---
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: darija_Latn
    dtype: string
  - name: darija_Arab_new
    dtype: string
  - name: english
    dtype: string
  - name: darija_Arab_old
    dtype: string
  splits:
  - name: train
    num_bytes: 2066939587.394
    num_examples: 12743
  download_size: 1286123292
  dataset_size: 2066939587.394
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
language:
- en
---
# Moroccan Darija Speech Dataset

## Overview
This dataset consists of **12,743 parallel text and speech samples** for **Moroccan Darija**, including its transcription in both Latin and Arabic scripts and English translations. It was created to support **speech recognition**, **language modeling**, and **NLP tasks** for Moroccan Darija.

## Dataset Source
The dataset was originally sourced from [this repository](https://github.com/darija-open-dataset/dataset/tree/main/sentences), where it was available as a **CSV file** containing three columns:
- **darija**: Sentences in Moroccan Darija using Latin letters.
- **eng**: English translations of the sentences.
- **darija_ar**: Sentences in Moroccan Darija using Arabic script.

## Data Preprocessing
To ensure data quality, the following preprocessing steps were applied:
1. **Removed all rows with missing values** in any of the three original columns.
2. **Filtered dataset** to retain only fully filled rows.
3. The cleaned dataset resulted in **12,743 sentences** ready for audio recording.

## Audio Recording Process

### **Total Audio Duration**
The total duration of the recorded audio is **9 hours and 46 minutes**.

The dataset was recorded by **7 contributors** (**4 females, 3 males**). Each sentence was spoken and recorded by one of the contributors. The dataset was divided into **13 chunks**, but contributors were not assigned strictly per chunk—some recorded more than **1,000 sentences**, while others recorded fewer.

### **Recording Distribution with Speaker Index**
Each contributor's contributions are indexed as follows:

```
Samples 0-999     -> F1 (Female 1)
Samples 1000-1999 -> M3 (Male 3)
Samples 2000-2730 -> F2 (Female 2)
Samples 2731-2800 -> M1 (Male 1)
Samples 2801-2999 -> M2 (Male 2)
Samples 3000-3999 -> M2 (Male 2)
Samples 4000-4999 -> M1 (Male 1)
Samples 5000-5999 -> F3 (Female 3)
Samples 6000-6999 -> M1 (Male 1)
Samples 7000-7999 -> F4 (Female 4)
Samples 8000-8999 -> F1 (Female 1)
Samples 9000-9999 -> M2 (Male 2)
Samples 10000-10999 -> M1 (Male 1)
Samples 11000-11999 -> M1 (Male 1)
Samples 12000-12350 -> M2 (Male 2)
Samples 12351-12742 -> M1 (Male 1)
```


```
0-999     -> Female
1000-1999 -> Male
2000-2730 -> Female
2731-2800 -> Male
2801-2999 -> Male
3000-3999 -> Male
4000-4999 -> Male
5000-5999 -> Female
6000-6999 -> Male
7000-7999 -> Female
8000-8999 -> Female
9000-9999 -> Male
10000-10999 -> Male
11000-11999 -> Male
12000-12350 -> Male
12351-12742 -> Male
```

- The recorded audio files were standardized to **16kHz sample rate** to maintain consistency.

## Transcription Correction Process
During the dataset review, spelling errors were identified in the **darija_ar** (Arabic script) column. For example:
- Incorrect: **"شوكران"**
- Correct: **"شكرا"**

### **Correction Methodology**
To correct these errors efficiently:
1. We used **[Wit.ai](https://wit.ai/) (Arabic language setting)** to transcribe all audio recordings.
2. [Wit.ai](https://wit.ai/) **skipped/ignored words that were not in Arabic dialects**.
3. Instead of manually correcting every sentence, we **added the missing words to the transcription**.
4. This method ensured high accuracy and eliminated human error in transcription.

## Final Dataset Structure
After full correction and validation, the final dataset consists of **five columns**:

| Column Name         | Description |
|---------------------|-------------|
| **audio**          | Speech recordings for Darija sentences |
| **darija_Ltn**     | Darija sentences using Latin letters |
| **darija_Arab_new** | Corrected Darija sentences using Arabic script |
| **english**        | English translation of Darija sentences |
| **darija_Arab_old** | Original (uncorrected) Darija sentences in Arabic script |

## Applications
This dataset can be used for:
- **Speech-to-text models** for Moroccan Darija.
- **NLP applications**, such as machine translation and text-to-speech synthesis.
- **Linguistic studies** on Moroccan Darija.
- **Automatic pronunciation analysis**.

## Acknowledgments

## Contact
For any inquiries or collaborations, feel free to connect with me on [LinkedIn](https://www.linkedin.com/in/mahmoudbidry/).
We would like to extend special thanks to the contributors:
- **BIDRY Mahmoud**
- **ZAIDOUNE Youssef**
- AtlasIA's community.

## License
This dataset is released under the **MIT License**. Feel free to use, modify, and distribute it for research and development purposes.

## Citation
If you use this dataset in your research, please cite it appropriately:
```
@misc{darija_speech_dataset,
  author = {BIDRY Mahmoud, ZAIDOUNE Youssef, et al.},
  title = {Moroccan Darija Speech Dataset},
  year = {2025},
  organization = {atlasIA}
  howpublished = {Hugging Face Datasets},
  url = {https://huggingface.co/datasets/atlasia/DODa-audio-dataset-V3}
}
```