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---
language:
- es
tags:
- audio # Example: audio
- automatic-speech-recognition # Example:
datasets:
- multilingual_librispeech
metrics:
- eval_wer: 0.073
model-index:
- name: wav2vec2-spanish-multilibrispeech
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: multilingual_librispeech # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: multilingual_librispeech es # Required. Example: Common Voice zh-CN
args: es # Optional. Example: zh-CN
metrics:
- type: wer
value: 0.073
name: eval_wer
- type: loss
value: 0.086
name: eval_loss
---
This is a model for automatic speech recognition in spanish, by using the Spanish portion of [multilingual_librispeech](https://huggingface.co/datasets/multilingual_librispeech) and the [pre-trained wav2vec2 multilingual from Facebook](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
For training the model, we used the same parameters as they recommend in [the paper](https://arxiv.org/abs/2006.13979). We trained for a total of 15 epochs, obtaining a final wer of 0.073.
An example of how to use this model:
```python
from transformers import Wav2Vec2Tokenizer, AutoModelForCTC
tokenizer = Wav2Vec2Tokenizer.from_pretrained(
"IIC/wav2vec2-spanish-multilibrispeech"
)
model = AutoModelForCTC.from_pretrained(
"IIC/wav2vec2-spanish-multilibrispeech"
)
```
### Contributions
Thanks to [@avacaondata](https://huggingface.co/avacaondata), [@alborotis](https://huggingface.co/alborotis), [@albarji](https://huggingface.co/albarji), [@Dabs](https://huggingface.co/Dabs), [@GuillemGSubies](https://huggingface.co/GuillemGSubies) for adding this model. |