thai-dialect_khummuang_model

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This is the baseline model of Khummuang in Thai-dialect corpus.

The training recipe was based on wsj recipe in espnet.

Model Description

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This model is a Hybrid CTC/Attention model with pre-trained HuBERT encoder.

The model was pre-trained on Thai-central, Khummuang, Korat, and Pattani and fine-tuned on Khummuang, Korat, and Pattani. (Experiment 3 in the paper)

We provide some demo code to do inference with this model architecture on colab here. (Code is for Thai-Central. Please select the correct model accordingly.)

Evaluation

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For evaluation, the metrics are CER and WER. before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in PyThaiNLP

In this reposirity, we also provide the vocabulary for building the newmm tokenizer using this script:

from pythainlp import Tokenizer

def get_tokenizer(vocab):

    custom_vocab = set(vocab)
    custom_tokenizer = Tokenizer(custom_vocab, engine='newmm')
    return custom_tokenizer

with open(<vocab_path>,'r',encoding='utf-8') as f:
        vocab = []
        for line in f.readlines():
            vocab.append(line.strip())

custom_tokenizer = get_tokenizer(vocab)

tokenized_sentence_list = custom_tokenizer.word_tokenize(<your_sentence>)

The CER and WER results on test set are:

<!-- |8.08|11.51|17.39|5.63|12.18|16.65|25.58|7.72| --> <!-- |18.17|22.38|31.01|13.75|31.74|37.68|50.54|24.82| -->

Acknowledgement

We would like to thank the PMU-C grant (Thai Language Automatic Speech Recognition Interface for Community E-Commerce, C10F630122) for the support of this research. We also would like to acknowledge the Apex compute cluster team which provides compute support for this project.

Paper

Thai Dialect Corpus and Transfer-based Curriculum Learning Investigation for Dialect Automatic Speech Recognition

@inproceedings{suwanbandit23_interspeech,
  author={Artit Suwanbandit and Burin Naowarat and Orathai Sangpetch and Ekapol Chuangsuwanich},
  title={{Thai Dialect Corpus and Transfer-based Curriculum Learning Investigation for Dialect Automatic Speech Recognition}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={4069--4073},
  doi={10.21437/Interspeech.2023-1828}
}