README.md

---
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

<!-- 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
}
```