datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ViMedCSS-Cop
🩺 ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset (LREC 2026)
📖 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… See the full description on the dataset page: https://huggingface.co/datasets/shannonnonshan/ViMedCSS-Cop.ViMD_chunked_10s
ViMD Chunked 10s — 16kHz
Preprocessed from ViMD (Nguyen et al., EMNLP 2024).
Preprocessing
Resample: 44.1kHz -> 16kHz mono
Chunking: each audio is split into consecutive NON-OVERLAPPING segments
of at most 10 seconds. ALL segments are kept, including the final
remainder (no minimum length filter). 1 original file -> ceil(len/10s) samples.
Splits: original ViMD train/valid/test kept (speaker-exclusive).
Segments of the same file always stay in the same split.… See the full description on the dataset page: https://huggingface.co/datasets/tannhoo06/ViMD_chunked_10s.ViMD_preprocessing
ViMD Truncated 10s — 16kHz
Preprocessed version of ViMD (Nguyen et al., EMNLP 2024) for Dialect Identification.
Preprocessing applied
Resample: 44.1kHz (original) → 16kHz, mono
Truncate: only the FIRST 10 SECONDS of each audio are kept
(files shorter than 10s are kept intact). 1 original file = 1 sample.
This follows the truncation strategy of Lu et al. (2020), NOT chunking.
Splits: original ViMD train/valid/test kept unchanged (speaker-exclusive).… See the full description on the dataset page: https://huggingface.co/datasets/tannhoo06/ViMD_preprocessing.