HifiGAN is a neural vocoder model for text-to-speech applications. It is intended as the second part of a two-stage speech synthesis pipeline, with a mel-spectrogram generator such as RADTTS as the first stage.
HifiGAN is a neural vocoder based on a generative adversarial network framework, During training, the model uses a powerful discriminator consisting of small sub-discriminators, each one focusing on specific periodic parts of a raw waveform. The generator is very fast and has a small footprint, while producing high quality speech.
This model is trained on a proprietary dataset sampled at 44100Hz, and can be used to generate English voices with an American accent. This model supports 1 male voice and 1 female voice that comes from the Riva RADTTS model.
HifiGAN is intended to be used as the second part of a two stage speech synthesis pipeline. HifiGAN takes a mel spectrogram and returns audio.
HifiGAN paper: https://arxiv.org/abs/2010.05646
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