Title: WaveFlow: A Compact Flow-based Model for Raw Audio. Abstract: In this work, we present WaveFlow, a small-footprint generative flow for raw audio, which is trained with maximum likelihood without complicated density distillation and auxiliary losses as used in Parallel WaveNet.  It provides a unified view of flow-based models for raw audio, including autoregressive flow (e.g., WaveNet) and bipartite flow (e.g., WaveGlow) as special cases. We systematically study these likelihood-based generative models for raw waveforms in terms of test likelihood and speech fidelity. We demonstrate that WaveFlow can synthesize high-fidelity speech and obtain comparable likelihood as WaveNet, while only requiring a few sequential steps to generate very long waveforms.  In particular, our small-footprint WaveFlow has only 5.91M parameters and can generate 22.05kHz speech 15.39 times faster than real-time on a GPU without customized inference kernels.