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---
license: cc
language:
- af
- ar
- ckb
- cs
- da
- de
- el
- en
- es
- fi
- fr
- gn
- he
- hi
- hu
- it
- ja
- ka
- kab
- ko
- lv
- nl
- quy
- ro
- sk
- sl
- sq
- sr
- th
- tr
- uk
- vi
- yue
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
<!-- Provide a quick summary of the dataset. -->
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
}
``` |