readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-finetune-spanish-col
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # wav2vec2-large-xlsr-finetune-spanish-col
  
  This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.7105
  - Wer: 0.9824
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 7.2829        | 3.25  | 400  | 2.9632          | 1.0    |
  | 2.9664        | 6.5   | 800  | 2.8494          | 1.0542 |
  | 2.8353        | 9.76  | 1200 | 2.8352          | 1.0101 |
  | 2.7863        | 13.01 | 1600 | 2.7421          | 0.9837 |
  | 2.762         | 16.26 | 2000 | 2.7254          | 0.9861 |
  | 2.7483        | 19.51 | 2400 | 2.7228          | 0.9874 |
  | 2.7482        | 22.76 | 2800 | 2.7228          | 0.9999 |
  | 2.7373        | 26.02 | 3200 | 2.7163          | 0.9824 |
  | 2.7328        | 29.27 | 3600 | 2.7105          | 0.9824 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.1+cu102
  - Datasets 1.13.3
  - Tokenizers 0.10.3
  