# CoMoSVC: Consistency Model-based Singing Voice Conversion

arXiv: https://arxiv.org/abs/2401.01792 · HTML: https://mlsanity.com/paper/2401.01792/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 13 of 13)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus](https://mlsanity.com/paper/2112.10358/) (2021)
- [A Wavenet for Speech Denoising](https://mlsanity.com/paper/1706.07162/) (2017)
- [CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model](https://mlsanity.com/paper/2305.06908/) (2023)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [Unsupervised Cross-Domain Singing Voice Conversion](https://mlsanity.com/paper/2008.02830/) (2020)
- [FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation](https://mlsanity.com/paper/2011.05731/) (2020)
- [PitchNet: Unsupervised Singing Voice Conversion with Pitch Adversarial Network](https://mlsanity.com/paper/1912.01852/) (2019)
- [Unsupervised Singing Voice Conversion](https://mlsanity.com/paper/1904.06590/) (2019)

## Built on by (most cited first, 11 of 11)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [FlashSpeech: Efficient Zero-Shot Speech Synthesis](https://mlsanity.com/paper/2404.14700/) (2024)
- [Efficient Text-driven Motion Generation via Latent Consistency Training](https://mlsanity.com/paper/2405.02791/) (2024)
- [SPA-SVC: Self-supervised Pitch Augmentation for Singing Voice Conversion](https://mlsanity.com/paper/2406.05692/) (2024)
- [SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion](https://mlsanity.com/paper/2402.12660/) (2024)
- [LCM-SVC: Latent Diffusion Model Based Singing Voice Conversion with Inference Acceleration via Latent Consistency Distillation](https://mlsanity.com/paper/2408.12354/) (2024)
- [LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling](https://mlsanity.com/paper/2409.08583/) (2024)
- [HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios](https://mlsanity.com/paper/2511.08496/) (2025)
- [USM-VC: Mitigating Timbre Leakage with Universal Semantic Mapping Residual Block for Voice Conversion](https://mlsanity.com/paper/2504.08524/) (2025)
- [Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation](https://mlsanity.com/paper/2606.07015/) (2026)
- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)

## Abstract (arXiv)

The diffusion-based Singing Voice Conversion (SVC) methods have achieved remarkable performances, producing natural audios with high similarity to the target timbre. However, the iterative sampling process results in slow inference speed, and acceleration thus becomes crucial. In this paper, we propose CoMoSVC, a consistency model-based SVC method, which aims to achieve both high-quality generation and high-speed sampling. A diffusion-based teacher model is first specially designed for SVC, and a student model is further distilled under self-consistency properties to achieve one-step sampling. Experiments on a single NVIDIA GTX4090 GPU reveal that although CoMoSVC has a significantly faster inference speed than the state-of-the-art (SOTA) diffusion-based SVC system, it still achieves comparable or superior conversion performance based on both subjective and objective metrics. Audio samples and codes are available at https://comosvc.github.io/.