README.md

---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- vi
pretty_name: VietMed unlabeled set
size_categories:
- 100K<n<1M
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: Metadata ID
    dtype: string
  splits:
  - name: train
    num_bytes: 57670081699.38
    num_examples: 230516
  download_size: 51899577807
  dataset_size: 57670081699.38
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# unofficial mirror of VietMed (Vietnamese speech data in medical domain) unlabeled set

official announcement: https://arxiv.org/abs/2404.05659

official download: https://huggingface.co/datasets/leduckhai/VietMed

this repo contains the unlabeled set: 966h - 230k samples

i also gather the metadata: see [info.csv](info.csv)

my extraction code: https://github.com/phineas-pta/fine-tune-whisper-vi/blob/main/misc/vietmed-unlabeled.py

need to do: check misspelling, restore foreign words phonetised to vietnamese

usage with HuggingFace:
```python
# pip install -q "datasets[audio]"
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from pandas import read_csv

repo_id = "doof-ferb/VietMed_unlabeled"
dataset = load_dataset(repo_id, split="train", streaming=True)
info_file = hf_hub_download(repo_id=repo_id, filename="info.csv", repo_type="dataset")
info_dict = read_csv(info_file, index_col=0).to_dict("index")

def merge_info(batch):
	meta = info_dict.get(batch["Metadata ID"], "")
	if meta != "":
		batch["Domain"]      = meta["Domain"]
		batch["ICD-10 Code"] = meta["ICD-10 Code"]
		batch["Accent"]      = meta["Accent"]
	else:
		batch["Domain"]      = ""
		batch["ICD-10 Code"] = ""
		batch["Accent"]      = ""
	return batch
dataset = dataset.map(merge_info)
```