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linagora
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linto-dataset-audio-ar-tn-augmented

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: train
      num_bytes: 12125888408
      num_examples: 252
  download_size: 11966562052
  dataset_size: 12125888408
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/Youtube_DiwanFM_VCA/train/train-*

LinTO DataSet Audio for Arabic Tunisian Augmented v0.1
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.

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, 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) to alter the original voices using 7 different pretrained models.

The image below shows the difference between the original and the augmented audio:

Wave Interface

  • 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:
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:

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'}]

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} }