README.md

---
language:
- ar
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
version: 1
dataset_info:
  features:
  - name: audio_id
    dtype: string
  - name: audio
    dtype: audio
  - name: segments
    list:
    - name: end
      dtype: float64
    - name: start
      dtype: float64
    - name: transcript
      dtype: string
    - name: transcript_raw
      dtype: string
  - name: transcript
    dtype: string

configs:
- config_name: default
  data_files:
  - split: train
    path: data/*/train/train-*
- config_name: AmenyKH
  data_files:
  - split: train
    path: data/AmenyKH/train/train-*
  - split: test
    path: data/AmenyKH/test/test-*
  splits:
  - name: train
    num_bytes: 584244476.742
    num_examples: 3094
  - name: test
    num_bytes: 8386953.0
    num_examples: 44
  download_size: 659775847
  dataset_size: 592631429.742
- config_name: ApprendreLeTunisien
  data_files:
  - split: train
    path: data/ApprendreLeTunisien/train/train-*
  - split: test
    path: data/ApprendreLeTunisien/test/test-*
  splits:
  - name: train
    num_bytes: 198592398.0
    num_examples: 878
  - name: test
    num_bytes: 16277071.0
    num_examples: 116
  download_size: 130969100
  dataset_size: 214869469.0
- config_name: OneStory
  data_files:
  - split: train
    path: data/OneStory/train/train-*
  - split: test
    path: data/OneStory/test/test-*
  splits:
  - name: train
    num_bytes: 65345927.0
    num_examples: 36
  - name: test
    num_bytes: 5218264.0
    num_examples: 3
  download_size: 64516876
  dataset_size: 70564191.0
- config_name: TunSwitchCS
  data_files:
  - split: train
    path: data/TunSwitchCS/train/train-*
  - split: test
    path: data/TunSwitchCS/test/test-*
  splits:
  - name: train
    num_bytes: 2024232163.068
    num_examples: 5377
  - name: test
    num_bytes: 79006291.0
    num_examples: 300
  download_size: 2266757942
  dataset_size: 2103238454.068
- config_name: Youtube_TNScrapped_V1
  data_files:
  - split: train
    path: data/Youtube_TNScrapped_V1/train/train-*
  - split: test
    path: data/Youtube_TNScrapped_V1/test/test-*
  splits:
  - name: train
    num_bytes: 45233498.0
    num_examples: 52
  - name: test
    num_bytes: 20040136.0
    num_examples: 5
  download_size: 1127125890
  dataset_size: 65273634.0
---
# LinTO DataSet Audio for Arabic Tunisian v0.1 <br />*A collection of Tunisian dialect audio and its annotations for STT task*

This is the first packaged version of the datasets used to train the Linto Tunisian dialect with code-switching STT
([linagora/linto-asr-ar-tn-0.1](https://huggingface.co/linagora/linto-asr-ar-tn-0.1)).

* [Dataset Summary](#dataset-summary)
* [Dataset composition](#dataset-composition)
  * [Sources](#sources)
  * [Data Table](#data_table)
  * [Data sources](#data-sources)
  * [Content Types](#content-types)
  * [Languages and Dialects](#languages-and-dialects)
* [Example use (python)](#example-use-python)
* [License](#license)
* [Citations](#citations)


## Dataset Summary

The **LinTO DataSet Audio for Arabic Tunisian v0.1** is a diverse collection of audio content including music, documentaries, podcasts, and other types of recordings, along with their corresponding transcriptions. The dataset is primarily focused on supporting speech recognition tasks for the Tunisian dialect, with some instances of code-switching between Tunisian Arabic, French, and English. It is organized into multiple configurations and splits for different experimental setups, making it valuable for Automatic Speech Recognition (ASR) research and development.

## Dataset Composition

The **LinTO DataSet Audio for Arabic Tunisian v0.1** comprises a diverse range of audio content collected from multiple sources. Below is a breakdown of the dataset’s composition:

### Sources

- **Hugging Face Datasets**: Various datasets obtained from the Hugging Face platform.
- **YouTube**: Audio collected from a range of YouTube channels and videos, including both shorts and long-form content, covering music, documentaries, and podcasts.
- **Websites**: Audio gathered from various online sources, including educational sites and story-sharing platforms.
### Data Table
| **subset** | **audio duration** | **labeled audio duration** | **# audios** | **# segments** | **# words** | **# characters** |
| --- | --- | --- | --- | --- | --- | --- |
| [AmenyKH](https://huggingface.co/datasets/amenIKh/dataset1) | 4h 5m 28s + 3m 42s | 4h 5m 28s + 3m 42s | 3094 + 44 | 3094 + 44 | 31713 + 483 | 158851 + 2462 |
| [ApprendreLeTunisien](https://www.apprendreletunisien.com/traduction.php?domaine=adjectif) | 37m 30s + 3m 4s | 37m 31s + 3m 4s | 878 + 116 | 878 + 116 | 1148 + 156 | 5220 + 711 |
| [MASC](https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus) | 2h 52m 43s | 1h 37m 11s | 47 | 1728 | 11750 | 59013 |
| [OneStory](https://www.onestory-media.org/story_sets/) | 1h 32m 47s + 8m 3s | 1h 31m 15s + 7m 43s | 36 + 3 | 494 + 43 | 12336 + 1028 | 56878 + 5059 |
| [TunSwitchCS](https://zenodo.org/records/8370566) | 10h 0m 56s + 27m 17s | 10h 0m 56s + 27m 17s | 5377 + 300 | 5377 + 300 | 74947 + 4253 | 391038 + 22304 |
| [TunSwitchTO](https://zenodo.org/records/8370566) | 3h 19m 6s + 28m 38s | 3h 19m 6s + 28m 38s | 2195 + 344 | 2195 + 344 | 18222 + 2736 | 94020 + 14102 |
| [Tunisian_dataset_STT-TTS15s_filtred1.0](https://huggingface.co/datasets/Arbi-Houssem/Tunisian_dataset_STT-TTS15s_filtred1.0) | 4h 7m 42s | 4h 8m 14s | 1029 | 1029 | 33428 | 172927 |
| [Wav2Vec-tunisian-Darja](https://huggingface.co/datasets/medaminekonyali/Value-Wav2Vec-tunisian-Darja-Augmented) | 3h 26m 33s | 3h 26m 33s | 7898 | 7898 | 20352 | 104176 |
| Youtube_AbdelAzizErwi | 24h 34m 10s | 21h 54m 32s | 25 | 21940 | 131544 | 623434 |
| Youtube_BayariBilionaire | 58m 49s | 55m 5s | 6 | 1080 | 7813 | 39831 |
| Youtube_DiwanFM | 5h 27m 6s | 4h 2m 43s | 36 | 4670 | 30310 | 152352 |
| Youtube_HamzaBaloumiElMohakek | 16h 41m 50s | 14h 5m 49s | 21 | 13734 | 89334 | 446736 |
| Youtube_HkeyetTounsiaMensia | 1h 44m 47s | 1h 24m 46s | 5 | 1518 | 10528 | 51570 |
| Youtube_LobnaMajjedi | 57m 22s | 53m 13s | 2 | 886 | 6134 | 30216 |
| Youtube_MohamedKhammessi | 1h 43m 52s | 1h 34m 3s | 2 | 1825 | 13216 | 64141 |
| Youtube_Qlm | 2h 31m 33s | 1h 51m 34s | 53 | 2541 | 15728 | 83682 |
| Youtube_TNScrapped_V1 | 4h 7m 58s + 18m 42s | 2h 33m 30s + 9m 53s | 52 + 5 | 2538 + 179 | 18777 + 1448 | 92531 + 7375 |
| Youtube_TN_Shorts | 3h 46m 26s | 3h 23m 43s | 135 | 2022 | 28129 | 143966 |
| Youtube_TV | 36m 36s | 31m 34s | 4 | 668 | 4768 | 24006 |
| **TOTAL** | **93h 13m 15s / 1h 29m 26s** | **81h 56m 45s / 1h 20m 17s** | **20895 / 812** | **76115 / 1026** | **560177 / 10104** | **2794588 / 52013** |
> **NB:** The **+** in each information column indicates the combined **train + test** data. For any datasets other than YouTube, which include their links, please review the provided links for additional details.

### Data Proccessing:
- **Audio Alignment**: Matching audio segments with corresponding text to ensure accurate transcription and contextual alignment.
- **Transcription Correction**: Reviewing and correcting transcriptions to address errors and discrepancies in the initial text.
- **Standardization**: Converting words and phrases into their standardized forms to maintain consistency across the dataset.
- **Padding**: Adding padding to shorter audio segments to address issues with Kaldi and ensure uniformity in input lengths.
- **Silence Removal**: Eliminating segments of audio that contain only silence to improve dataset efficiency and relevance.
- **Annotation**: Labeling audio segments that require transcriptions and other metadata. Ensuring that non-annotated audio is reviewed and annotated if necessary.

