  Recent studies have shown that neural vocoders based on generative
adversarial network (GAN) can generate audios with high quality. While GAN
based neural vocoders have shown to be computationally much more efficient than
those based on autoregressive predictions, the real-time generation of the
highest quality audio on CPU is still a very challenging task. One major
computation of all GAN-based neural vocoders comes from the stacked upsampling
layers, which were designed to match the length of the waveform's length of
output and temporal resolution. Meanwhile, the computational complexity of
upsampling networks is closely correlated with the numbers of samples generated
for each window. To reduce the computation of upsampling layers, we propose a
new GAN based neural vocoder called Basis-MelGAN where the raw audio samples
are decomposed with a learned basis and their associated weights. As the
prediction targets of Basis-MelGAN are the weight values associated with each
learned basis instead of the raw audio samples, the upsampling layers in
Basis-MelGAN can be designed with much simpler networks. Compared with other
GAN based neural vocoders, the proposed Basis-MelGAN could produce comparable
high-quality audio but significantly reduced computational complexity from
HiFi-GAN V1's 17.74 GFLOPs to 7.95 GFLOPs.
