  GAN vocoders are currently one of the state-of-the-art methods for building
high-quality neural waveform generative models. However, most of their
architectures require dozens of billion floating-point operations per second
(GFLOPS) to generate speech waveforms in samplewise manner. This makes GAN
vocoders still challenging to run on normal CPUs without accelerators or
parallel computers. In this work, we propose a new architecture for GAN
vocoders that mainly depends on recurrent and fully-connected networks to
directly generate the time domain signal in framewise manner. This results in
considerable reduction of the computational cost and enables very fast
generation on both GPUs and low-complexity CPUs. Experimental results show that
our Framewise WaveGAN vocoder achieves significantly higher quality than
auto-regressive maximum-likelihood vocoders such as LPCNet at a very low
complexity of 1.2 GFLOPS. This makes GAN vocoders more practical on edge and
low-power devices.
