metadata

dataset_info:
  features:
    - name: vocalized
      dtype: string
    - name: erroneous
      dtype: string
  splits:
    - name: train
      num_bytes: 4494447826
      num_examples: 1460625
    - name: valid
      num_bytes: 93024950
      num_examples: 30129
    - name: test
      num_bytes: 46293622
      num_examples: 15062
  download_size: 2248929902
  dataset_size: 4633766398
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: valid
        path: data/valid-*
      - split: test
        path: data/test-*
license: mit
task_categories:
  - text2text-generation
language:
  - ar
tags:
  - Speech-Correction
  - Diacritics
  - Vocalized
  - Arabic
  - Tashkeel

Text with Diacritics Correction Dataset

Overview

This dataset is an Arabic dataset derived from Abdou/arabic-tashkeel-dataset, with additional preprocessing and error injection to help train models for speech and typing correction.

Key Features

✅ Preprocessed Arabic text: Cleaned vocalized column by removing non-Arabic characters, symbols, emojis, brackets, and numbers.
✅ Injected realistic errors: Includes four types of errors that simulate real-world speech and typing mistakes.
✅ Large-scale dataset: Over 1.5 million text samples for robust training.
✅ Suitable for NLP tasks: Useful for text correction, speech recognition, and Arabic NLP research.

Dataset Details

Statistics

Split	Samples
Train	1,460,625
Validation	30,129
Test	15,062
Total	1,505,816

Data Format

File Type: Parquet
Columns:
- vocalized: Preprocessed Arabic text
- erroneous: The same text with injected errors

Error Injection Details

We introduced four types of common Arabic text errors to enhance the dataset’s usability for speech and typing correction:

Keyboard Errors 🖥️
- Mistyped letters based on nearby keyboard keys.
- Example: السلام → الستام (minor keyboard slip).
Ordering Errors 🔀
- Swapped adjacent letters.
- Example: مرحبا → مرحاب (common typing mistake).
Phonetic Errors 🎙️
- Replaced letters with phonetically similar ones.
- Example: ن ↔ م, س ↔ ث.
Diacritic Errors 🐪
- Altered or removed Arabic diacritics (Tashkeel).
- Example: كَتَبَ → كَتُبَ.

These errors mimic real-world mistakes in speech recognition and Arabic typing, improving model robustness.

Usage

Loading the Dataset in Python

from datasets import load_dataset

dataset = load_dataset("FlouBsy/Text-with-Diacritics-Correction")

# Example access
print(dataset['train'][0]) # Show first sample

Collaborators

Basma M. (Main Contributor)
Selsabeel A.

Citations

@misc{arabic_tashkeel_dataset,
  author = {Abdou},
  title = {{Arabic Tashkeel Dataset}},
  year = {2024},
  url = {https://huggingface.co/datasets/Abdou/arabic-tashkeel-dataset}
}

@misc{Text-with-Diacritics-Correction,
  author = {Basma M., Selsabeel A.},
  title = {{Arabic Speech & Typing Errors with Diacritics Dataset}},
  year = {2025},
  url = {https://huggingface.co/datasets/FlouBsy/Text-with-Diacritics-Correction}
}