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--- |
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language: |
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- multilingual |
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- ain |
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license: apache-2.0 |
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--- |
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## Wav2Vec2-Large-XLSR-53 pretrained on Ainu language data |
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This is a [wav2vec-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) model adapted for the Ainu language by performing continued pretraining for 100k steps on 234 hours of speech data in Hokkaido Ainu and Sakhalin Ainu. |
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For details, please refer to the [paper](https://arxiv.org/abs/2301.07295). |
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A model fine-tuned for automatic transcription of Sakhalin Ainu is also available: [wav2vec2-large-xlsr-53-ain-sakh](https://huggingface.co./karolnowakowski/wav2vec2-large-xlsr-53-ain-sakh). |
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## Citation |
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When using the model please cite the following paper: |
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```bibtex |
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@article{NOWAKOWSKI2023103148, |
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title = {Adapting multilingual speech representation model for a new, underresourced language through multilingual fine-tuning and continued pretraining}, |
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journal = {Information Processing & Management}, |
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volume = {60}, |
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number = {2}, |
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pages = {103148}, |
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year = {2023}, |
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issn = {0306-4573}, |
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doi = {https://doi.org/10.1016/j.ipm.2022.103148}, |
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url = {https://www.sciencedirect.com/science/article/pii/S0306457322002497}, |
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author = {Karol Nowakowski and Michal Ptaszynski and Kyoko Murasaki and Jagna Nieuważny}, |
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keywords = {Automatic speech transcription, ASR, Wav2vec 2.0, Pretrained transformer models, Speech representation models, Cross-lingual transfer, Language documentation, Endangered languages, Underresourced languages, Sakhalin Ainu}, |
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abstract = {In recent years, neural models learned through self-supervised pretraining on large scale multilingual text or speech data have exhibited promising results for underresourced languages, especially when a relatively large amount of data from related language(s) is available. While the technology has a potential for facilitating tasks carried out in language documentation projects, such as speech transcription, pretraining a multilingual model from scratch for every new language would be highly impractical. We investigate the possibility for adapting an existing multilingual wav2vec 2.0 model for a new language, focusing on actual fieldwork data from a critically endangered tongue: Ainu. Specifically, we (i) examine the feasibility of leveraging data from similar languages also in fine-tuning; (ii) verify whether the model’s performance can be improved by further pretraining on target language data. Our results show that continued pretraining is the most effective method to adapt a wav2vec 2.0 model for a new language and leads to considerable reduction in error rates. Furthermore, we find that if a model pretrained on a related speech variety or an unrelated language with similar phonological characteristics is available, multilingual fine-tuning using additional data from that language can have positive impact on speech recognition performance when there is very little labeled data in the target language.} |
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} |
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``` |
