Duplicate from SUST-CSE-Speech/banspeech

Co-authored-by: Ahnaf Mozib Samin <ahnafsamin@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
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+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
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+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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.

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