sanganaka/Vedavani-Dataset

Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry

Vedavani is the first benchmark dataset for automatic speech recognition (ASR) on Vedic Sanskrit poetry, consisting of richly annotated verses from the Rig Veda and Atharva Veda. This corpus captures the unique prosodic structure, phonetic complexity, and chanting style found in traditional Vedic recitation.

🔗 Paper: Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry (ACL 2025)
📁 GitHub Repository: https://github.com/SujeetNlp/Vedavani
📜 License: Apache License 2.0

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📦 Dataset Contents

This repository contains:

• train.csv — Metadata for training set
• validation.csv — Metadata for validation set
• test.csv — Metadata for test set
• Audio_files — Audio files in WAV format (segmented and aligned) [Due to hugging face restrictions files are organized in folder containing maximum 9000 files each. While using them in training/testing kindly move all the files in one single directory.]
• README — Textual documentation

Each CSV includes:

• path: Relative path to audio file
• transcription: Ground-truth text in Devanagari script, including prosodic markers

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📊 Dataset Statistics

Property	Value
Total Duration	~54 hours
Total Samples	30,779
Verses from Rig Veda	20,782
Verses from Atharva Veda	9,997
Avg. Audio Length	6.36 seconds
Vocabulary Size	64,082 unique words

Data Splits

Split	# Samples
Train	24,623
Validation	3,078
Test	3,078

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Use Cases

Vedavani is particularly useful for:

• Fine-tuning and benchmarking ASR models (e.g., Whisper, IndicWhisper, Wav2Vec2)
• Studying phonetic alignment in Sanskrit poetry
• Low-resource speech processing
• Prosody-aware speech models

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Citation

@article{
  title={Vedavani: A Benchmark Corpus for ASR on Vedic Sanskrit Poetry},
  author={Sujeet Kumar, Pretam Ray, Abhinay Beerukuri, Shrey Kamoji, Manoj Balaji Jagadeeshan, and Pawan Goyal},
  journal={https://arxiv.org/pdf/2506.00145v1},
  year={2025}
}