README.md

---
license: cc-by-sa-4.0
language:
- th
metrics:
- cer
- wer
library_name: espnet
pipeline_tag: automatic-speech-recognition
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is the baseline model of Thai-central in [Thai-dialect corpus](https://github.com/SLSCU/thai-dialect-corpus).

The training recipe was based on wsj recipe in [espnet](https://github.com/espnet/espnet/).



### Model Description

<!-- Provide a longer summary of what this model is. -->

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](https://colab.research.google.com/drive/1stltGdpG9OV-sCl9QgkvEXZV7fGB2Ixe?usp=sharing).


## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

For evaluation, the metrics are CER and WER. before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)

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

```python
from pythainlp import word_tokenize

tokenized_sentence_list = word_tokenize(<your_sentence>)
```

The CER and WER results on test set are:

CER = 2.0

WER = 6.9

## Paper

[Thai Dialect Corpus and Transfer-based Curriculum Learning Investigation for Dialect Automatic Speech Recognition](https://www.isca-speech.org/archive/pdfs/interspeech_2023/suwanbandit23_interspeech.pdf)
```
@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}
}
```