README.md

---
license: cc-by-nc-4.0
configs:
- config_name: Bengali
  data_files:
  - split: train
    path: Bengali/train-*
- config_name: Gujarati
  data_files:
  - split: train
    path: Gujarati/train-*
dataset_info:
- config_name: Bengali
  features:
  - name: audio
    dtype: audio
  - name: Generative Model
    dtype: string
  - name: Source Speaker_ID
    dtype: float64
  - name: Target Speaker ID
    dtype: int64
  - name: Gender
    dtype: string
  - name: Source Reference Audio
    dtype: string
  - name: Target Reference Audio
    dtype: string
  - name: TTS Transcript
    dtype: string
  splits:
  - name: train
    num_bytes: 26636977797.52
    num_examples: 83448
  download_size: 33644253977
  dataset_size: 26636977797.52
- config_name: Gujarati
  features:
  - name: audio
    dtype: audio
  - name: Generative Model
    dtype: string
  - name: Source Speaker_ID
    dtype: float64
  - name: Target Speaker ID
    dtype: int64
  - name: Gender
    dtype: string
  - name: Source Reference Audio
    dtype: string
  - name: Target Reference Audio
    dtype: string
  - name: TTS Transcript
    dtype: string
  splits:
  - name: train
    num_bytes: 35729511328.708
    num_examples: 118778
  download_size: 50811268801
  dataset_size: 35729511328.708
---

# IndicSynth
*A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages*

**🏆 Outstanding Paper Award, ACL 2025**

---

## 🧠 Overview

**IndicSynth** is a novel multilingual synthetic speech dataset designed to advance multilingual **audio deepfake detection (ADD)** and **anti-spoofing** research. It covers **12 low-resource Indian languages** and provides both **mimicry** and **diversity** subsets.

> 4,000+ hours of synthetic audio  
> 12 languages: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, Urdu  
> Useful for ADD, speaker verification (SV), and bias studies  
---
## 📂 Dataset Structure

Each language folder contains:

<pre>  
IndicSynth/
├── Bengali/
│ ├── audio/ # All .wav files (synthetic clips)
│ └── metadata.csv # Metadata for all synthetic clips
├── Gujarati/
│ ├── audio/
│ └── metadata.csv
</pre>
Each 'metadata.csv' includes:
- Generative Model (xtts_v2 / vits / freevc24)
- Speaker IDs
- Gender
- Transcript (if applicable)
- File path to synthetic audio

📝 **Note on Transcripts in Metadata:**
The transcripts included in the metadata.csv files represent the intended text prompts used during synthetic speech generation via TTS models. We provide these transcripts to enable future explorations, but do not guarantee perfect alignment with the generated audio. If you intend to use IndicSynth for speech-to-text or similar tasks, we strongly recommend conducting careful human evaluation with proficient native speakers of the respective languages.

---
## ⚙️ IndicSynth Generation?
Synthetic data was generated using:

| Model      | Type      | Transcript | Fine-Tuned |
|------------|-----------|------------|-------------|
| xtts_v2  | TTS       | Yes        | Yes (for 10 languages) |
| vits     | TTS       | Yes        | No          |
| freevc24 | VC        | No         | No          |

> **Mimicry subset**: For anti-spoofing research
> **Diversity subset**: Contains diverse set of realistic synthetic voices for multilingual audio deepfake detection research
For more details, please see the Table 1 and Section 3 of our paper: https://aclanthology.org/2025.acl-long.1070.pdf
---

## 📦 Access the Dataset

You can load a specific language using:
```python
from datasets import load_dataset

ds = load_dataset("vdivyasharma/IndicSynth", name="Hindi", split="train")
```
## License
IndicSynth is released under the **CC BY-NC 4.0 License**.  
It is intended for **non-commercial, academic research only**.

## Citation
If you use IndicSynth, please cite the following papers:
<pre>@inproceedings{sharma-etal-2025-indicsynth,
    title = "{I}ndic{S}ynth: A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource {I}ndian Languages",
    author = "Sharma, Divya V  and
      Ekbote, Vijval  and
      Gupta, Anubha",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.1070/",
    pages = "22037--22060",
    ISBN = "979-8-89176-251-0"
}
</pre>
<pre>@article{IndicSuperb,
author = {Javed, Tahir and Bhogale, Kaushal and Raman, Abhigyan and Kumar, Pratyush and Kunchukuttan, Anoop and Khapra, Mitesh},
year = {2023},
month = {06},
pages = {12942-12950},
title = {IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages},
volume = {37},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
doi = {10.1609/aaai.v37i11.26521}
}
</pre>
---

## 💬 Contact
For questions or feedback, please feel free to reach out at [email protected].

## 🙏 Acknowledgments
> 🌍 ACL Diversity & Inclusion Subsidy for enabling in-person presentation at ACL 2025
 
> 🤝 HuggingFace for dataset hosting support