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wannaphong/wav2vec2-large-xlsr-53-th-cv8-deepcut

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face

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Thai Wav2Vec2 with CommonVoice V8 (deepcut tokenizer) + language model

This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in airesearch/wav2vec2-large-xlsr-53-th. It was finetune wav2vec2-large-xlsr-53.

Model description

—Technical report: Thai Wav2Vec2.0 with CommonVoice V8

Datasets

It is increase new data from The Common Voice V8 dataset to Common Voice V7 dataset or remove all data in Common Voice V7 dataset before split Common Voice V8 then add CommonVoice V7 dataset back to dataset.

It use ekapolc/Thai_commonvoice_split script for split Common Voice dataset.

Models

This model was finetune wav2vec2-large-xlsr-53 model with Thai Common Voice V8 dataset and It use pre-tokenize with deepcut.tokenize.

Evaluation

Test with CommonVoice V8 Testset

Model	WER by newmm (%)	WER by deepcut (%)	CER
AIResearch.in.th and PyThaiNLP	17.414503	11.923089	3.854153
wav2vec2 with deepcut	16.354521	11.424476	3.684060
wav2vec2 with newmm	16.698299	11.436941	3.737407
wav2vec2 with deepcut + language model	12.630260	9.613886	3.292073
wav2vec2 with newmm + language model	12.583706	9.598305	3.276610

Test with CommonVoice V7 Testset (same test by CV V7)

Model	WER by newmm (%)	WER by deepcut (%)	CER
AIResearch.in.th and PyThaiNLP	13.936698	9.347462	2.804787
wav2vec2 with deepcut	12.776381	8.773006	2.628882
wav2vec2 with newmm	12.750596	8.672616	2.623341
wav2vec2 with deepcut + language model	9.940050	7.423313	2.344940
wav2vec2 with newmm + language model	9.559724	7.339654	2.277071

This is use same testset from https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th.

Links:

—GitHub Dataset: https://github.com/wannaphong/thai_commonvoice_dataset
—Technical report: Thai Wav2Vec2.0 with CommonVoice V8

BibTeX entry and citation info

@misc{phatthiyaphaibun2022thai,
      title={Thai Wav2Vec2.0 with CommonVoice V8}, 
      author={Wannaphong Phatthiyaphaibun and Chompakorn Chaksangchaichot and Peerat Limkonchotiwat and Ekapol Chuangsuwanich and Sarana Nutanong},
      year={2022},
      eprint={2208.04799},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}