HQ SVC
Official Repository of Paper: "Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios"(AAAI 2026)
Official Repository of Paper: "Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios"(AAAI 2026) The project is written primarily in Python, first published in 2025. Key topics include: ddsp, deep-learning, diffusion, singing-voice-conversion, singing-voice-synthesis.
HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios (HQ-SVC: 低资源场景下的零样本高质量歌声转换方法)
Official Repository of Paper: "Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios"(AAAI 2026)
<div align="center"> <p> <img src="images/kon-new.gif" alt="HQ-SVC Logo" width="300"> </p> <a href="https://arxiv.org/abs/2511.08496"><img src="https://img.shields.io/badge/arXiv-2511.08496-b31b1b.svg?logo=arxiv&logoColor=white" alt="arXiv"></a> <a href="https://shawnpi233.github.io/HQ-SVC-demo"><img src="https://img.shields.io/badge/Demos-🌐-blue" alt="Demos"></a> <a href="https://huggingface.co/shawnpi/HQ-SVC"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Models%20-%20Access-orange" alt="Models Access"></a> <a href="https://github.com/ShawnPi233/HQ-SVC" target="_blank" rel="noopener noreferrer"> <img src="https://img.shields.io/badge/GitHub-Repository-blue?logo=github" alt="GitHub Repository"></a> </div>HQ-SVC is an efficient framework for high-quality zero-shot singing voice conversion (SVC) in low-resource scenarios. It achieves disentanglement of content and speaker features via a unified decoupled codec, and enhances synthesis quality through multi-feature fusion and progressive optimization.
Unlike existing methods that demand large datasets or heavy computational resources, HQ-SVC unifies:
- 🚀 Zero-shot conversion for unseen speakers without fine-tuning
- ⚡ Low-resource training (single consumer-grade GPU, <80h data)
- 🎧 Dual capabilities: high-quality singing voice conversion + voice super-resolution
- 🎯 Superior naturalness and speaker similarity compared to SOTA methods
🗞 News
- [2025-11-08] 🎉 Paper accepted by AAAI 2026
- [2025-11-12] 🎉 arXiv paper released
- [2025-11-12] 🎉 Demo released
- [2025-12-24] 🎉 Inference codes and pre-trained models released
📅 Release Plan
- arXiv preprint
- Online demo
- Inference codes
- Pre-trained models
- Training codes
✨ New features
- Singing style control
- Improved quality
🎸 Try Inference
1. Download Codes and Environment(下载代码和环境)
-
Tested only on Linux platforms with CUDA >= 11.8 (仅在 Linux 平台、CUDA >= 11.8 的环境上测试通过)
-
Windows users can use WSL (Ubuntu) for deployment and execution (Windows 用户可以使用 WSL (Ubuntu) 进行部署运行)
bashgit clone https://github.com/ShawnPi233/HQ-SVC.git cd HQ-SVC
bashwget -c https://huggingface.co/shawnpi/HQ-SVC/resolve/main/environment.tar.gz
bashwget -c https://hf-mirror.com/shawnpi/HQ-SVC/resolve/main/environment.tar.gz # Optional mirror
2. Unzip Environment(解压环境)
bashmkdir -p venv tar -xzf environment.tar.gz -C venv
3. Activate Environment(激活环境)
bashsource venv/bin/activate
4. Running(运行)
bashexport HF_ENDPOINT=https://hf-mirror.com # Optional mirror python gradio_app.py
- If you encounter the error
Caught signal 11 (Segmentation fault: address not mapped to object at address (nil))(如果报错Caught signal 11 (Segmentation fault: address not mapped to object at address (nil))) - Please execute the following code before running the above code (请执行以下代码后再启动上述代码)
bashunset LD_LIBRARY_PATH
Access at http://127.0.0.1:7860/
<div align="center"> <img src="images/sr.png" alt="sr" width="500">Zero-shot Super-Resolution (16 kHz to 44.1 kHz): Input only source audio
Zero-shot Singing Voice Conversion: Input both source audio and target audio
📜 Citation
If you use HQ-SVC in your research, please cite our work:
bibtex@inproceedings{bai2026hqsvc, author = {Bingsong Bai and Yizhong Geng and Fengping Wang and Cong Wang and Puyuan Guo and Yingming Gao and Ya Li}, title = {HQ-SVC: Towards High-Quality Zero-Shot Singing Voice Conversion in Low-Resource Scenarios}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {40}, number = {36}, pages = {30013--30021}, year = {2026}, doi = {10.1609/aaai.v40i36.40249}, url = {https://doi.org/10.1609/aaai.v40i36.40249} }
🙏 Acknowledgement
We thank the open-source communities behind:
⭐️ Star History
Contributors
Showing top 1 contributor by commit count.
