metadata
license: cc-by-nc-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 Khummuang 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 encoder.
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 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>)
| Micro CER | Macro CER | Survival CER | E-commerce WER | Micro WER | Macro WER | Survival WER | E-commerce WER |
|---|---|---|---|---|---|---|---|
| 5.35 | 5.65 | 6.29 | 5.02 | 7.53 | 8.73 | 11.38 | 6.09 |
Paper
@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}
}