Datasets:
metadata
viewer: true
dataset_info:
- config_name: Chinese
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration
dtype: float64
- name: text
dtype: string
- name: traditional_chinese
dtype: string
- name: English
dtype: string
- name: Vietnamese
dtype: string
- name: French
dtype: string
- name: German
dtype: string
splits:
- name: train
num_examples: 1242
- name: eval
num_examples: 91
- name: corrected.test
num_examples: 225
- config_name: English
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration
dtype: float64
- name: text
dtype: string
- name: Vietnamese
dtype: string
- name: Chinese
dtype: string
- name: traditional_chinese
dtype: string
- name: French
dtype: string
- name: German
dtype: string
- name: source
dtype: string
- name: link
dtype: string
- name: type
dtype: string
- name: topic
dtype: string
- name: icd-10 code
dtype: string
- name: speaker
dtype: string
- name: role
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
splits:
- name: train
num_examples: 25512
- name: eval
num_examples: 2816
- name: corrected.test
num_examples: 4751
- config_name: French
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration
dtype: float64
- name: text
dtype: string
- name: English
dtype: string
- name: Vietnamese
dtype: string
- name: Chinese
dtype: string
- name: traditional_chinese
dtype: string
- name: German
dtype: string
splits:
- name: train
num_examples: 1403
- name: eval
num_examples: 42
- name: corrected.test
num_examples: 344
- config_name: German
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration
dtype: float64
- name: text
dtype: string
- name: English
dtype: string
- name: Vietnamese
dtype: string
- name: Chinese
dtype: string
- name: traditional_chinese
dtype: string
- name: French
dtype: string
splits:
- name: train
num_examples: 1443
- name: eval
num_examples: 287
- name: corrected.test
num_examples: 1091
- config_name: Vietnamese
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: duration
dtype: float64
- name: text
dtype: string
- name: English
dtype: string
- name: Chinese
dtype: string
- name: traditional_chinese
dtype: string
- name: French
dtype: string
- name: German
dtype: string
splits:
- name: train
num_examples: 4548
- name: eval
num_examples: 1137
- name: corrected.test
num_examples: 3437
configs:
- config_name: Chinese
data_files:
- split: train
path: chinese/train-*
- split: eval
path: chinese/eval-*
- split: corrected.test
path: chinese/corrected.test-*
- config_name: English
data_files:
- split: train
path: english/train-*
- split: eval
path: english/eval-*
- split: corrected.test
path: english/corrected.test-*
- config_name: French
data_files:
- split: train
path: french/train-*
- split: eval
path: french/eval-*
- split: corrected.test
path: french/corrected.test-*
- config_name: German
data_files:
- split: train
path: german/train-*
- split: eval
path: german/eval-*
- split: corrected.test
path: german/corrected.test-*
- config_name: Vietnamese
data_files:
- split: train
path: vietnamese/train-*
- split: eval
path: vietnamese/eval-*
- split: corrected.test
path: vietnamese/corrected.test-*
task_categories:
- translation
- automatic-speech-recognition
language:
- vi
- en
- de
- zh
- fr
license: mit
tags:
- medical
MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation
Preprint
Khai Le-Duc*, Tuyen Tran*,
Bach Phan Tat, Nguyen Kim Hai Bui, Quan Dang, Hung-Phong Tran, Thanh-Thuy Nguyen, Ly Nguyen, Tuan-Minh Phan, Thi Thu Phuong Tran, Chris Ngo,
Nguyen X. Khanh**, Thanh Nguyen-Tang**
*Equal contribution
**Equal supervision
- Abstract: Multilingual speech translation (ST) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMed-ST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, Traditional Chinese and Simplified Chinese, together with the models. With 290,000 samples, our dataset is the largest medical machine translation (MT) dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most extensive analysis study in ST research to date, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence (seq2seq) comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: https://github.com/leduckhai/MultiMed-ST.
Please press ⭐ button and/or cite papers if you feel helpful.
Citation: Please cite this paper: https://arxiv.org/abs/2504.03546
@article{le2025multimedst,
title={MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation},
author={Le-Duc, Khai and Tran, Tuyen and Tat, Bach Phan and Bui, Nguyen Kim Hai and Dang, Quan and Tran, Hung-Phong and Nguyen, Thanh-Thuy and Nguyen, Ly and Phan, Tuan-Minh and Tran, Thi Thu Phuong and others},
journal={arXiv preprint arXiv:2504.03546},
year={2025}
}
Dataset and Models:
Dataset: HuggingFace dataset
Fine-tuned models: HuggingFace models
Contact:
Core developers:
Khai Le-Duc
University of Toronto, Canada
Email: [email protected]
GitHub: https://github.com/leduckhai
Tuyen Tran
Hanoi University of Science and Technology, Vietnam
Email: [email protected]
Bui Nguyen Kim Hai
Eötvös Loránd University, Hungary
Email: [email protected]