---
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: A Vietnamese Medical Code-Switching Speech Dataset (LREC 2026)

## 📖 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 the paper: https://arxiv.org/abs/2602.12911

```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",
}
```