Vietnamese speech dataset
Collection
for speech-related tasks: speech-to-text & text-to-speech
•
25 items
•
Updated
•
9
audio
audioduration (s) 0.1
29.2
| Metadata ID
stringclasses 672
values |
---|---|
VietMed_un_418 |
|
VietMed_un_500 |
|
VietMed_un_361 |
|
VietMed_un_424 |
|
VietMed_un_412 |
|
VietMed_un_261 |
|
VietMed_un_243 |
|
VietMed_un_557 |
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VietMed_un_394 |
|
VietMed_un_492 |
|
VietMed_un_454 |
|
VietMed_un_437 |
|
VietMed_un_687 |
|
VietMed_un_270 |
|
VietMed_un_337 |
|
VietMed_un_290 |
|
VietMed_un_337 |
|
VietMed_un_772 |
|
VietMed_un_239 |
|
VietMed_un_473 |
|
VietMed_un_329 |
|
VietMed_un_413 |
|
VietMed_un_396 |
|
VietMed_un_415 |
|
VietMed_un_104 |
|
VietMed_un_421 |
|
VietMed_un_509 |
|
VietMed_un_500 |
|
VietMed_un_488 |
|
VietMed_un_483 |
|
VietMed_un_345 |
|
VietMed_un_013 |
|
VietMed_un_419 |
|
VietMed_un_309 |
|
VietMed_un_384 |
|
VietMed_un_695 |
|
VietMed_un_323 |
|
VietMed_un_588 |
|
VietMed_un_399 |
|
VietMed_un_322 |
|
VietMed_un_325 |
|
VietMed_un_797 |
|
VietMed_un_595 |
|
VietMed_un_573 |
|
VietMed_un_422 |
|
VietMed_un_370 |
|
VietMed_un_420 |
|
VietMed_un_100 |
|
VietMed_un_407 |
|
VietMed_un_284 |
|
VietMed_un_388 |
|
VietMed_un_448 |
|
VietMed_un_432 |
|
VietMed_un_315 |
|
VietMed_un_104 |
|
VietMed_un_264 |
|
VietMed_un_421 |
|
VietMed_un_339 |
|
VietMed_un_330 |
|
VietMed_un_212 |
|
VietMed_un_500 |
|
VietMed_un_275 |
|
VietMed_un_360 |
|
VietMed_un_790 |
|
VietMed_un_318 |
|
VietMed_un_576 |
|
VietMed_un_263 |
|
VietMed_un_735 |
|
VietMed_un_308 |
|
VietMed_un_354 |
|
VietMed_un_603 |
|
VietMed_un_259 |
|
VietMed_un_437 |
|
VietMed_un_388 |
|
VietMed_un_019 |
|
VietMed_un_569 |
|
VietMed_un_488 |
|
VietMed_un_459 |
|
VietMed_un_337 |
|
VietMed_un_262 |
|
VietMed_un_478 |
|
VietMed_un_341 |
|
VietMed_un_709 |
|
VietMed_un_280 |
|
VietMed_un_398 |
|
VietMed_un_429 |
|
VietMed_un_324 |
|
VietMed_un_511 |
|
VietMed_un_394 |
|
VietMed_un_223 |
|
VietMed_un_352 |
|
VietMed_un_321 |
|
VietMed_un_377 |
|
VietMed_un_461 |
|
VietMed_un_482 |
|
VietMed_un_689 |
|
VietMed_un_234 |
|
VietMed_un_408 |
|
VietMed_un_493 |
|
VietMed_un_346 |
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
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:
# 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)