Datasets:
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---
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},
}
``` |