![]() Losses constraining the generated audio to roughly match the ground truth in It learns to produce highįidelity audio through a combination of adversarial feedback and prediction ![]() Thus efficient for both training and inference, using a differentiableĪlignment scheme based on token length prediction. Our proposed generator is feed-forward and Models which operate directly on character or phoneme input sequences and Speech from normalised text or phonemes in an end-to-end manner, resulting in In this work, we take on the challenging task of learning to synthesise Processing stages, each of which is designed or learnt independently from the ![]() Authors: Jeff Donahue, Sander Dieleman, Mikołaj Bińkowski, Erich Elsen, Karen Simonyan Download PDF Abstract: Modern text-to-speech synthesis pipelines typically involve multiple
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