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Several recent work on speech synthesis have employed generative adversarialnetworks (GANs) to produce raw waveforms. Although such methods improve thesampling efficiency and memory usage, their sample quality has not yet reachedthat of autoregressive and flow-based generative models. In this work, wepropose HiFi-GAN, which achieves both efficient and high-fidelity speechsynthesis. As speech audio consists of sinusoidal signals with various periods,we demonstrate that modeling periodic patterns of an audio is crucial forenhancing sample quality. A subjective human evaluation (mean opinion score,MOS) of a single speaker dataset indicates that our proposed methoddemonstrates similarity to human quality while generating 22.05 kHzhigh-fidelity audio 167.9 times faster than real-time on a single V100 GPU. Wefurther show the generality of HiFi-GAN to the mel-spectrogram inversion ofunseen speakers and end-to-end speech synthesis. Finally, a small footprintversion of HiFi-GAN generates samples 13.4 times faster than real-time on CPUwith comparable quality to an autoregressive counterpart.

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis