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---
language:
- ar
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
version: 1.0
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
splits:
- name: train
num_bytes: 839147756.322
num_examples: 6146
download_size: 798894474
dataset_size: 839147756.322
configs:
- config_name: default
data_files:
- split: train
path: data/ApprendreLeTunisien_VCA/train/train-*
---
# LinTO DataSet Audio for Arabic Tunisian Augmented v0.1 <br />*A collection of Tunisian dialect audio and its annotations for STT task*
This is the augmented 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)
* [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 Augmented v0.1** is a dataset that builds on **LinTO DataSet Audio for Arabic Tunisian v0.1**, using a subset of the original audio data. Augmentation techniques, including noise reduction and SoftVC VITS Singing Voice Conversion (SVC), have been applied to enhance the dataset for improved performance in Arabic Tunisian Automatic Speech Recognition (ASR) tasks.
## Dataset Composition:
The **LinTO DataSet Audio for Arabic Tunisian Augmented v0.1** comprises a diverse range of augmented audio samples using different techniques. Below is a breakdown of the dataset’s composition:
### Sources
| **subset** | **audio duration** | **labeled audio duration** | **# audios** | **# segments** | **# words** | **# characters** |
| --- | --- | --- | --- | --- | --- | --- |
| ApprendreLeTunisienVCA | 2h 40m 6s | 2h 40m 6s | 6146 | 6146 | 8078 | 36687 |
| MASCNoiseLess | 2h 49m 56s | 1h 38m 17s | 48 | 1742 | 11909 | 59876 |
| MASC_NoiseLess_VCA | 19h 49m 31s | 11h 27m 59s | 336 | 12194 | 83377 | 411999 |
| OneStoryVCA | 9h 16m 51s | 9h 7m 32s | 216 | 2964 | 73962 | 341670 |
| TunSwitchCS_VCA | 59h 39m 10s | 59h 39m 10s | 37639 | 37639 | 531727 | 2760268 |
| TunSwitchTO_VCA | 18h 57m 34s | 18h 57m 34s | 15365 | 15365 | 129304 | 659295 |
| Youtube_AbdelAzizErwi_VCA | 122h 51m 1s | 109h 32m 39s | 125 | 109700 | 657720 | 3117170 |
| Youtube_BayariBilionaireVCA | 4h 54m 8s | 4h 35m 25s | 30 | 5400 | 39065 | 199155 |
| Youtube_DiwanFM_VCA | 38h 10m 6s | 28h 18m 58s | 252 | 32690 | 212170 | 1066464 |
| Youtube_HkeyetTounsiaMensia_VCA | 12h 13m 29s | 9h 53m 22s | 35 | 10626 | 73696 | 360990 |
| Youtube_LobnaMajjedi_VCA | 6h 41m 38s | 6h 12m 31s | 14 | 6202 | 42938 | 211512 |
| Youtube_MohamedKhammessi_VCA | 12h 7m 8s | 10h 58m 21s | 14 | 12775 | 92512 | 448987 |
| Youtube_Shorts_VCA | 26h 26m 25s | 23h 45m 58s | 945 | 14154 | 201138 | 1021713 |
| Youtube_TNScrappedNoiseLess_V1 | 4h 2m 9s | 2h 33m 30s | 52 | 2538 | 18777 | 92530 |
| Youtube_TNScrapped_NoiseLess_VCA_V1 | 28h 15m 1s | 17h 54m 32s | 364 | 17766 | 132587 | 642292 |
| **TOTAL** | **402h 47m 10s** | **389h 43m 37s** | **58129** | **276204** | **1311134** | **7405055** |
### Data Proccessing:
- **Noise Reduction**: Applying techniques to minimize background noise and enhance audio clarity for better model performance. For this, we used **Deezer [Spleeter](https://github.com/deezer/spleeter)**, a library with pretrained models, to separate vocals from music.
- **Voice Conversion**: Modifying speaker characteristics (e.g., pitch) through voice conversion techniques to simulate diverse speaker profiles and enrich the dataset. For this, we chose **SoftVC VITS Singing Voice Conversion** ([SVC](https://github.com/voicepaw/so-vits-svc-fork)) to alter the original voices using 7 different pretrained models.
The image below shows the difference between the original and the augmented audio:
- The first row shows the original waveform.
- The second row shows the audio after noise reduction.
- The last row shows the audio with voice conversion augmentation.
### Content Types
- **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 more than 317 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**: Includes a diverse range of augmented audio with 5 to 7 different voices, as well as noise reduction applied to two datasets.
## 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-augmented-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'}]
```
## 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} }
```
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