metadata

license: cc-by-sa-4.0
language:
  - th
metrics:
  - cer
  - wer
library_name: espnet
pipeline_tag: automatic-speech-recognition

Model Card for Model ID

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

The training recipe was based on wsj recipe in espnet.

Model Description

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

This model trained on Thai-central for being the supervised pre-trained model in transfer-based curriculum learning experiment.

you can demo on colab with this link. (Free google colab cannot inferences > 3 seconds of speech.)

Evaluation

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 word_tokenize

tokenized_sentence_list = word_tokenize(<your_sentence>)

CER = 2.0

WER = 6.9

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}
}