readme: ---
  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.