# StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching

arXiv: https://arxiv.org/abs/2412.04724 · HTML: https://mlsanity.com/paper/2412.04724/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone](https://mlsanity.com/paper/2112.02418/) (2021)
- [NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers](https://mlsanity.com/paper/2304.09116/) (2023)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme](https://mlsanity.com/paper/2109.13821/) (2021)
- [VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion](https://mlsanity.com/paper/2106.10132/) (2021)
- [Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning](https://mlsanity.com/paper/2103.09420/) (2021)
- [DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors](https://mlsanity.com/paper/2406.11427/) (2024)
- [VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing](https://mlsanity.com/paper/2404.06674/) (2024)
- [DualVC: Dual-mode Voice Conversion using Intra-model Knowledge Distillation and Hybrid Predictive Coding](https://mlsanity.com/paper/2305.12425/) (2023)
- [SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs](https://mlsanity.com/paper/2307.09435/) (2023)
- [Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling](https://mlsanity.com/paper/2410.01350/) (2024)

## Built on by (most cited first, 14 of 14)

- [Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversion](https://mlsanity.com/paper/2505.24291/) (2025)
- [ClapFM-EVC: High-Fidelity and Flexible Emotional Voice Conversion with Dual Control from Natural Language and Speech](https://mlsanity.com/paper/2505.13805/) (2025)
- [Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis](https://mlsanity.com/paper/2509.24531/) (2025)
- [DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching](https://mlsanity.com/paper/2508.05978/) (2025)
- [LatentVoiceGrad: Nonparallel Voice Conversion with Latent Diffusion/Flow-Matching Models](https://mlsanity.com/paper/2509.08379/) (2025)
- [Provable Speech Attributes Conversion via Latent Independence](https://mlsanity.com/paper/2510.05191/) (2025)
- [DeepASMR: LLM-Based Zero-Shot ASMR Speech Generation for Anyone of Any Voice](https://mlsanity.com/paper/2601.15596/) (2026)
- [Unifying Speech Editing Detection and Content Localization via Prior-Enhanced Audio LLMs](https://mlsanity.com/paper/2601.21463/) (2026)
- [CoSyncDiT: Cognitive Synchronous Diffusion Transformer for Movie Dubbing](https://mlsanity.com/paper/2604.12292/) (2026)
- [DiFlow-TTS: Compact and Low-Latency Zero-Shot Text-to-Speech with Discrete Flow Matching](https://mlsanity.com/paper/2509.09631/) (2025)
- [Insight-A: Attribution-aware for Multimodal Misinformation Detection](https://mlsanity.com/paper/2511.21705/) (2025)
- [Conditioned in Training, Dispensable at Inference: Typological Conditioning for Multilingual Speech Translation](https://mlsanity.com/paper/2605.16026/) (2026)
- [Enhancing Flow Matching with A Unified Guidance Framework for Efficient and Robust Speech Synthesis](https://mlsanity.com/paper/2607.00363/) (2026)
- [MeanVoiceFlow2: Joint Optimization of Mean Flow and Content Encoder for Fast One-Step Zero-Shot Voice Conversion](https://mlsanity.com/paper/2609.40087/) (2026)

## Abstract (arXiv)

Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content. Despite recent advancements in zero-shot VC using language model-based or diffusion-based approaches, several challenges remain: 1) current approaches primarily focus on adapting timbre from unseen speakers and are unable to transfer style and timbre to different unseen speakers independently; 2) these approaches often suffer from slower inference speeds due to the autoregressive modeling methods or the need for numerous sampling steps; 3) the quality and similarity of the converted samples are still not fully satisfactory. To address these challenges, we propose a style controllable zero-shot VC approach named StableVC, which aims to transfer timbre and style from source speech to different unseen target speakers. Specifically, we decompose speech into linguistic content, timbre, and style, and then employ a conditional flow matching module to reconstruct the high-quality mel-spectrogram based on these decomposed features. To effectively capture timbre and style in a zero-shot manner, we introduce a novel dual attention mechanism with an adaptive gate, rather than using conventional feature concatenation. With this non-autoregressive design, StableVC can efficiently capture the intricate timbre and style from different unseen speakers and generate high-quality speech significantly faster than real-time. Experiments demonstrate that our proposed StableVC outperforms state-of-the-art baseline systems in zero-shot VC and achieves flexible control over timbre and style from different unseen speakers. Moreover, StableVC offers approximately 25x and 1.65x faster sampling compared to autoregressive and diffusion-based baselines.