Datasets:
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---
license: cc-by-4.0
dataset_info:
features:
- name: segment_id
dtype: string
- name: audio
dtype:
audio:
decode: false
- name: duration_seconds
dtype: int64
- name: segment_text
dtype: string
- name: cs_terms_list
dtype: string
- name: cs_terms_count
dtype: int64
- name: topic
dtype: string
- name: original_video_link
dtype: string
- name: original_video_title
dtype: string
- name: start_time
dtype: string
- name: end_time
dtype: string
splits:
- name: train
num_bytes: 14582022075
num_examples: 11832
- name: validation
num_bytes: 2139515036
num_examples: 1714
- name: test
num_bytes: 2026901460
num_examples: 1614
- name: hard
num_bytes: 814798996
num_examples: 658
download_size: 18312886260
dataset_size: 19563237567
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: hard
path: data/hard-*
task_categories:
- automatic-speech-recognition
language:
- vi
tags:
- medical
- code-switching
---
# 🩺 ViMedCSS: Vietnamese Medical Code-Switching Speech Dataset
## 📖 Overview
ViMedCSS is a Vietnamese medical speech dataset for code-switching ASR, where each utterance contains at least one non-Vietnamese (mainly English) medical term embedded in Vietnamese speech.
## 📊 Dataset Statistics
### Split Statistics (from `ViMedCSS-Metadata`)
| Split | # Rows | Duration (hours) | Avg duration (s) | Total CS terms |
|---|---:|---:|---:|---:|
| train | 11,832 | 24.30 | 7.39 | 12,314 |
| validation | 1,714 | 3.57 | 7.49 | 1,814 |
| test | 1,614 | 3.39 | 7.56 | 1,695 |
| hard | 658 | 1.38 | 7.57 | 758 |
| **Total** | **15,818** | **32.64** | **7.43** | **16,581** |
### Topic Statistics (from `ViMedCSS-Metadata`)
| Topic | # Rows | Duration (hours) | Total CS terms |
|---|---:|---:|---:|
| Medical Sciences | 6,836 | 14.68 | 7,459 |
| Pathology & Pathogens | 4,827 | 10.00 | 4,951 |
| Treatments | 1,969 | 3.80 | 1,985 |
| Nutrition | 1,155 | 2.14 | 1,155 |
| Diagnostics | 1,031 | 2.02 | 1,031 |
## 🧾 Data Fields
Each row in metadata corresponds to one segment audio file, where:
- `segment_id` maps to `segment_id.wav` (for example: `Med_CS-100-17` -> `Med_CS-100-17.wav`)
Main fields:
- `segment_id`: utterance identifier
- `duration_seconds`: utterance duration
- `segment_text`: Vietnamese transcript containing code-switched term(s)
- `cs_terms_list`: semicolon-separated code-switched terms
- `cs_terms_count`: number of code-switched terms in the utterance
- `topic` (or `Topic` in one CSV): medical topic label
- `original_video_link`: source video URL
- `original_video_title`: source video title
- `start_time`, `end_time`: segment boundaries in source audio/video
When loaded from Hugging Face, an `audio` column is available with waveform bytes/path in the standard 🤗 Datasets `Audio` format.
## 🔽 How to Load
Load directly with 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset("tensorxt/ViMedCSS")
print(dataset)
```
Clone with Git LFS:
```bash
git lfs install
git clone https://huggingface.co/datasets/tensorxt/ViMedCSS
```
## 📝 Notes
- The paper reports the full corpus statistics (34.57h).
- The `hard` split is intended for evaluating rare/unseen code-switched medical terms, following the paper’s benchmark setup.
## 📜 License
The paper states that data are collected from publicly available YouTube content for research purposes, and the medical dictionary resource used in construction is under institutional intellectual property licensing.
Please verify usage rights for your setting before redistribution or commercial use.
## 🙏 Citation
If you use ViMedCSS, please cite:
```bibtex
@inproceedings{nguyen-etal-2026-vimedcss,
title = "{V}i{M}ed{CSS}: A Vietnamese Medical Code-Switching Speech Dataset \& Benchmark",
author = "Tung X. Nguyen, Nhu Vo, Giang-Son Nguyen, Duy Mai Hoang, Chien Dinh Huynh, Inigo Jauregi Unanue, Massimo Piccardi, Wray Buntine, Dung D. Le",
booktitle = "Proceedings of the 2026 Language Resources and Evaluation Conference (LREC 2026)",
year = "2026",
}
``` |