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Datasets:
linagora
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linto-dataset-audio-ar-tn-0.1

Tasks:
Automatic Speech Recognition
Text-to-Speech
Text-to-Audio
Modalities:
Audio
Text
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Languages:
Arabic
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metadata
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
  splits:
    - name: test
      num_bytes: 242514526
      num_examples: 331
    - name: train
      num_bytes: 45233498
      num_examples: 52
  download_size: 1107487442
  dataset_size: 287748024
configs:
  - 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
        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
        num_examples: 878
      - name: test
        num_bytes: 16277071
        num_examples: 116
    download_size: 130969100
    dataset_size: 214869469
  - 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
        num_examples: 36
      - name: test
        num_bytes: 5218264
        num_examples: 3
    download_size: 64516876
    dataset_size: 70564191
  - 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
        num_examples: 300
    download_size: 2266757942
    dataset_size: 2103238454.068
  - config_name: default
    data_files:
      - split: train
        path: data/Youtube_TNScrapped_V1/train/train-*
      - split: test
        path: data/TunSwitchTO/test/test-*

LinTO DataSet Audio for Arabic Tunisian v0.1
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).

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 4h 5m 28s + 3m 42s 4h 5m 28s + 3m 42s 3094 + 44 3094 + 44 31713 + 483 158851 + 2462
ApprendreLeTunisien 37m 30s + 3m 4s 37m 31s + 3m 4s 878 + 116 878 + 116 1148 + 156 5220 + 711
MASC 2h 52m 43s 1h 37m 11s 47 1728 11750 59013
OneStory 1h 32m 47s + 8m 3s 1h 31m 15s + 7m 43s 36 + 3 494 + 43 12336 + 1028 56878 + 5059
TunSwitchCS 10h 0m 56s + 27m 17s 10h 0m 56s + 27m 17s 5377 + 300 5377 + 300 74947 + 4253 391038 + 22304
TunSwitchTO 3h 19m 6s + 28m 38s 3h 19m 6s + 28m 38s 2195 + 344 2195 + 344 18222 + 2736 94020 + 14102
Tunisian_dataset_STT-TTS15s_filtred1.0 4h 7m 42s 4h 8m 14s 1029 1029 33428 172927
Wav2Vec-tunisian-Darja 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:
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:

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 v0.1 corpus, please cite this page:

@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}},
}

@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} }