readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-demo-google-colab
    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-base-timit-demo-google-colab
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5499
  - Wer: 0.3435
  
  ## 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: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.599         | 1.0   | 500   | 2.1267          | 0.9976 |
  | 1.016         | 2.01  | 1000  | 0.6193          | 0.5443 |
  | 0.5299        | 3.01  | 1500  | 0.5324          | 0.4889 |
  | 0.3626        | 4.02  | 2000  | 0.4525          | 0.4402 |
  | 0.2854        | 5.02  | 2500  | 0.4266          | 0.4233 |
  | 0.2373        | 6.02  | 3000  | 0.4713          | 0.4082 |
  | 0.1979        | 7.03  | 3500  | 0.4778          | 0.4018 |
  | 0.1761        | 8.03  | 4000  | 0.4585          | 0.3947 |
  | 0.1537        | 9.04  | 4500  | 0.5297          | 0.3946 |
  | 0.1379        | 10.04 | 5000  | 0.4988          | 0.3856 |
  | 0.124         | 11.04 | 5500  | 0.5262          | 0.3852 |
  | 0.11          | 12.05 | 6000  | 0.5545          | 0.3854 |
  | 0.106         | 13.05 | 6500  | 0.5196          | 0.3805 |
  | 0.0918        | 14.06 | 7000  | 0.4515          | 0.3655 |
  | 0.0829        | 15.06 | 7500  | 0.5087          | 0.3722 |
  | 0.0775        | 16.06 | 8000  | 0.4980          | 0.3781 |
  | 0.0685        | 17.07 | 8500  | 0.5564          | 0.3650 |
  | 0.0655        | 18.07 | 9000  | 0.5323          | 0.3672 |
  | 0.0578        | 19.08 | 9500  | 0.5675          | 0.3637 |
  | 0.052         | 20.08 | 10000 | 0.5604          | 0.3664 |
  | 0.0512        | 21.08 | 10500 | 0.5922          | 0.3804 |
  | 0.0431        | 22.09 | 11000 | 0.6379          | 0.3754 |
  | 0.0428        | 23.09 | 11500 | 0.5905          | 0.3764 |
  | 0.0393        | 24.1  | 12000 | 0.5667          | 0.3542 |
  | 0.0326        | 25.1  | 12500 | 0.5612          | 0.3537 |
  | 0.0289        | 26.1  | 13000 | 0.5618          | 0.3475 |
  | 0.0298        | 27.11 | 13500 | 0.5578          | 0.3439 |
  | 0.0264        | 28.11 | 14000 | 0.5547          | 0.3433 |
  | 0.026         | 29.12 | 14500 | 0.5499          | 0.3435 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  