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