Update README.md

README.md

@@ -1,3 +1,147 @@
 ---
 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://www.sciencedirect.com/science/article/abs/pii/S0167639321001370)
+- **Point of Contact:** [Ahnaf Mozib Samin, Dept. of CSE, SUST](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. 
+The dialectal domain can be used for dialect recognition task. The [corresponding paper](https://www.sciencedirect.com/science/article/abs/pii/S0167639321001370) reports 
+detailed information about the development of BanSpeech and also the performance of state-of-the-art fully supervised, self-supervised and weakly supervised models' 
+performance 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 zipped BanSpeech folder from [this HuggingFace directory.](https://huggingface.co/datasets/ahnafsamin/SUBAK.KO/tree/main/Data)
+The zipped 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 directory. 
+
+### Supported Tasks and Leaderboards
+
+This benchmark is designed for the automatic speech recognition performance evaluation. The associated paper provides the baseline results on SUBAK.KO corpus. 
+
+### 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{kibria2022bangladeshi,
+  title={Bangladeshi Bangla speech corpus for automatic speech recognition research},
+  author={Kibria, Shafkat and Samin, Ahnaf Mozib and Kobir, M Humayon and Rahman, M Shahidur and Selim, M Reza and Iqbal, M Zafar},
+  journal={Speech Communication},
+  volume={136},
+  pages={84--97},
+  year={2022},
+  publisher={Elsevier}
+}
+```
+
+### Contributions
+
+Thanks to [Ahnaf Mozib Samin](https://huggingface.co/ahnafsamin) for adding this dataset.