| --- |
| 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. |
