metadata

license: cc-by-4.0
task_categories:
  - automatic-speech-recognition
  - text-to-speech
language:
  - vi
pretty_name: InfoRe Technology public dataset №1
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: transcription
      dtype: string
  splits:
    - name: train
      num_bytes: 7370428827.92
      num_examples: 14935
  download_size: 7832947140
  dataset_size: 7370428827.92
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

unofficial mirror of InfoRe Technology public dataset №1

official announcement: https://www.facebook.com/groups/j2team.community/permalink/1010834009248719/

25h, 14.9k samples, InfoRe paid a contractor to read text

official download: magnet:?xt=urn:btih:1cbe13fb14a390c852c016a924b4a5e879d85f41&dn=25hours.zip&tr=http%3A%2F%2Foffice.socials.vn%3A8725%2Fannounce

mirror: https://files.huylenguyen.com/25hours.zip

unzip password: BroughtToYouByInfoRe

pre-process: none

need to do: check misspelling

usage with HuggingFace:

# pip install -q "datasets[audio]"
from datasets import load_dataset
from torch.utils.data import DataLoader

dataset = load_dataset("doof-ferb/infore1_25hours", split="train", streaming=True)
dataset.set_format(type="torch", columns=["audio", "transcription"])
dataloader = DataLoader(dataset, batch_size=4)