README.md

---
license: cc-by-sa-4.0
language:
- th
tags:
- speech-recognition
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: sentence
    dtype: string
  - name: speaker_id
    dtype: string
  - name: mic
    dtype: string
  - name: duration
    dtype: float64
  splits:
  - name: train
    num_bytes: 8212128894.78
    num_examples: 120245
  - name: validation
    num_bytes: 1296622162.01
    num_examples: 13090
  - name: test
    num_bytes: 1623791447.32
    num_examples: 27580
  download_size: 13180732521
  dataset_size: 11132542504.109999
---

# LOTUSDIS

## Dataset Description

## How to use

You can easily load the dataset using the 🤗 `datasets` library. The dataset can be loaded and prepared with a single line of Python code:
```python
from datasets import load_dataset
lotus_dis = load_dataset("nectec/LOTUSDIS", split="train")
```

To iterate through the dataset without downloading it entirely, you can use streaming mode:
```python
from datasets import load_dataset
lotus_dis = load_dataset("nectec/LOTUSDIS", split="train", streaming=True)
print(next(iter(lotus_dis)))
```

Learn more about how to load and prepare audio datasets in the [Hugging Face Audio Datasets tutorial](https://huggingface.co/blog/audio-datasets).

Full meeting session resources:
- Audio files: [Download here](https://drive.google.com/file/d/1ofw99Y5W1p8f1DSaIbJkS0xWtuTI2Hrc/view)
- Annotation files (TextGrid): [Download here](https://drive.google.com/file/d/14fMv_X_8sGDPGbnU-hpJ85Mug43AHlgO/view)

## Citation
```
@misc{tipaksorn2025lotusdisthaifarfieldmeeting,
      title={LOTUSDIS: A Thai far-field meeting corpus for robust conversational ASR}, 
      author={Pattara Tipaksorn and Sumonmas Thatphithakkul and Vataya Chunwijitra and Kwanchiva Thangthai},
      year={2025},
      eprint={2509.18722},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.18722}, 
}
```