HooliGAN: Robust, High Quality Neural Vocoding

By Ollie McCarthy, Zo Ahmed. 2020

14 citations recorded by the source corpus.

Recent developments in generative models have shown that deep learning combined with traditional digital signal processing (DSP) techniques could successfully generate convincing violin samples [1], that source-excitation combined with WaveNet yields high-quality vocoders [2, 3] and that generative adversarial network (GAN) training can improve naturalness [4, 5]. By combining the ideas in these models we introduce HooliGAN, a robust vocoder that has state of the art results, finetunes very well to smaller datasets (<30 minutes of speechdata) and generates audio at 2.2MHz on GPU and 35kHz on CPU. We also show a simple modification to Tacotron-basedmodels that allows seamless integration with HooliGAN. Results from our listening tests show the proposed model's ability to consistently output high-quality audio with a variety of datasets, big and small. We provide samples at the following demo page: this https URL

Subfields: Computer Vision and Pattern Recognition, Natural Language Processing, Signal Processing

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Citations in the corpus

WaveGrad: Estimating Gradients for Waveform Generation (2020, background): “…network (GAN) (Donahue et al., 2018; Engel et al., 2019; Kumar et al., 2019; Yamamoto et al., 2020; Bikowski et al., 2020; Yang et al., 2020; McCarthy & Ahmed, 2020) and energy score (Gritsenko et al., 2020), variational models like variational auto-encoder (VAE) (Peng et al., 2020),…”