---
language:
- th
license: cc-by-nc-sa-4.0
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: sentence
    dtype: string
  - name: thai_sentence
    dtype: string
  - name: dialect_type
    dtype: string
  - name: utterance
    dtype: string
  splits:
  - name: train
    num_bytes: 385893455.968
    num_examples: 24543
  download_size: 364746887
  dataset_size: 385893455.968
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Porjai-Thai-voice-dataset-khummuang
This corpus contains a officially split of 700 hours for Central Thai, and 40 hours for the three dialect each. The corpus is designed such that there are some parallel sentences between the dialects, making it suitable for Speech and Machine translation research.

Our demo ASR model can be found at https://www.cmkl.ac.th/research/porjai. The Thai Central data was collected using [Wang Data Market](https://www.wang.in.th/).

Since parts of this corpus are in the [ML-SUPERB](https://multilingual.superbbenchmark.org/) challenge, the test sets are not released in this github and would be released subsequently in ML-SUPERB.

The baseline models of our corpus are at:  
[Thai-central](https://huggingface.co./SLSCU/thai-dialect_thai-central_model)  
[Khummuang](https://huggingface.co./SLSCU/thai-dialect_khummuang_model)  
[Korat](https://huggingface.co./SLSCU/thai-dialect_korat_model)  
[Pattani](https://huggingface.co./SLSCU/thai-dialect_pattani_model)  

The Thai-dialect Corpus is licensed under [CC-BY-SA 4.0.](https://creativecommons.org/licenses/by-sa/4.0/)

# Acknowledgements
This dataset was created with support from the PMU-C grant (Thai Language Automatic Speech Recognition Interface for Community E-Commerce, C10F630122) and compute support from the Apex cluster team. Some evaluation data was donated by Wang.

# Citation
```
@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}
}
```