|
|
--- |
|
|
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 |
|
|
--- |
|
|
|
|
|
<p align="center"> |
|
|
<img src="./MultiMedST_icon.png" alt="MultiMedST_icon" width="70"> |
|
|
</p> |
|
|
|
|
|
<h1 align="center">MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation</h1> |
|
|
|
|
|
|
|
|
<p align="center"> |
|
|
<a href="https://arxiv.org/abs/2504.03546"> |
|
|
<img src="https://img.shields.io/badge/Paper-arXiv%3A2504.03546-b31b1b?logo=arxiv&logoColor=white" alt="Paper"> |
|
|
</a> |
|
|
<a href="https://huggingface.co/datasets/leduckhai/MultiMed-ST"> |
|
|
<img src="https://img.shields.io/badge/Dataset-HuggingFace-blue?logo=huggingface&logoColor=white" alt="Dataset"> |
|
|
</a> |
|
|
<a href="https://huggingface.co/leduckhai/MultiMed-ST"> |
|
|
<img src="https://img.shields.io/badge/Models-HuggingFace-green?logo=huggingface&logoColor=white" alt="Models"> |
|
|
</a> |
|
|
<a href="https://github.com/leduckhai/MultiMed-ST/blob/main/LICENSE"> |
|
|
<img src="https://img.shields.io/badge/License-MIT-yellow" alt="License"> |
|
|
</a> |
|
|
<a href="https://github.com/leduckhai/MultiMed-ST/stargazers"> |
|
|
<img src="https://img.shields.io/github/stars/leduckhai/MultiMed-ST?style=social" alt="Stars"> |
|
|
</a> |
|
|
</p> |
|
|
|
|
|
<p align="center"> |
|
|
<strong>📘 EMNLP 2025</strong> |
|
|
</p> |
|
|
|
|
|
<p align="center"> |
|
|
<b>Khai Le-Duc*</b>, <b>Tuyen Tran*</b>, 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** |
|
|
</p> |
|
|
|
|
|
<p align="center"> |
|
|
<sub>*Equal contribution | **Equal supervision</sub> |
|
|
</p> |
|
|
|
|
|
--- |
|
|
|
|
|
> ⭐ **If you find this work useful, please consider starring the repo and citing our paper!** |
|
|
|
|
|
--- |
|
|
|
|
|
## 🧠 Abstract |
|
|
|
|
|
Multilingual speech translation (ST) in the **medical domain** enhances patient care by enabling effective communication across language barriers, alleviating workforce shortages, and improving diagnosis and treatment — especially in global health emergencies. |
|
|
|
|
|
In this work, we introduce **MultiMed-ST**, the *first large-scale multilingual medical speech translation dataset*, spanning **all translation directions** across **five languages**: |
|
|
🇻🇳 Vietnamese, 🇬🇧 English, 🇩🇪 German, 🇫🇷 French, 🇨🇳 Traditional & Simplified Chinese. |
|
|
|
|
|
With **290,000 samples**, *MultiMed-ST* represents: |
|
|
- 🧩 the **largest medical MT dataset** to date |
|
|
- 🌐 the **largest many-to-many multilingual ST dataset** across all domains |
|
|
|
|
|
We also conduct **the most comprehensive ST analysis in the field's history**, to our best knowledge, covering: |
|
|
- ✅ Empirical baselines |
|
|
- 🔄 Bilingual vs. multilingual study |
|
|
- 🧩 End-to-end vs. cascaded models |
|
|
- 🎯 Task-specific vs. multi-task seq2seq approaches |
|
|
- 🗣️ Code-switching analysis |
|
|
- 📊 Quantitative & qualitative error analysis |
|
|
|
|
|
All **code, data, and models** are publicly available: 👉 [**GitHub Repository**](https://github.com/leduckhai/MultiMed-ST) |
|
|
|
|
|
|
|
|
<p align="center"> |
|
|
<img src="./poster_MultiMed-ST_EMNLP2025.png" alt="poster_MultiMed-ST_EMNLP2025" width="85%"> |
|
|
</p> |
|
|
|
|
|
--- |
|
|
|
|
|
## 🧰 Repository Overview |
|
|
|
|
|
This repository provides scripts for: |
|
|
|
|
|
- 🎙️ **Automatic Speech Recognition (ASR)** |
|
|
- 🌍 **Machine Translation (MT)** |
|
|
- 🔄 **Speech Translation (ST)** — both **cascaded** and **end-to-end** seq2seq models |
|
|
|
|
|
It includes: |
|
|
|
|
|
- ⚙️ Model preparation & fine-tuning |
|
|
- 🚀 Training & inference scripts |
|
|
- 📊 Evaluation & benchmarking utilities |
|
|
|
|
|
--- |
|
|
|
|
|
## 📦 Dataset & Models |
|
|
|
|
|
- **Dataset:** [🤗 Hugging Face Dataset](https://huggingface.co/datasets/leduckhai/MultiMed-ST) |
|
|
- **Fine-tuned Models:** [🤗 Hugging Face Models](https://huggingface.co/leduckhai/MultiMed-ST) |
|
|
|
|
|
You can explore and download all fine-tuned models for **MultiMed-ST** directly from our Hugging Face repository: |
|
|
|
|
|
<details> |
|
|
