metadata

license: cc
language:
  - ab
  - af
  - am
  - ar
  - as
  - ast
  - az
  - ba
  - bas
  - be
  - bg
  - bn
  - br
  - ca
  - ckb
  - cnh
  - cs
  - cv
  - cy
  - da
  - de
  - dv
  - dyu
  - el
  - en
  - eo
  - es
  - et
  - eu
  - fa
  - fi
  - fr
  - gl
  - gn
  - ha
  - he
  - hi
  - hsb
  - hu
  - ia
  - id
  - ig
  - is
  - it
  - ja
  - ka
  - kab
  - kk
  - kmr
  - ko
  - ky
  - lg
  - lo
  - lt
  - lv
  - mdf
  - mhr
  - mk
  - ml
  - mn
  - mr
  - mrj
  - mt
  - myv
  - nl
  - oc
  - or
  - pl
  - ps
  - pt
  - quy
  - ro
  - ru
  - sah
  - sat
  - sc
  - sk
  - skr
  - sl
  - sq
  - sr
  - ta
  - th
  - ti
  - tig
  - tk
  - tok
  - tr
  - tt
  - tw
  - ug
  - uk
  - ur
  - uz
  - vi
  - vot
  - yue
  - zgh
  - zh
  - yo
task_categories:
  - automatic-speech-recognition
pretty_name: Common Voice Corpus 15.0
size_categories:
  - 100B<n<1T
tags:
  - mozilla
  - foundation

Dataset Card for Common Voice Corpus 15.0

This dataset is an unofficial converted version of the Mozilla Common Voice Corpus 15. It currently contains the following languages: Arabic, French, Georgian, German, Hebrew, Italian, Portuguese, and Spanish, among others. Additional languages are being converted and will be uploaded in the next few days.

How to use

The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset function.

For example, to download the Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese):

from datasets import load_dataset

cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")

Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True argument to the load_dataset function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.

from datasets import load_dataset

cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train", streaming=True)

print(next(iter(cv_15)))

Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed).

Local

from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler

cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
batch_sampler = BatchSampler(RandomSampler(cv_15), batch_size=32, drop_last=False)
dataloader = DataLoader(cv_15, batch_sampler=batch_sampler)

Streaming

from datasets import load_dataset
from torch.utils.data import DataLoader

cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train")
dataloader = DataLoader(cv_15, batch_size=32)

To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets.

Dataset Structure

Data Instances A typical data point comprises the path to the audio file and its sentence. Additional fields include accent, age, client_id, up_votes, down_votes, gender, locale and segment.

Licensing Information

Public Domain, CC-0

Citation Information

@inproceedings{commonvoice:2020,
  author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
  title = {Common Voice: A Massively-Multilingual Speech Corpus},
  booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
  pages = {4211--4215},
  year = 2020
}