Flashsr: One-step Versatile Audio Super-resolution Via Diffusion Distillation

Abstract

Versatile audio super-resolution (SR) is the challenging task of restoring high-frequency components from low-resolution audio with sampling rates between 4kHz and 32kHz in various domains such as music, speech, and sound effects. Previous diffusion-based SR methods suffer from slow inference due to the need for a large number of sampling steps. In this paper, we introduce FlashSR, a single-step diffusion model for versatile audio super-resolution aimed at producing 48kHz audio. FlashSR achieves fast inference by utilizing diffusion distillation with three objectives: distillation loss, adversarial loss, and distribution-matching distillation loss. We further enhance performance by proposing the SR Vocoder, which is specifically designed for SR models operating on mel-spectrograms. FlashSR demonstrates competitive performance with the current state-of-the-art model in both objective and subjective evaluations while being approximately 22 times faster.

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keyim2025flashsr

Related papers

• Flowhigh: Towards Efficient And High-quality Audio Super-resolution With Single-step Flow Matching (2025)5.84
• Flashaudio: Rectified Flows For Fast And High-fidelity Text-to-audio Generation (2024)5.13
• STSR: High-fidelity Speech Super-resolution Via Spectral-transient Context Modeling (2025)0.00
• Universr: Unified And Versatile Audio Super-resolution Via Vocoder-free Flow Matching (2025)0.00
• Neural Vocoder Is All You Need For Speech Super-resolution (2022)12.25
• Audio Super-resolution With Latent Bridge Models (2025)0.00
• Edmsound: Spectrogram Based Diffusion Models For Efficient And High-quality Audio Synthesis (2023)0.00
• Score Distillation Sampling For Audio: Source Separation, Synthesis, And Beyond (2025)0.00