---
license: mpl-2.0
language:
- pt
---
# 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: Portuguese 



## 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.