SEACrowd/oil

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The declared licence (cc-by-nc-nd-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-nd-4.0, from the tagEvidence: README.md at 61807ca
Licence files: LICENSE, detected as cc-by-nc-nd-4.0Evidence: LICENSE at 61807ca
Declared sources: None listed in the card metadata
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Citation text from the card

@inproceedings{maxwelll-smith-foley-2023-automated,
title = "Automated speech recognition of {I}ndonesian-{E}nglish language lessons on {Y}ou{T}ube using transfer learning",
author = "Maxwell-Smith, Zara and Foley, Ben",
editor = "Serikov, Oleg
        and Voloshina, Ekaterina
        and Postnikova, Anna
        and Klyachko, Elena
        and Vylomova, Ekaterina
        and Shavrina, Tatiana
        and Le Ferrand, Eric
        and Malykh, Valentin
        and Tyers, Francis
        and Arkhangelskiy, Timofey
        and Mikhailov, Vladislav",
    booktitle = "Proceedings of the Second Workshop on NLP Applications to Field Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.fieldmatters-1.1",
    doi = "10.18653/v1/2023.fieldmatters-1.1",
    pages = "1--16",
    abstract = "Experiments to fine-tune large multilingual models with limited data from a specific domain or setting has potential
    to improve automatic speech recognition (ASR) outcomes. This paper reports on the use of the Elpis ASR pipeline to fine-tune two
    pre-trained base models, Wav2Vec2-XLSR-53 and Wav2Vec2-Large-XLSR-Indonesian, with various mixes of data from 3 YouTube channels
    teaching Indonesian with English as the language of instruction. We discuss our results inferring new lesson audio (22-46%
    word error rate) in the context of speeding data collection in

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dataset_licence({ "id": "SEACrowd/oil" })
signal: "restrictive"
stated_licence: "cc-by-nc-nd-4.0"
sources: 0