license: apache-2.0
language:
- en
tags:
- ai
- rvc
- vc
- voice-cloning
- applio
- titan
- pretrained
base_model: lj1995/VoiceConversionWebUI
datasets:
- blaise-tk/TITAN-Medium
pipeline_tag: audio-to-audio
TITAN: A Versatile, Robust, and High-Quality Pretrained Model for Retrieval-based Voice Conversion (RVC) Training
Overview
TITAN is a state-of-the-art pretrained model designed for Retrieval-based Voice Conversion (https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/) training. It offers a robust solution for transforming voice characteristics from one speaker to another, providing high-quality results with minimal training effort.
Model Details
Titan-Medium
- Training Environment: Utilized a RTX 3060 TI on Applio v3.1.1 (https://github.com/IAHispano/Applio), employing a batch size of 8 over a span of 3 weeks.
- Iterations (48k): 1018660 Steps and 530 Epochs
- Iterations (40k): 1010588 Steps and 467 Epochs
- Iterations (32k): 1001469 Steps and 463 Epochs
- Sampling rate: 48k, 40k, 32k
- Fine-tuning Process: RVC v2 pretrained with pitch guidance, leveraging an 11.15-hour dataset sourced from Expresso (https://arxiv.org/abs/2308.05725) also available on datasets/blaise-tk/TITAN-Medium.
Samples
Tests performed with a premature ckpt at ~700k steps doing all tests under the same conditions.
| Titan-Medium | Ov2 | Ov2.1 |
|---|---|---|
Titan-Large
- Details forthcoming...
Collaborators
We appreciate the contributions of our collaborators who have helped in the development and refinement of TITAN.
- Mustar
- SimplCup
- UnitedShoes
Beta Testers
We extend our gratitude to the beta testers who provided valuable feedback during the testing phase of TITAN.
- SimplCup
- Leo_Frixi
- Light
- SCRFilms
- Ryanz
- Litsa_the_dancer
Citation
Should you find TITAN beneficial for your research endeavors or projects, we kindly request citing our repository:
@article{titan,
title={TITAN: A Versatile, Robust, and High-Quality Pretrained Model for Retrieval-based Voice Conversion (RVC) Training},
author={Blaise},
journal={Hugging Face},
year={2024},
publisher={Blaise},
url={https://huggingface.co/blaise-tk/TITAN/}
}