  Real-world audio recordings are often degraded by factors such as noise,
reverberation, and equalization distortion. This paper introduces HiFi-GAN, a
deep learning method to transform recorded speech to sound as though it had
been recorded in a studio. We use an end-to-end feed-forward WaveNet
architecture, trained with multi-scale adversarial discriminators in both the
time domain and the time-frequency domain. It relies on the deep feature
matching losses of the discriminators to improve the perceptual quality of
enhanced speech. The proposed model generalizes well to new speakers, new
speech content, and new environments. It significantly outperforms
state-of-the-art baseline methods in both objective and subjective experiments.
