  An automatic pitch correction system typically includes several stages, such
as pitch extraction, deviation estimation, pitch shift processing, and
cross-fade smoothing. However, designing these components with strategies often
requires domain expertise and they are likely to fail on corner cases. In this
paper, we present KaraTuner, an end-to-end neural architecture that predicts
pitch curve and resynthesizes the singing voice directly from the tuned pitch
and vocal spectrum extracted from the original recordings. Several vital
technical points have been introduced in KaraTuner to ensure pitch accuracy,
pitch naturalness, timbre consistency, and sound quality. A feed-forward
Transformer is employed in the pitch predictor to capture longterm dependencies
in the vocal spectrum and musical note. We also develop a pitch-controllable
vocoder based on a novel source-filter block and the Fre-GAN architecture.
KaraTuner obtains a higher preference than the rule-based pitch correction
approach through A/B tests, and perceptual experiments show that the proposed
vocoder achieves significant advantages in timbre consistency and sound quality
compared with the parametric WORLD vocoder, phase vocoder and CLPC vocoder.
