaussian noise directly to audio latents is innovative and demonstrates a clear departure from traditional diffusion-based methods. The incorporation of distillation from a masked autoregressive model further enhances the model's performance, showcasing a thoughtful combination of techniques to achieve rapid and high-quality audio synthesis.
Results & evaluation
The experimental evaluation is comprehensive, utilizing the AudioCaps benchmark for both objective and subjective assessments. The paper reports consistent improvements over existing one-step baselines, with significant gains in fidelity and semantic relevance as measured by various metrics (FD, FAD, KL, IS, CLAP). The results demonstrate not only superior performance compared to prior models but also a substantial reduction in inference time, achieving up to 8.5 times faster batch inference than the state-of-the-art AR diffusion system, IMPACT. The thoroughness of the experiments, including ablation studies on representation distillation and classifier-free guidance, adds credibility to the findings.
Reproducibility
The paper provides detailed descriptions of the experimental setup, including datasets, model configurations, and evaluation metrics, which contribute to reproducibility. However, the absence of a publicly accessible code repository or demo limits the ability of other researchers to replicate the results directly. Clear documentation of hyperparameters and training procedures is essential for future work in this area.
Limitations
While the proposed method shows promising results, it still falls short of the audio quality achieved by multi-step diffusion models, indicating that there may be inherent trade-offs between speed and fidelity. The reliance on a single sampling step may also limit the model's flexibility in generating more complex audio sequences. Additionally, the paper does not address potential biases in the training datasets, which could affect the generalizability of the model.
Broader impact
The advancements in low-latency text-to-audio generation have significant implications for real-time applications in multimedia content creation, interactive media, and personalized audio experiences. The ability to generate high-quality audio quickly opens up new avenues for user engagement and creative expression. Furthermore, the integration of energy-distance training and representation distillation could inspire future research in other generative tasks across different modalities. The paper presents a significant advancement in efficient generative media by introducing a one-step sampling framework that achieves substantially faster inference while maintaining high audio fidelity and semantic relevance. The innovative combination of energy-distance training and representation distillation represents a meaningful contribution to the field of machine learning, particularly in audio generation.
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