RESEARCH
Kits Voice Conversion (KVC)
KVC: Studio-Quality Singing Voice Conversion
Kits.AI is the world’s leading platform for professional AI singing voice conversion. Millions of music producers and vocalists rely on Kits for studio-quality AI vocals that capture the natural intonation, dynamics, and nuance of the human voice.
The research team at Kits.AI has designed Kits Voice Conversion (KVC), an industry-leading voice-to-voice conversion system that pushes the boundaries of quality in voice-to-voice conversion technology.
This page is an overview of the growing list of innovations within KVC — improved architecture, robust pre-trained weights, and optimized infrastructure — that make it the top choice for industry professionals worldwide.
KVC Architecture: Optimized for Singing
KVC has made architectural improvements optimizing specifically for professional quality singing outputs. This section outlines the architectural improvements that enables KVC to outperform open-source SVC systems across a number of dimensions, including pronunciation, pitch accuracy, frequency range, and dynamics.
Kits Base Weights
Kits has curated and hand-processed a proprietary dataset sourced from individual vocalists who are compensated for the rights to train on recordings of their voice. These recordings form the dataset that KVC base weights are trained on. Whenever a voice is cloned with KVC, it draws from the quality of this dataset.
Our training data, data sourcing and data management practices are certified as Fairly Trained. We remain committed to respecting the rights of artists and supporting them financially.
Pitch Detection: Kits Hybrid Pitch
More accurate detection of F0 is critical for the SVC task. The Kits Research team has developed a custom pitch detection algorithm called Kits Hybrid Pitch that outperforms baseline Crepe, RMVPE, and Mangio-Crepe leading to improved results.
Metrics for RMVPE
Metrics for Hybrid
Open Source: RVC with RMVPE
Kits Hybrid
Adaptive Content Retrieval
KVC uses adaptive content feature retrieval smoothing, which leads to higher levels of speaker similarity over standard retrieval SVC systems like RVC. During inference, Kits VC takes input features and applies retrieval strength adaptively: the more aligned the features are, the more the content features are pulled towards retrieval.
This results in a higher preservation of phonemic content leading to improvements in pronunciation and speaker similarity.
Open Source: Contentvec + nearest neighbor retrieval
Kits: Adaptive feature retrieval
Advanced Content Encoding: Xeus, Hybrid
Open-source SVC systems use Hubert or ContentVec weights. KVC is integrated with both ContentVec, as well as advanced content encoders like Xeus and hybrid systems, which can lead to improvements in pronunciation. Examples are included below.
Training Pre-Processing
Intelligent Slicing
KVC uses a more intelligent slicing method to train on longer, more complete phrases, avoiding cutting in the middle of a word or phrase.
Breath and Noise Removal
KVC includes additional steps for adaptive noise removal to enhance quality.
Adaptive EQ for Spectral Balance
KVC includes automatic EQ adjustment in both training and inference, resulting in higher spectral balance and parity between input and output audio.
Inference Post-Processing
Pitch Correction
Automatic pitch correction, inspired by tools like Antares Auto-Tune, is optionally applied during conversion.
Stylistic Effects
Stylistic effects like stereo widening and reverb are built directly into the inference pipeline, improving the stylistic quality of singing outputs.
Audio Examples
Pitch Stability
Where Open Source weights are largely trained on speech data, the base weights of KVC are optimized for singing. The result: fuller, clearer notes across (and even beyond) the range of a singer.
Open Source (RVC)
Kits VC
Vocal Energy
With KVC, the energy level in an input file is reproduced much more realistically than with Open Source alternatives. Volume fluctuations, breathiness, and smooth note onsets result in a much more natural result.
Open Source (RVC)
Kits VC
Volume
Through adaptive pre-processing, KVC addresses volume artifacts common to Open Source RVC conversions.
Open Source (RVC)
Kits VC
Sonic Quality
Without careful EQ and dynamic range processing, a voice model can quickly sound harsh. KVC adaptively balances volume and frequency response of training datasets for smooth, low-distortion conversions.
Open Source (RVC)
Kits VC
Pitch/Vocal Fry
Through improvements to pitch detection, feature retrieval, and temporal resolution, KVC makes improvements to subtle inflections like vocal fry and breathy singing styles.