  In this paper, we propose multi-band MelGAN, a much faster waveform
generation model targeting to high-quality text-to-speech. Specifically, we
improve the original MelGAN by the following aspects. First, we increase the
receptive field of the generator, which is proven to be beneficial to speech
generation. Second, we substitute the feature matching loss with the
multi-resolution STFT loss to better measure the difference between fake and
real speech. Together with pre-training, this improvement leads to both better
quality and better training stability. More importantly, we extend MelGAN with
multi-band processing: the generator takes mel-spectrograms as input and
produces sub-band signals which are subsequently summed back to full-band
signals as discriminator input. The proposed multi-band MelGAN has achieved
high MOS of 4.34 and 4.22 in waveform generation and TTS, respectively. With
only 1.91M parameters, our model effectively reduces the total computational
complexity of the original MelGAN from 5.85 to 0.95 GFLOPS. Our Pytorch
implementation, which will be open-resourced shortly, can achieve a real-time
factor of 0.03 on CPU without hardware specific optimization.
