HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial
  Network for High-Fidelity Speech Super-Resolution
Authors: name: Shengkui Zhao, name: Kun Zhou, name: Zexu Pan, name: Yukun Ma, name: Chong Zhang, name: Bin Ma

The application of generative adversarial networks (GANs) has recently
advanced speech super-resolution (SR) based on intermediate representations
like mel-spectrograms. However, existing SR methods that typically rely on
independently trained and concatenated networks may lead to inconsistent
representations and poor speech quality, especially in out-of-domain scenarios.
In this work, we propose HiFi-SR, a unified network that leverages end-to-end
adversarial training to achieve high-fidelity speech super-resolution. Our
model features a unified transformer-convolutional generator designed to
seamlessly handle both the prediction of latent representations and their
conversion into time-domain waveforms. The transformer network serves as a
powerful encoder, converting low-resolution mel-spectrograms into latent space
representations, while the convolutional network upscales these representations
into high-resolution waveforms. To enhance high-frequency fidelity, we
incorporate a multi-band, multi-scale time-frequency discriminator, along with
a multi-scale mel-reconstruction loss in the adversarial training process.
HiFi-SR is versatile, capable of upscaling any input speech signal between 4
kHz and 32 kHz to a 48 kHz sampling rate. Experimental results demonstrate that
HiFi-SR significantly outperforms existing speech SR methods across both
objective metrics and ABX preference tests, for both in-domain and
out-of-domain scenarios (https://github.com/modelscope/ClearerVoice-Studio).