File size: 2,570 Bytes
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dataset_info:
features:
- name: id
dtype: int32
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: arabic
dtype: string
- name: english
dtype: string
splits:
- name: train
num_bytes: 1494439642.810786
num_examples: 2228
- name: validation
num_bytes: 186469553.36575875
num_examples: 278
- name: test
num_bytes: 187140351.28793773
num_examples: 279
download_size: 1848856905
dataset_size: 1868049547.4644823
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# FLEURS-AR-EN Dataset
## Dataset Description
FLEURS-AR-EN is an Arabic-to-English dataset designed for Speech Translation tasks. This dataset is derived from Google's FLEURS (Few-shot Learning Evaluation of Universal Representations of Speech) dataset, specifically focusing on aligned Arabic audio samples with their corresponding Arabic transcriptions and English translations.
### Overview
- **Task:** Speech Translation
- **Languages:** Arabic (source) → English (target)
- **Source:** Google FLEURS dataset
- **Dataset Size:** 1.87 GB (1,868,049,547 bytes)
- **Download Size:** 1.85 GB (1,848,856,905 bytes)
## Dataset Structure
### Features
- `id` (int32): Unique identifier for each example
- `audio` (audio): Audio file with 16kHz sampling rate
- `arabic` (string): Arabic transcription
- `english` (string): English translation
### Splits
- **Train**: 2,228 examples (1.49 GB)
- **Validation**: 278 examples (186.47 MB)
- **Test**: 279 examples (187.14 MB)
### Data Files
The dataset is organized into the following structure:
```
data/
├── train-*
├── validation-*
└── test-*
```
## Dataset Details
The dataset was created by aligning the Arabic and English portions of the FLEURS dataset through identifying common IDs between Arabic and English data and merging the datasets based on these common identifiers.
## Citation
```bibtex
@article{fleurs2022arxiv,
title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera,
Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
journal = {arXiv preprint arXiv:2205.12446},
url = {https://arxiv.org/abs/2205.12446},
year = {2022},
}
```
## Contact
For questions or issues related to the dataset, please contact:
Farah Abdou (faraahabdou@gmail.com)
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