linagora/linto-dataset-audio-ar-tn

LinTO DataSet Audio for Arabic Tunisian <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).

• —Dataset Summary
• —Dataset composition
• —Sources
• —Data Table
• —Data sources
• —Content Types
• —Languages and Dialects
• —Example use (python)
• —License
• —Citations

Dataset Summary

The LinTO DataSet Audio for Arabic Tunisian 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 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

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:

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")

Check the containt of dataset:

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

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 dataset, please cite the following paper (arxiv:2504.02604).

@misc{linagora2024Linto-tn,
  title = {LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect},
  author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré},
  year = {2025},
  month = {March},
  eprint={2504.02604},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  note={Good Data Workshop, AAAI 2025},
  url={arxiv.org/abs/2504.02604},
}

Please also consider citing the papers of datasets included in this dataset:

@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}
}

@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} }