RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction
Peng Liu Dongyang Dai Zhiyong Wu
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 acoustic 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 straight 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 160 times faster than real-time on a GPU. Both an online demonstration and the source code are accessible.
Rectified Flow, Audio Waveform Reconstruction, Multi-band audio generation，Real-time diffusion  Vocoder
RFWave, a frame-level multi-band Rectified Flow model, achieves high-fidelity audio waveform reconstruction from Mel-spectrograms or discrete tokens, with generation speeds up to 160 times faster than real-time on a GPU.