### Content Types
- **Music**: Includes recordings of different music genres.
- **FootBall**: Includes recordings of football news and reviews.
- **Documentaries**: Audio from documentaries about history and nature.
- **Podcasts**: Conversations and discussions from various podcast episodes.
- **Authors**:  Audio recordings of authors reading or discussing different stories: horror, children's literature, life lessons, and others.
- **Lessons**: Learning resources for the Tunisian dialect.
- **Others**: Mixed recordings with various subjects.

### Languages and Dialects
- **Tunisian Arabic**:  The primary focus of the dataset, including Tunisian Arabic and some Modern Standard Arabic (MSA).
- **French**: Some instances of French code-switching.
- **English**: Some instances of English code-switching.

### Characteristics
- **Audio Duration**: The dataset contains approximately 93 hours of audio recordings.
- **Segments Duration**: This dataset contains segments, each with a duration of less than 30 seconds.
- **Labeled Data**: Includes annotations and transcriptions for a significant portion of the audio content.

### Data Distribution
- **Training Set**: Comprises a diverse range of audio recordings, each representing different contexts, aimed at enhancing the model's performance across various scenarios.
- **Testing Set**: onsists of a varied set of audio recordings, also covering different contexts, dedicated to assessing the model’s performance and generalization.

This composition ensures a comprehensive representation of various audio types and linguistic features, making the dataset valuable for a range of ASR research and development tasks.

## Example use (python)
- **Load  the dataset in python**:
```python
from datasets import load_dataset

# dataset will be loaded as a DatasetDict of train and test
dataset = load_dataset("linagora/linto-dataset-audio-ar-tn-0.1")
```

Check the containt of dataset:
```python
example = dataset['train'][0] 
audio_array = example['audio']["array"]
segments = example['segments']
transcription = example['transcript']

print(f"Audio array: {audio_array}")
print(f"Segments: {segments}")
print(f"Transcription: {transcription}")
```
**Example**
```bash
Audio array: [0. 0. 0. ... 0. 0. 0.]
Transcription: أسبقية قبل أنا ما وصلت خممت فيه كيما باش نحكيو من بعد إلا ما أنا كإنطريبرنور كباعث مشروع صارولي برشا مشاكل فالجستين و صارولي مشاكل مع لعباد لي كانت موفرتلي اللوجسيل ولا اللوجسيل أوف لنيه ولا لوجسيل بيراتي
segments: [{'end': 14.113, 'start': 0.0, 'transcript': 'أسبقية قبل أنا ما وصلت خممت فيه كيما باش نحكيو من بعد إلا ما أنا كإنطريبرنور كباعث مشروع صارولي برشا مشاكل فالجستين و صارولي مشاكل مع لعباد لي كانت موفرتلي اللوجسيل ولا اللوجسيل أوف لنيه ولا لوجسيل بيراتي', 'transcript_raw': 'أسبقية قبل أنا ما وصلت خممت فيه كيما باش نحكيو من بعد إلا ما أنا كإنطريبرنور كباعث مشروع صارولي برشا مشاكل فالجستين و صارولي مشاكل مع لعباد لي كانت موفرتلي اللوجسيل ولا اللوجسيل أوف لنيه ولا لوجسيل بيراتي'}]
```


## License
Given that some of the corpora used for training and evaluation are available only under CC-BY-4.0 licenses, we have chosen to license the entire dataset under CC-BY-4.0.

## Citations
When using the **LinTO DataSet Audio for Arabic Tunisian v0.1** corpus, please cite this page:

```bibtex
@misc{linagora2024Linto-tn,
  author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré and Sarah zribi and Wajdi Ghezaiel},
  title = {LinTO DataSet Audio for Arabic Tunisian v0.1},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace},
  howpublished = {\url{https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn-0.1}},
}
```

```bibtex
@misc{abdallah2023leveraging,
      title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, 
      author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem},
      year={2023},
      eprint={2309.11327},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}
```

```bibtex
@data{e1qb-jv46-21,
doi = {10.21227/e1qb-jv46},
url = {https://dx.doi.org/10.21227/e1qb-jv46},
author = {Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
publisher = {IEEE Dataport},
title = {MASC: Massive Arabic Speech Corpus},
year = {2021} }
```