Datasets:

tensorxt
/

ViMedCSS

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:

from datasets import load_dataset

dataset = load_dataset("tensorxt/ViMedCSS")
print(dataset)

Clone with Git LFS:

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:

@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",
}