|
--- |
|
viewer: true |
|
|
|
dataset_info: |
|
- config_name: Chinese |
|
features: |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: text |
|
dtype: string |
|
- name: duration |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 182566135.142 |
|
num_examples: 1242 |
|
- name: eval |
|
num_bytes: 12333509.0 |
|
num_examples: 91 |
|
- name: test |
|
num_bytes: 33014034.0 |
|
num_examples: 225 |
|
download_size: 227567289 |
|
dataset_size: 227913678.142 |
|
- config_name: English |
|
features: |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: text |
|
dtype: string |
|
- name: duration |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 2789314997.152 |
|
num_examples: 25512 |
|
- name: eval |
|
num_bytes: 299242087.632 |
|
num_examples: 2816 |
|
- name: test |
|
num_bytes: 553873172.749 |
|
num_examples: 4751 |
|
download_size: 3627859275 |
|
dataset_size: 3642430257.533 |
|
- config_name: French |
|
features: |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: text |
|
dtype: string |
|
- name: duration |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 168642145.231 |
|
num_examples: 1403 |
|
- name: eval |
|
num_bytes: 5164908.0 |
|
num_examples: 42 |
|
- name: test |
|
num_bytes: 42780388.0 |
|
num_examples: 344 |
|
download_size: 216118671 |
|
dataset_size: 216587441.231 |
|
- config_name: German |
|
features: |
|
- name: audio |
|
dtype: audio |
|
- name: text |
|
dtype: string |
|
- name: duration |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 181312217.029 |
|
num_examples: 1443 |
|
- name: test |
|
num_bytes: 137762006.256 |
|
num_examples: 1091 |
|
- name: eval |
|
num_bytes: 35475098.0 |
|
num_examples: 287 |
|
download_size: 354494147 |
|
dataset_size: 354549321.285 |
|
- config_name: Vietnamese |
|
features: |
|
- name: audio |
|
dtype: audio |
|
- name: text |
|
dtype: string |
|
- name: duration |
|
dtype: float64 |
|
splits: |
|
- name: train |
|
num_bytes: 56584901.453 |
|
num_examples: 2773 |
|
- name: test |
|
num_bytes: 69598082.31 |
|
num_examples: 3437 |
|
- name: dev |
|
num_bytes: 57617298.896 |
|
num_examples: 2912 |
|
download_size: 181789393 |
|
dataset_size: 183800282.659 |
|
configs: |
|
- config_name: Chinese |
|
data_files: |
|
- split: train |
|
path: Chinese/train-* |
|
- split: eval |
|
path: Chinese/eval-* |
|
- split: test |
|
path: Chinese/test-* |
|
- config_name: English |
|
data_files: |
|
- split: train |
|
path: English/train-* |
|
- split: eval |
|
path: English/eval-* |
|
- split: test |
|
path: English/test-* |
|
- config_name: French |
|
data_files: |
|
- split: train |
|
path: French/train-* |
|
- split: eval |
|
path: French/eval-* |
|
- split: test |
|
path: French/test-* |
|
- config_name: German |
|
data_files: |
|
- split: train |
|
path: German/train-* |
|
- split: test |
|
path: German/test-* |
|
- split: eval |
|
path: German/eval-* |
|
- config_name: Vietnamese |
|
data_files: |
|
- split: train |
|
path: Vietnamese/train-* |
|
- split: test |
|
path: Vietnamese/test-* |
|
- split: dev |
|
path: Vietnamese/dev-* |
|
--- |
|
|
|
# MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder |
|
|
|
## Description: |
|
Multilingual automatic speech recognition (ASR) in the medical domain serves as a foundational task for various downstream applications such as speech translation, spoken language understanding, and voice-activated assistants. |
|
This technology 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 introduce *MultiMed*, a collection of small-to-large end-to-end ASR models for the medical domain, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese, together with the corresponding real-world ASR dataset. |
|
To our best knowledge, *MultiMed* stands as **the largest and the first multilingual medical ASR dataset**, in terms of total duration, number of speakers, diversity of diseases, recording conditions, speaker roles, unique medical terms, accents, and ICD-10 codes. |
|
|
|
|
|
Please cite this paper: [https://arxiv.org/abs/2409.14074](https://arxiv.org/abs/2409.14074) |
|
|
|
@inproceedings{le2024multimed, |
|
title={MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder}, |
|
author={Le-Duc, Khai and Phan, Phuc and Pham, Tan-Hanh and Tat, Bach Phan and Ngo, Minh-Huong and Hy, Truong-Son}, |
|
journal={arXiv preprint arXiv:2409.14074}, |
|
year={2024} |
|
} |
|
|
|
To load labeled data, please refer to our [HuggingFace](https://huggingface.co/datasets/leduckhai/MultiMed), [Paperswithcodes](https://paperswithcode.com/dataset/multimed). |
|
|
|
## Contact: |
|
|
|
If any links are broken, please contact me for fixing! |
|
|
|
Thanks [Phan Phuc](https://www.linkedin.com/in/pphuc/) for dataset viewer <3 |
|
|
|
``` |
|
Le Duc Khai |
|
University of Toronto, Canada |
|
Email: [email protected] |
|
GitHub: https://github.com/leduckhai |
|
``` |