| --- |
| license: cc-by-nc-4.0 |
| pipeline_tag: audio-to-audio |
| library_name: f5-tts |
| extra_gated_prompt: "You agree to not use the model to generate, share, or promote content that is illegal, harmful, deceptive, or intended to impersonate real individuals without their informed consent." |
| extra_gated_fields: |
| Affiliation: text |
| Country: country |
| I agree to use this model for non-commercial use ONLY: checkbox |
| --- |
| |
| # EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion |
|
|
| [](https://github.com/EZ-VC/EZ-VC) |
| [](https://aclanthology.org/2025.findings-emnlp.1077/) |
| [](https://ez-vc.github.io/EZ-VC-Demo/) |
| [](https://asr.iitm.ac.in/) |
| <!-- <img src="https://github.com/user-attachments/assets/12d7749c-071a-427c-81bf-b87b91def670" alt="Watermark" style="width: 40px; height: auto"> --> |
|
|
|
|
| ### Our paper has been published in the Findings of EMNLP 2025! |
|
|
| ## Installation |
|
|
| ### Create a separate environment if needed |
|
|
| ```bash |
| # Create a python 3.10 conda env (you could also use virtualenv) |
| conda create -n ez-vc python=3.10 |
| conda activate ez-vc |
| ``` |
|
|
| ### Local installation |
|
|
| ```bash |
| git clone https://github.com/EZ-VC/EZ-VC |
| cd EZ-VC |
| git submodule update --init --recursive |
| pip install -e . |
| |
| # Install espnet for xeus (Exactly this version) |
| pip install 'espnet @ git+https://github.com/wanchichen/espnet.git@ssl' |
| ``` |
|
|
| ## Inference |
|
|
| We have provided a Jupyter notebook for inference in "src/f5_tts/infer/infer.ipynb". |
| |
| Open [Inference notebook](https://github.com/EZ-VC/EZ-VC/blob/main/src/f5_tts/infer/infer.ipynb). |
| |
| Run all. |
| |
| The converted audio will be available at the last cell. |
| |
| |
| ## Acknowledgements |
| |
| - [F5-TTS](https://arxiv.org/abs/2410.06885) for opensourcing their code which has made EZ-VC possible. |
| |
| ## Citation |
| If our work and codebase is useful for you, please cite as: |
| ``` |
| @inproceedings{joglekar-etal-2025-ez, |
| title = "{EZ}-{VC}: Easy Zero-shot Any-to-Any Voice Conversion", |
| author = "Joglekar, Advait and |
| Singh, Divyanshu and |
| Bhatia, Rooshil Rohit and |
| Umesh, Srinivasan", |
| editor = "Christodoulopoulos, Christos and |
| Chakraborty, Tanmoy and |
| Rose, Carolyn and |
| Peng, Violet", |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025", |
| month = nov, |
| year = "2025", |
| address = "Suzhou, China", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2025.findings-emnlp.1077/", |
| doi = "10.18653/v1/2025.findings-emnlp.1077", |
| pages = "19768--19774", |
| ISBN = "979-8-89176-335-7", |
| abstract = "Voice Conversion research in recent times has increasingly focused on improving the zero-shot capabilities of existing methods. Despite remarkable advancements, current architectures still tend to struggle in zero-shot cross-lingual settings. They are also often unable to generalize for speakers of unseen languages and accents. In this paper, we adopt a simple yet effective approach that combines discrete speech representations from self-supervised models with a non-autoregressive Diffusion-Transformer based conditional flow matching speech decoder. We show that this architecture allows us to train a voice-conversion model in a purely textless, self-supervised fashion. Our technique works without requiring multiple encoders to disentangle speech features. Our model also manages to excel in zero-shot cross-lingual settings even for unseen languages. We provide our code, model checkpoint and demo samples here: https://github.com/ez-vc/ez-vc" |
| } |
| ``` |
| ## License |
| |
| Our code is released under MIT License. The pre-trained models are licensed under the CC-BY-NC license. Sorry for any inconvenience this may cause. |
