Datasets:

IntisarUddin
/

Bengali_Long_form_ASR

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Bad split: full. Available splits: ['train']
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 61, in get_rows
                  ds = load_dataset(
                       ^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1502, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1196, in as_streaming_dataset
                  raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
              ValueError: Bad split: full. Available splits: ['train']

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Bengali Long-Form ASR Dataset

Dataset Summary

The Bengali Long-Form ASR Dataset is a large-scale collection of long-duration Bangla speech recordings paired with verified transcripts. The dataset is designed specifically for long-form Automatic Speech Recognition (ASR) research.

Key Statistics

Total duration: 310.06 hours
Number of recordings: 382
Average duration per recording: ~48.7 minutes
Language: Bengali (bn)
Audio format: WAV
Sampling rate: 16 kHz
Channels: Mono

This dataset is suitable for:

Long-form ASR research
Whisper fine-tuning
Transformer-based speech modeling
Low-resource speech research

Dataset Structure

root/
├── audio/
├── dataset_metadata.csv
└── dataset_metadata.json

Audio

All recordings are stored in the audio/ directory.

Example: audio/audio_001.wav

Audio Properties:

Format: WAV
Sampling rate: 16 kHz
Mono channel

Metadata

The metadata file (dataset_metadata.csv) contains:

file_name: Audio file name (e.g., audio_001.wav)
original_id: Original YouTube video ID
title: Original video title
duration_min: Duration of recording in minutes
transcription: Full transcription text

Transcription Method

Transcriptions were generated using YouTube auto-captions or creator-provided subtitles, followed by manual correction and normalization.

This semi-automatic approach balances scalability and transcription quality.

Dataset Splitting

This dataset does NOT include predefined train/validation/test splits.

Since speaker metadata is not available, users are advised to perform recording-wise splitting to avoid data leakage.

Recommended split strategy:

Train: 80%
Validation: 10%
Test: 10%

Splitting should be performed at the audio file level, not at the text level.

Intended Use

This dataset is intended for academic and research use in:

Long-form Automatic Speech Recognition
Context-aware ASR systems
End-to-end speech modeling
Bangla speech-language modeling

Known Limitations

No speaker annotations
No predefined dataset splits
Long-form audio requires segmentation before training
Possible domain imbalance
Minor transcription inconsistencies may remain

Loading the Dataset

Using Hugging Face

from datasets import load_dataset

dataset = load_dataset("IntisarUddin/Bengali_Long_form_ASR")

print(dataset)

Or Manually Using Pandas

import pandas as pd

df = pd.read_csv("dataset_metadata.csv")

print(df.head())

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC-BY 4.0) License.

Users are free to share and adapt the material for any purpose, even commercially, provided appropriate credit is given.

Citation

@dataset{IntisarBengaliLongFormASR2026, author = {K M Intisar Uddin}, title = {Bengali Long-Form ASR Dataset}, year = {2026}, publisher = {Hugging Face}, license = {CC-BY 4.0} }

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