Datasets:
File size: 1,867 Bytes
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---
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)
```
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