README.md

---
license: cc-by-nc-4.0
task_categories:
- automatic-speech-recognition
language:
- bn
tags:
- Evaluation Benchmark
- Robustness
- ASR
- Bengali
- Spontaneous Speech
size_categories:
- 1K<n<10K
---

# Dataset Card for BanSpeech

## Table of Contents
- [Dataset Card for SUBAK.KO](#dataset-card-for-BanSpeech)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
    - [Data Fields](#data-fields)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Social Impact of Dataset](#social-impact-of-dataset)
    - [Discussion of Biases](#discussion-of-biases)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Dataset Curators](#dataset-curators)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Developed By** Dept. of CSE, SUST, Bangladesh
- **Paper:** [BanSpeech: A Multi-domain Bangla Speech Recognition Benchmark Toward Robust Performance in Challenging Conditions](https://ieeexplore.ieee.org/document/10453554)
- **Point of Contact:** [Ahnaf Mozib Samin](mailto:asamin9796@gmail.com)

### Dataset Summary

BanSpeech is a publicly available human-annotated Bangladeshi standard Bangla multi-domain automatic speech recognition (ASR) benchmark. 
This benchmark contains approximately 6.52 hours of human-annotated broadcast speech, totaling 8085 utterances, across 13 distinct domains and 
is primarily designed for ASR performance evaluation in challenging conditions e.g. spontaneous, domain-shifting, multi-talker, code-switching. 
In addition, BanSpeech covers dialectal domains from 7 regions of Bangladesh, however, this part is weakly labeled and can be used for dialect recognition task. 
The [corresponding paper](https://ieeexplore.ieee.org/document/10453554) reports 
detailed information about the development of BanSpeech, along with an analysis of the performance of state-of-the-art 
fully supervised, self-supervised, and weakly supervised models on BanSpeech.

BanSpeech is developed by the researchers from the **Department of Computer Science and Engineering (CSE)** at **Shahjalal University of Science and Technology (SUST), 
Bangladesh**.


### Example Usage
To load the full BanSpeech, use the following code:

```python
from datasets import load_dataset

dataset = load_dataset("SUST-CSE-Speech/banspeech")
```

To load a specific domain of the BanSpeech, define the domain in the split parameter and set the streaming mode as True in the following way:

```python
from datasets import load_dataset

dataset = load_dataset("SUST-CSE-Speech/banspeech", split="sports", streaming=True)
```
More documentation on streaming can be found [from this link.](https://huggingface.co/docs/datasets/stream#split-dataset)

Alternatively, you can manually download the BanSpeech from [this HuggingFace directory.](https://huggingface.co/datasets/SUST-CSE-Speech/banspeech/blob/main/zipped_data/banspeech.zip)
The compressed folder contains speeches from the 13 general domains as well as the 7 dialectal domains.
The csv files corresponding to the domains can be found in the same zipped file. 

### Supported Tasks and Leaderboards

This benchmark is designed for the automatic speech recognition performance evaluation. The associated paper provides the comprehensive evaluation of the state-of-the-art
models on BanSpeech. 

### Languages

Bangladeshi standard Bangla

## Dataset Structure

### Data Instances

A typical data point comprises the path to the audio file and its transcription.

```
{
 'audio': {'path': '/home/username/Study/wav2vec2/bangla_broadcast_speech_corpus/banspeech/television_news/news_shomoy_11_d_222.wav',
		   'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
		   'sampling_rate': 16000},
 'transcript': 'এবং রাস্তা হয়েছে',
 'path': '/television_news/news_shomoy_11_d_222.wav'
}
```

### Data Fields

- audio: A dictionary containing the path to the original audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.

- transcription: The orthographic transcription

- file_path: The relative path to the audio file


## Additional Information

### Licensing Information

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)

### Citation Information
Please cite the following paper if you use the corpus.

```
@ARTICLE{10453554,
  author={Samin, Ahnaf Mozib and Kobir, M. Humayon and Rafee, Md. Mushtaq Shahriyar and Ahmed, M. Firoz and Hasan, Mehedi and Ghosh, Partha and Kibria, Shafkat and Rahman, M. Shahidur},
  journal={IEEE Access}, 
  title={BanSpeech: A Multi-Domain Bangla Speech Recognition Benchmark Toward Robust Performance in Challenging Conditions}, 
  year={2024},
  volume={12},
  number={},
  pages={34527-34538},
  keywords={Speech recognition;Data models;Benchmark testing;Speech processing;Robustness;Solid modeling;Task analysis;Automatic speech recognition;Transfer learning;Neural networks;Convolutional neural networks;Supervised learning;Automatic speech recognition;Bangla;domain shifting;read speech;spontaneous speech;transfer learning},
  doi={10.1109/ACCESS.2024.3371478}}
```

### Contributions

Thanks to [Ahnaf Mozib Samin](https://huggingface.co/ahnafsamin) for adding this dataset.