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Recent advancements in neural vocoding are predominantly driven by GenerativeAdversarial Networks (GANs) operating in the time-domain. While effective, thisapproach neglects the inductive bias offered by time-frequency representations,resulting in reduntant and computionally-intensive upsampling operations.Fourier-based time-frequency representation is an appealing alternative,aligning more accurately with human auditory perception, and benefitting fromwell-established fast algorithms for its computation. Nevertheless, directreconstruction of complex-valued spectrograms has been historicallyproblematic, primarily due to phase recovery issues. This study seeks to closethis gap by presenting Vocos, a new model that directly generates Fourierspectral coefficients. Vocos not only matches the state-of-the-art in audioquality, as demonstrated in our evaluations, but it also substantially improvescomputational efficiency, achieving an order of magnitude increase in speedcompared to prevailing time-domain neural vocoding approaches. The source codeand model weights have been open-sourced athttps://github.com/charactr-platform/vocos.

Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis