--- 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 Khummuang 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 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](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 Tokenizer def get_tokenizer(vocab): custom_vocab = set(vocab) custom_tokenizer = Tokenizer(custom_vocab, engine='newmm') return custom_tokenizer with open(,'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() ``` |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 [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} } ```