  In this paper, we propose WG-WaveNet, a fast, lightweight, and high-quality
waveform generation model. WG-WaveNet is composed of a compact flow-based model
and a post-filter. The two components are jointly trained by maximizing the
likelihood of the training data and optimizing loss functions on the frequency
domains. As we design a flow-based model that is heavily compressed, the
proposed model requires much less computational resources compared to other
waveform generation models during both training and inference time; even though
the model is highly compressed, the post-filter maintains the quality of
generated waveform. Our PyTorch implementation can be trained using less than 8
GB GPU memory and generates audio samples at a rate of more than 960 kHz on an
NVIDIA 1080Ti GPU. Furthermore, even if synthesizing on a CPU, we show that the
proposed method is capable of generating 44.1 kHz speech waveform 1.2 times
faster than real-time. Experiments also show that the quality of generated
audio is comparable to those of other methods. Audio samples are publicly
available online.
