  Recent advancements in generative modeling have significantly enhanced the
reconstruction of audio waveforms from various representations. While diffusion
models are adept at this task, they are hindered by latency issues due to their
operation at the individual sample point level and the need for numerous
sampling steps. In this study, we introduce RFWave, a cutting-edge multi-band
Rectified Flow approach designed to reconstruct high-fidelity audio waveforms
from Mel-spectrograms or discrete tokens. RFWave uniquely generates complex
spectrograms and operates at the frame level, processing all subbands
simultaneously to boost efficiency. Leveraging Rectified Flow, which targets a
flat transport trajectory, RFWave achieves reconstruction with just 10 sampling
steps. Our empirical evaluations show that RFWave not only provides outstanding
reconstruction quality but also offers vastly superior computational
efficiency, enabling audio generation at speeds up to 97 times faster than
real-time on a GPU. An online demonstration is available at:
https://rfwave-demo.github.io/rfwave/.
