metadata

license: cc-by-nc-sa-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, 26k 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

usage with HuggingFace:

# 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)