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Traditional voice conversion methods rely on parallel recordings of multiplespeakers pronouncing the same sentences. For real-world applications however,parallel data is rarely available. We propose MelGAN-VC, a voice conversionmethod that relies on non-parallel speech data and is able to convert audiosignals of arbitrary length from a source voice to a target voice. We firstlycompute spectrograms from waveform data and then perform a domain translationusing a Generative Adversarial Network (GAN) architecture. An additionalsiamese network helps preserving speech information in the translation process,without sacrificing the ability to flexibly model the style of the targetspeaker. We test our framework with a dataset of clean speech recordings, aswell as with a collection of noisy real-world speech examples. Finally, weapply the same method to perform music style transfer, translating arbitrarilylong music samples from one genre to another, and showing that our framework isflexible and can be used for audio manipulation applications different fromvoice conversion.

MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms