Datasets:
File size: 6,895 Bytes
0a8caf9 7ce0436 0a8caf9 7ce0436 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 db17dee c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 c640583 0a8caf9 7ce0436 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 |
---
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
**<div align="center">Preprint</div>**
<div align="center">Khai Le-Duc*, Tuyen Tran*,</div>
<div align="center">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,</div>
<div align="center">Nguyen X. Khanh**, Thanh Nguyen-Tang**</div>
<div align="center">*Equal contribution</div>
<div align="center">**Equal supervision</div>
* **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](https://github.com/leduckhai/MultiMed-ST).
> Please press ⭐ button and/or cite papers if you feel helpful.
* **GitHub:**
[https://github.com/leduckhai/MultiMed-ST](https://github.com/leduckhai/MultiMed-ST)
* **Citation:**
Please cite this paper: [https://arxiv.org/abs/2504.03546](https://arxiv.org/abs/2504.03546)
``` bibtex
@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](https://huggingface.co/datasets/leduckhai/MultiMed-ST)
Fine-tuned models: [HuggingFace models](https://huggingface.co/leduckhai/MultiMed-ST)
## 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]
``` |