SEACrowd/vivos

VIVOS is a Vietnamese speech corpus consisting of 15 hours of recording speech prepared for Automatic Speech Recognition task. This speech corpus is collected by recording speech data from more than 50 native Vietnamese volunteers.

Languages

vie

Supported Tasks

Speech Recognition

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/vivos", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("vivos", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("vivos"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://zenodo.org/records/7068130

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Share Alike 4.0 (cc-by-sa-4.0)

Citation

If you are using the Vivos dataloader in your work, please cite the following:

@inproceedings{luong-vu-2016-non,
    title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
    author = "Luong, Hieu-Thi  and
      Vu, Hai-Quan",
    editor = "Murakami, Yohei  and
      Lin, Donghui  and
      Ide, Nancy  and
      Pustejovsky, James",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service
    Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for
    Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://aclanthology.org/W16-5207",
    pages = "51--55",
    abstract = "In this paper we describe a non-expert setup for Vietnamese speech recognition
    system using Kaldi toolkit. We collected a speech corpus over fifteen hours from about fifty
    Vietnamese native speakers and using it to test the feasibility of our setup. The essential
    linguistic components for the Automatic Speech Recognition (ASR) system was prepared basing
    on the written form of the language instead of expertise knowledge on linguistic and phonology
    as commonly seen in rich resource languages like English. The modeling of tones by integrating
    them into the phoneme and using the phonetic decision tree is also discussed. Experimental
    results showed this setup for ASR systems does yield competitive results while still have
    potentials for further improvements.",
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}