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An automatic pitch correction system typically includes several stages, suchas pitch extraction, deviation estimation, pitch shift processing, andcross-fade smoothing. However, designing these components with strategies oftenrequires domain expertise and they are likely to fail on corner cases. In thispaper, we present KaraTuner, an end-to-end neural architecture that predictspitch curve and resynthesizes the singing voice directly from the tuned pitchand vocal spectrum extracted from the original recordings. Several vitaltechnical points have been introduced in KaraTuner to ensure pitch accuracy,pitch naturalness, timbre consistency, and sound quality. A feed-forwardTransformer is employed in the pitch predictor to capture longterm dependenciesin the vocal spectrum and musical note. We also develop a pitch-controllablevocoder based on a novel source-filter block and the Fre-GAN architecture.KaraTuner obtains a higher preference than the rule-based pitch correctionapproach through A/B tests, and perceptual experiments show that the proposedvocoder achieves significant advantages in timbre consistency and sound qualitycompared with the parametric WORLD vocoder, phase vocoder and CLPC vocoder.

KaraTuner: Towards end to end natural pitch correction for singing voice in karaoke