<summary><b>🔹 Whisper ASR Fine-tuned Models (Click to expand) </b></summary> |
|
|
|
|
|
| Language | Model Link | |
|
|
|-----------|------------| |
|
|
| Chinese | [whisper-small-chinese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-chinese) | |
|
|
| English | [whisper-small-english](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-english) | |
|
|
| French | [whisper-small-french](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-french) | |
|
|
| German | [whisper-small-german](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-german) | |
|
|
| Multilingual | [whisper-small-multilingual](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-multilingual) | |
|
|
| Vietnamese | [whisper-small-vietnamese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/asr/whisper-small-vietnamese) | |
|
|
|
|
|
</details> |
|
|
|
|
|
<details> |
|
|
<summary><b>🔹 LLaMA-based MT Fine-tuned Models (Click to expand) </b></summary> |
|
|
|
|
|
| Source → Target | Model Link | |
|
|
|------------------|------------| |
|
|
| Chinese → English | [llama_Chinese_English](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Chinese_English) | |
|
|
| Chinese → French | [llama_Chinese_French](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Chinese_French) | |
|
|
| Chinese → German | [llama_Chinese_German](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Chinese_German) | |
|
|
| Chinese → Vietnamese | [llama_Chinese_Vietnamese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Chinese_Vietnamese) | |
|
|
| English → Chinese | [llama_English_Chinese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_English_Chinese) | |
|
|
| English → French | [llama_English_French](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_English_French) | |
|
|
| English → German | [llama_English_German](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_English_German) | |
|
|
| English → Vietnamese | [llama_English_Vietnamese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_English_Vietnamese) | |
|
|
| French → Chinese | [llama_French_Chinese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_French_Chinese) | |
|
|
| French → English | [llama_French_English](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_French_English) | |
|
|
| French → German | [llama_French_German](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_French_German) | |
|
|
| French → Vietnamese | [llama_French_Vietnamese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_French_Vietnamese) | |
|
|
| German → Chinese | [llama_German_Chinese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_German_Chinese) | |
|
|
| German → English | [llama_German_English](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_German_English) | |
|
|
| German → French | [llama_German_French](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_German_French) | |
|
|
| German → Vietnamese | [llama_German_Vietnamese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_German_Vietnamese) | |
|
|
| Vietnamese → Chinese | [llama_Vietnamese_Chinese](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Vietnamese_Chinese) | |
|
|
| Vietnamese → English | [llama_Vietnamese_English](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Vietnamese_English) | |
|
|
| Vietnamese → French | [llama_Vietnamese_French](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Vietnamese_French) | |
|
|
| Vietnamese → German | [llama_Vietnamese_German](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/llama_Vietnamese_German) | |
|
|
|
|
|
</details> |
|
|
|
|
|
<details> |
|
|
<summary><b>🔹 m2m100_418M MT Fine-tuned Models (Click to expand) </b></summary> |
|
|
|
|
|
| Source → Target | Model Link | |
|
|
|------------------|------------| |
|
|
| de → en | [m2m100_418M-finetuned-de-to-en](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-de-to-en) | |
|
|
| de → fr | [m2m100_418M-finetuned-de-to-fr](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-de-to-fr) | |
|
|
| de → vi | [m2m100_418M-finetuned-de-to-vi](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-de-to-vi) | |
