Datasets:
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---
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:
```python
# 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)
``` |