SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation
arXiv:2606.31259v1 Announce Type: cross 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 tempo
The rapid advancement in AI necessitates more efficient and less data-intensive methods for generating high-quality synthetic media, driving innovation in distillation techniques.
This development significantly lowers the barrier to entry for high-quality text-to-audio generation by reducing the need for costly paired data and compute resources.
Audio generation models can now be distilled more efficiently using only text captions, leading to faster inference and potentially broader accessibility and application.
- · AI developers
- · Content creators
- · Audio synthesis platforms
- · Smaller AI labs
- · Large-scale audio data providers
- · Legacy text-to-audio models
One-step text-to-audio generation becomes more accessible and faster due to data-efficient distillation.
This efficiency could lead to a proliferation of synthetic audio content, impacting media production and potentially increasing challenges in audio content verification.
The reduced data requirements might accelerate the development of personalized synthetic audio experiences and bespoke sound design at scale.
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Read at arXiv cs.AI