--- language: - ar task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio license: cc-by-4.0 version: 1.0 dataset_info: - config_name: default 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 - config_name: ApprendreLeTunisienVCA 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 - config_name: MASC_NoiseLess 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: 1798927453.0 num_examples: 48 download_size: 1508394957 dataset_size: 1798927453.0 - config_name: MASC_NoiseLess_VCA 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: 6297517576.0 num_examples: 336 download_size: 5218109270 dataset_size: 6297517576.0 - config_name: OneStory_VCA 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: 2948770377.0 num_examples: 216 download_size: 2745380587 dataset_size: 2948770377.0 - config_name: TunSwitchCS_VCA 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: 16211221231.134 num_examples: 37639 download_size: 18870351203 dataset_size: 16211221231.134 - config_name: TunSwitchTO_VCA 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: 5926536342.08 num_examples: 15365 download_size: 5236455978 dataset_size: 5926536342.08 - config_name: Youtube_AbdelAzizErwi_VCA 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: 39027242686.0 num_examples: 125 download_size: 30064752032 dataset_size: 39027242686.0 - config_name: Youtube_BayariBilionaireVCA 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: 1557801334.0 num_examples: 30 download_size: 1524983572 dataset_size: 1557801334.0 - config_name: Youtube_DiwanFM_VCA 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: 12125888408.0 num_examples: 252 download_size: 11966562052 dataset_size: 12125888408.0 - config_name: Youtube_HkeyetTounsiaMensia_VCA 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: 3883840637.0 num_examples: 35 download_size: 3803268888 dataset_size: 3883840637.0 - config_name: Youtube_LobnaMajjedi_VCA 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: 2126737013.0 num_examples: 14 download_size: 2045521265 dataset_size: 2126737013.0 - config_name: Youtube_MohamedKhammessi_VCA 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: 3850743255.0 num_examples: 14 download_size: 3803407855 dataset_size: 3850743255.0 - config_name: Youtube_Shorts_VCA 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: 8401284864.0 num_examples: 945 download_size: 8279119035 dataset_size: 8401284864.0 - config_name: Youtube_TNScrapped_V1_NoiseLess 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: 2510511859.0 num_examples: 52 download_size: 2163493076 dataset_size: 2510511859.0 - config_name: Youtube_TNScrapped_V1_NoiseLess_VCA 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: 8973984541.0 num_examples: 364 download_size: 7561296937 dataset_size: 8973984541.0 - config_name: Youtube_TV_VCA 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: 1357183734.0 num_examples: 28 download_size: 1317232730 dataset_size: 1357183734.0 configs: - config_name: default default: true data_files: - split: train path: data/*/train/train-* - config_name: ApprendreLeTunisienVCA data_files: - split: train path: data/ApprendreLeTunisien_VCA/train/train-* - config_name: MASC_NoiseLess data_files: - split: train path: data/MASC_NoiseLess/train/train-* - config_name: MASC_NoiseLess_VCA data_files: - split: train path: data/MASC_NoiseLess_VCA/train/train-* - config_name: OneStoryVCA data_files: - split: train path: data/OneStory_VCA/train/train-* - config_name: TunSwitchCS_VCA data_files: - split: train path: data/TunSwitchCS_VCA/train/train-* - config_name: TunSwitchTO_VCA data_files: - split: train path: data/TunSwitchTO_VCA/train/train-* - config_name: Youtube_AbdelAzizErwi_VCA data_files: - split: train path: data/Youtube_AbdelAzizErwi_VCA/train/train-* - config_name: Youtube_BayariBilionaireVCA data_files: - split: train path: data/Youtube_BayariBilionaire_VCA/train/train-* - config_name: Youtube_DiwanFM_VCA data_files: - split: train path: data/Youtube_DiwanFM_VCA/train/train-* - config_name: Youtube_HkeyetTounsiaMensia_VCA data_files: - split: train path: data/Youtube_HkeyetTounsiaMensia_VCA/train/train-* - config_name: Youtube_LobnaMajjedi_VCA data_files: - split: train path: data/Youtube_LobnaMajjedi_VCA/train/train-* - config_name: Youtube_MohamedKhammessi_VCA data_files: - split: train path: data/Youtube_MohamedKhammessi_VCA/train/train-* - config_name: Youtube_Shorts_VCA data_files: - split: train path: data/Youtube_Shorts_VCA/train/train-* - config_name: Youtube_TNScrapped_V1_NoiseLess data_files: - split: train path: data/Youtube_TNScrapped_V1_NoiseLess/train/train-* - config_name: Youtube_TNScrapped_V1_NoiseLess_VCA data_files: - split: train path: data/Youtube_TNScrapped_V1_NoiseLess_VCA/train/train-* - config_name: Youtube_TV_VCA data_files: - split: train path: data/Youtube_TV_VCA/train/train-* --- # LinTO DataSet Audio for Arabic Tunisian Augmented
*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](https://huggingface.co/linagora/linto-asr-ar-tn). * [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** is a dataset that builds on [**LinTO DataSet Audio for Arabic Tunisian**](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn), 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** 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 | | MASC_NoiseLess | 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 7s | 10h 58m 21s | 14 | 12775 | 92512 | 448987 | | Youtube_Shorts_VCA | 26h 26m 25s | 23h 45m 25s | 945 | 14154 | 201138 | 1021713 | | Youtube_TNScrapped_V1_NoiseLess | 4h 2m 9s | 2h 31m 05s | 52 | 2538 | 18777 | 92530 | | Youtube_TNScrapped_V1_NoiseLess_VCA | 28h 15m 1s | 17h 37m 36s | 364 | 17766 | 132587 | 642292 | | Youtube_TV_VCA | 4h 16m 16s | 3h 40m 56s | 28 | 4676 | 33376 | 311500 | | **TOTAL** | **373h 10m 28s** | **320h 36m 58s** | **61609** | **292257** | **2342336** | **11742108** | ### 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: ![Wave Interface](img.png) - 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") ``` 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** corpus, please cite this page: ```bibtex @misc{linagora2024Linto-tn, author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré}, title = {LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect}, year = {2024}, publisher = {HuggingFace}, journal = {HuggingFace}, howpublished = {\url{https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn}}, } ``` ```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} } ```