---
license: gpl-3.0
datasets:
- ai4bharat/IndicVoices
language:
- hi
- bn
- ta
- te
- ml
- kn
- gu
- mr
- or
- pa
- as
- en
base_model:
- Plachta/Seed-VC
pipeline_tag: audio-to-audio
tags:
- voice-conversion
- Voice-Changer
- Voice
---

# IndicVoiceChanger

**IndicVoiceChanger** is a finetuned version of the [Seed Voice Conversion](https://huggingface.co/Plachta/Seed-VC/tree/main) model, adapted for Indian languages.  
It enables high-quality voice conversion across multiple Indian languages, preserving speaker identity while changing the voice characteristics.

## Overview
This model is built upon the Seed Voice Conversion checkpoints and finetuned with a mix of publicly available open-source datasets and our own proprietary dataset.  
It is designed to work well on speech data from diverse Indian languages, accents, and speaking styles.

## Try It Out

Experience the model firsthand at: **[Hugging Face Spaces Demo](https://huggingface.co/spaces/DreamSyncCo/IndicVoiceChanger)**

## Fine-tuning on Custom Data

While the zero shot performance of this model is usually good, it can be further improved with fine-tuning. This model supports efficient fine-tuning on your custom speakers with remarkable data efficiency and speed:

- **Minimal Data Requirements**: Train on new speakers with as little as **1 utterance per speaker**
- **Ultra-Fast Training**: Achieve good results in just **100 training steps** (approximately **2 minutes on T4 GPU**)
- **Speaker Adaptation**: Significantly improve performance on specific target speakers through personalized fine-tuning

### Getting Started with Fine-tuning

For detailed instructions on installation, usage, and fine-tuning, please refer to the comprehensive guide at: **https://github.com/Plachtaa/seed-vc**

**Note**: We will be updating this repository with example notebooks demonstrating the fine-tuning process soon.

**Important**: When fine-tuning for Indian languages, make sure to use the checkpoints provided in this repository as your starting point for optimal performance.

## Acknowledgments

Special thanks to the [SeedVC](https://github.com/Plachtaa/seed-vc) project for providing the foundational architecture and training framework that made this Indian language adaptation possible.