Datasets:

sanganaka
/

Vedavani-Dataset

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
audio_file: string
text: string
length: double
audio: null
transcription: null
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 604
to
{'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'transcription': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1914, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              audio_file: string
              text: string
              length: double
              audio: null
              transcription: null
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 604
              to
              {'audio': Audio(sampling_rate=None, decode=True, stream_index=None), 'transcription': Value('string')}
              because column names don't match

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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

📦 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

📊 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

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

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}
}

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