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--- |
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license: gpl-3.0 |
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datasets: |
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- ai4bharat/IndicVoices |
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language: |
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- hi |
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- bn |
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- ta |
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- te |
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- ml |
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- kn |
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- gu |
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- mr |
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- or |
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- pa |
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- as |
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- en |
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base_model: |
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- Plachta/Seed-VC |
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pipeline_tag: audio-to-audio |
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tags: |
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- voice-conversion |
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- Voice-Changer |
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- Voice |
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--- |
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# IndicVoiceChanger |
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**IndicVoiceChanger** is a finetuned version of the [Seed Voice Conversion](https://huggingface.co/Plachta/Seed-VC/tree/main) model, adapted for Indian languages. |
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It enables high-quality voice conversion across multiple Indian languages, preserving speaker identity while changing the voice characteristics. |
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## Overview |
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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. |
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It is designed to work well on speech data from diverse Indian languages, accents, and speaking styles. |
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## Try It Out |
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Experience the model firsthand at: **[Hugging Face Spaces Demo](https://huggingface.co/spaces/DreamSyncCo/IndicVoiceChanger)** |
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## Fine-tuning on Custom Data |
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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: |
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- **Minimal Data Requirements**: Train on new speakers with as little as **1 utterance per speaker** |
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- **Ultra-Fast Training**: Achieve good results in just **100 training steps** (approximately **2 minutes on T4 GPU**) |
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- **Speaker Adaptation**: Significantly improve performance on specific target speakers through personalized fine-tuning |
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### Getting Started with Fine-tuning |
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For detailed instructions on installation, usage, and fine-tuning, please refer to the comprehensive guide at: **https://github.com/Plachtaa/seed-vc** |
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**Note**: We will be updating this repository with example notebooks demonstrating the fine-tuning process soon. |
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**Important**: When fine-tuning for Indian languages, make sure to use the checkpoints provided in this repository as your starting point for optimal performance. |
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## Acknowledgments |
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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. |
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