|
|
| de → zh | [m2m100_418M-finetuned-de-to-zh](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-de-to-zh) | |
|
|
| en → de | [m2m100_418M-finetuned-en-to-de](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-en-to-de) | |
|
|
| en → fr | [m2m100_418M-finetuned-en-to-fr](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-en-to-fr) | |
|
|
| en → vi | [m2m100_418M-finetuned-en-to-vi](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-en-to-vi) | |
|
|
| en → zh | [m2m100_418M-finetuned-en-to-zh](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-en-to-zh) | |
|
|
| fr → de | [m2m100_418M-finetuned-fr-to-de](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-fr-to-de) | |
|
|
| fr → en | [m2m100_418M-finetuned-fr-to-en](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-fr-to-en) | |
|
|
| fr → vi | [m2m100_418M-finetuned-fr-to-vi](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-fr-to-vi) | |
|
|
| fr → zh | [m2m100_418M-finetuned-fr-to-zh](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-fr-to-zh) | |
|
|
| vi → de | [m2m100_418M-finetuned-vi-to-de](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-vi-to-de) | |
|
|
| vi → en | [m2m100_418M-finetuned-vi-to-en](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-vi-to-en) | |
|
|
| vi → fr | [m2m100_418M-finetuned-vi-to-fr](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-vi-to-fr) | |
|
|
| vi → zh | [m2m100_418M-finetuned-vi-to-zh](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-vi-to-zh) | |
|
|
| zh → de | [m2m100_418M-finetuned-zh-to-de](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-zh-to-de) | |
|
|
| zh → en | [m2m100_418M-finetuned-zh-to-en](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-zh-to-en) | |
|
|
| zh → fr | [m2m100_418M-finetuned-zh-to-fr](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-zh-to-fr) | |
|
|
| zh → vi | [m2m100_418M-finetuned-zh-to-vi](https://huggingface.co/leduckhai/MultiMed-ST/tree/main/m2m100_418M-finetuned-zh-to-vi) | |
|
|
|
|
|
</details> |
|
|
|
|
|
--- |
|
|
|
|
|
## 👨💻 Core Developers |
|
|
|
|
|
1. **Khai Le-Duc** |
|
|
|
|
|
University of Toronto, Canada |
|
|
|
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📧 [duckhai.le@mail.utoronto.ca](mailto:duckhai.le@mail.utoronto.ca) |
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🔗 [https://github.com/leduckhai](https://github.com/leduckhai) |
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2. **Tuyen Tran**: 📧 [tuyencbt@gmail.com](mailto:tuyencbt@gmail.com) |
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Hanoi University of Science and Technology, Vietnam |
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3. **Nguyen Kim Hai Bui**: 📧 [htlulem185@gmail.com](mailto:htlulem185@gmail.com) |
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Eötvös Loránd University, Hungary |
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## 🧾 Citation |
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If you use our dataset or models, please cite: |
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📄 [arXiv:2504.03546](https://arxiv.org/abs/2504.03546) |
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```bibtex |
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@inproceedings{le2025multimedst, |
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title={MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation}, |
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author={Le-Duc, Khai and Tran, Tuyen and Tat, Bach Phan and Bui, Nguyen Kim Hai and Anh, Quan Dang and Tran, Hung-Phong and Nguyen, Thanh Thuy and Nguyen, Ly and Phan, Tuan Minh and Tran, Thi Thu Phuong and others}, |
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booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing}, |
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pages={11838--11963}, |
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year={2025} |
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} |
