--- 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-* - config_name: Hindi data_files: - split: train path: Hindi/train-* - config_name: Kannada data_files: - split: train path: Kannada/train-* - config_name: Malayalam data_files: - split: train path: Malayalam/train-* - config_name: Marathi data_files: - split: train path: Marathi/train-* - config_name: Odia data_files: - split: train path: Odia/train-* - config_name: Punjabi data_files: - split: train path: Punjabi/train-* - config_name: Sanskrit data_files: - split: train path: Sanskrit/train-* - config_name: Tamil data_files: - split: train path: Tamil/train-* - config_name: Telugu data_files: - split: train path: Telugu/train-* - config_name: Urdu data_files: - split: train path: Urdu/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 - config_name: Hindi 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: 53003219701.92 num_examples: 205938 download_size: 56012925652 dataset_size: 53003219701.92 - config_name: Kannada 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: 45376904249.101 num_examples: 115023 download_size: 68372746210 dataset_size: 45376904249.101 - config_name: Malayalam 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: 15637888779.776 num_examples: 34128 download_size: 24407793288 dataset_size: 15637888779.776 - config_name: Marathi 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: 71754219535.2 num_examples: 130150 download_size: 42561790240 dataset_size: 71754219535.2 - config_name: Odia features: - name: audio dtype: audio - name: Generative Model dtype: string - name: Source Speaker_ID dtype: float64 - name: Target Speaker ID dtype: string - 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: 16945788457.908 num_examples: 52236 download_size: 23671968893 dataset_size: 16945788457.908 - config_name: Punjabi 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: 66672910244.46 num_examples: 248354 download_size: 100577772676 dataset_size: 66672910244.46 - config_name: Sanskrit 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: 197955155096.904 num_examples: 377504 download_size: 167171842242 dataset_size: 197955155096.904 - config_name: Tamil 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: 85162574185.912 num_examples: 282312 download_size: 148502748897 dataset_size: 85162574185.912 - config_name: Telugu 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: 64247424400.08 num_examples: 169896 download_size: 91708786871 dataset_size: 64247424400.08 - config_name: Urdu 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: 23993474813.016 num_examples: 94898 download_size: 37773964000 dataset_size: 23993474813.016 tags: - indian speech - indian languages - synthetic speech - deepfake - audio deepfake detection - indian deepfake detection - anti-spoofing - text-to-speech - tts - voice cloning - voice conversion - vc - add - fake - speech - low-resource languages - multilingual - asv - sv - speaker verification - linguistic bias - gender - bias - generalizable language: - bn - gu - hi - kn - ml - mr - or - pa - sa - ta - te - ur task_categories: - audio-classification - text-to-speech - automatic-speech-recognition pretty_name: IndicSynth --- # IndicSynth: Indian Multilingual Audio Deepfake Detection & Anti-Spoofing Dataset *A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages to facilitate audio deepfake detection and anti-spoofing research* **πŸ† 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: 1. Multilingual audio deepfake detection (ADD) research or mitigating linguistic biases in audio deepfake detection systems 2. Enhancing the robustness of speaker verification (SV) systems against spoofing (impersonation) attacks and developing anti-spoofing solutions 3. Cross-lingual or gender bias studies in speech synthesis and recognition systems --- ## πŸ“‚ Dataset Structure Each language folder contains: 
  
IndicSynth/
β”œβ”€β”€ Bengali/
β”‚ β”œβ”€β”€ audio/ # All .wav files (synthetic clips)
β”‚ └── metadata.csv # Metadata for all synthetic clips
β”œβ”€β”€ Gujarati/
β”‚ β”œβ”€β”€ audio/
β”‚ └── metadata.csv
Each 'metadata.csv' includes: - Generative Model (xtts_v2 / vits / freevc24) - Speaker IDs - Gender - Transcript (if applicable) - File path to synthetic audio **Bona fide audios**: The bona fide source and target speech samples referenced in IndicSynth metadata are drawn from the IndicSUPERB dataset. Please refer to the official repository for details: https://github.com/AI4Bharat/indicSUPERB. πŸ“ **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. --- ## βš™οΈ 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 --- ## πŸ“¦ Access the Dataset You can load data in a specific target language using the following code: ```python import os import soundfile as sf from datasets import load_dataset from tqdm import tqdm import pandas as pd language = "Hindi" # Specify the target language here # Load Dataset dataset = load_dataset("vdivyasharma/IndicSynth", name=language, split="train") # Create target directory structure output_dir = language audio_dir = os.path.join(output_dir, "audio") os.makedirs(audio_dir, exist_ok=True) # Store metadata rows here metadata_rows = [] # Loop through dataset and save each clip for example in tqdm(dataset): audio_array = example["audio"]["array"] sampling_rate = example["audio"]["sampling_rate"] # Get filename original_name = example.get("file") or example.get("path") or example["audio"]["path"].split("/")[-1] # Save audio to audio/ subfolder audio_path = os.path.join("audio", original_name) # relative path for metadata sf.write(os.path.join(output_dir, audio_path), audio_array, sampling_rate) # Store metadata row row = {k: v for k, v in example.items() if k != "audio"} row["file_name"] = audio_path metadata_rows.append(row) # Save metadata to CSV df = pd.DataFrame(metadata_rows) df.to_csv(os.path.join(output_dir, "metadata.csv"), index=False) ``` --- ## 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: 
@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"
}

@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}
}
--- ## πŸ’¬ Contact For questions or feedback, please feel free to reach out at divyas@iiitd.ac.in. ## πŸ™ Acknowledgments - 🌍 ACL Diversity & Inclusion Subsidy for enabling in-person presentation at ACL 2025 - 🀝 HuggingFace for dataset hosting support - πŸŽ“SBILab and Infosys Centre for Artificial Intelligence (CAI) at IIIT-Delhi for their support