  In this work, we propose WaveFlow, a small-footprint generative flow for raw
audio, which is directly trained with maximum likelihood. It handles the
long-range structure of 1-D waveform with a dilated 2-D convolutional
architecture, while modeling the local variations using expressive
autoregressive functions. WaveFlow provides a unified view of likelihood-based
models for 1-D data, including WaveNet and WaveGlow as special cases. It
generates high-fidelity speech as WaveNet, while synthesizing several orders of
magnitude faster as it only requires a few sequential steps to generate very
long waveforms with hundreds of thousands of time-steps. Furthermore, it can
significantly reduce the likelihood gap that has existed between autoregressive
models and flow-based models for efficient synthesis. Finally, our
small-footprint WaveFlow has only 5.91M parameters, which is 15$\times$ smaller
than WaveGlow. It can generate 22.05 kHz high-fidelity audio 42.6$\times$
faster than real-time (at a rate of 939.3 kHz) on a V100 GPU without engineered
inference kernels.
