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: 0.5035
  - Wer: 0.3346
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 1.1411        | 1.0   | 500   | 0.6675          | 0.6001 |
  | 0.5668        | 2.01  | 1000  | 0.4699          | 0.4973 |
  | 0.3773        | 3.01  | 1500  | 0.4475          | 0.4403 |
  | 0.2696        | 4.02  | 2000  | 0.4162          | 0.4166 |
  | 0.2165        | 5.02  | 2500  | 0.3809          | 0.4011 |
  | 0.1849        | 6.02  | 3000  | 0.4120          | 0.3849 |
  | 0.1542        | 7.03  | 3500  | 0.4436          | 0.3770 |
  | 0.1385        | 8.03  | 4000  | 0.3977          | 0.3732 |
  | 0.1224        | 9.04  | 4500  | 0.4530          | 0.3672 |
  | 0.1233        | 10.04 | 5000  | 0.3949          | 0.3596 |
  | 0.1078        | 11.04 | 5500  | 0.4616          | 0.3539 |
  | 0.097         | 12.05 | 6000  | 0.4354          | 0.3697 |
  | 0.0821        | 13.05 | 6500  | 0.4370          | 0.3643 |
  | 0.0724        | 14.06 | 7000  | 0.4729          | 0.3587 |
  | 0.0678        | 15.06 | 7500  | 0.5822          | 0.3742 |
  | 0.0632        | 16.06 | 8000  | 0.4460          | 0.3513 |
  | 0.0627        | 17.07 | 8500  | 0.5531          | 0.3537 |
  | 0.0574        | 18.07 | 9000  | 0.5262          | 0.3575 |
  | 0.0515        | 19.08 | 9500  | 0.4794          | 0.3488 |
  | 0.0475        | 20.08 | 10000 | 0.4941          | 0.3458 |
  | 0.0463        | 21.08 | 10500 | 0.4741          | 0.3377 |
  | 0.0392        | 22.09 | 11000 | 0.5390          | 0.3381 |
  | 0.0401        | 23.09 | 11500 | 0.4984          | 0.3413 |
  | 0.0371        | 24.1  | 12000 | 0.5112          | 0.3460 |
  | 0.0305        | 25.1  | 12500 | 0.5255          | 0.3418 |
  | 0.0278        | 26.1  | 13000 | 0.5045          | 0.3389 |
  | 0.0265        | 27.11 | 13500 | 0.4990          | 0.3371 |
  | 0.0248        | 28.11 | 14000 | 0.5242          | 0.3362 |
  | 0.0249        | 29.12 | 14500 | 0.5035          | 0.3346 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  