---
dataset_info:
  config_name: es
  features:
  - name: input_values
    sequence: float32
  - name: input_length
    dtype: int64
  - name: labels
    sequence: int64
  splits:
  - name: train
    num_bytes: 28416160808
    num_examples: 91374
  - name: test
    num_bytes: 1946938848
    num_examples: 5286
  download_size: 30161672462
  dataset_size: 30363099656
configs:
- config_name: es
  data_files:
  - split: train
    path: es/train-*
  - split: test
    path: es/test-*
license: gpl-3.0
language:
- es
pretty_name: Common Voice 13.0 - Wav2Vec2 Preprocessed
---
# Common Voice 13.0 - Wav2Vec2 Preprocessed

Basically took [Common Voice 13.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0), removed all languages 
but English and Spanish, removed all splits but train and test, then 
preprocessed data just as [this tutorial](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for training 
Wav2Vec2 model for speech-recognition. Uploaded with `push_to_hub` function.

For now, just available in Spanish. Use as follows:

```python
from datasets import load_dataset

train_ds = load_dataset("cristibp11/common_voice_13_0_wav2vec2_preprocessed", "es", split="train")
```