Datasets:
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---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- vi
pretty_name: FPT Open Speech Dataset (FOSD)
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 684961355.008
num_examples: 25917
download_size: 819140462
dataset_size: 684961355.008
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# unofficial mirror of FPT Open Speech Dataset (FOSD)
released publicly in 2018 by FPT Corporation
100h, 25.9k samples
official link (dead): https://fpt.ai/fpt-open-speech-data/
mirror: https://data.mendeley.com/datasets/k9sxg2twv4/4
DOI: `10.17632/k9sxg2twv4.4`
pre-process:
- remove non-sense strings: `-N` `\r\n`
- remove 4 files because missing transcription:
- `Set001_V0.1_008210.mp3`
- `Set001_V0.1_010753.mp3`
- `Set001_V0.1_011477.mp3`
- `Set001_V0.1_011841.mp3`
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/fpt_fosd", split="train", streaming=True)
dataset.set_format(type="torch", columns=["audio", "transcription"])
dataloader = DataLoader(dataset, batch_size=4)
``` |