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: 0.5185
  - Wer: 0.3370
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5137        | 1.0   | 500   | 1.6719          | 0.9580 |
  | 0.8324        | 2.01  | 1000  | 0.5546          | 0.5341 |
  | 0.4365        | 3.01  | 1500  | 0.4567          | 0.4635 |
  | 0.3058        | 4.02  | 2000  | 0.4429          | 0.4454 |
  | 0.2284        | 5.02  | 2500  | 0.4734          | 0.4186 |
  | 0.1892        | 6.02  | 3000  | 0.4191          | 0.4030 |
  | 0.1542        | 7.03  | 3500  | 0.4522          | 0.3985 |
  | 0.1364        | 8.03  | 4000  | 0.4749          | 0.3922 |
  | 0.1239        | 9.04  | 4500  | 0.4950          | 0.3977 |
  | 0.1092        | 10.04 | 5000  | 0.4468          | 0.3779 |
  | 0.0956        | 11.04 | 5500  | 0.4897          | 0.3789 |
  | 0.0897        | 12.05 | 6000  | 0.4927          | 0.3718 |
  | 0.0792        | 13.05 | 6500  | 0.5242          | 0.3699 |
  | 0.0731        | 14.06 | 7000  | 0.5202          | 0.3772 |
  | 0.0681        | 15.06 | 7500  | 0.5046          | 0.3637 |
  | 0.062         | 16.06 | 8000  | 0.5336          | 0.3664 |
  | 0.0556        | 17.07 | 8500  | 0.5017          | 0.3633 |
  | 0.0556        | 18.07 | 9000  | 0.5466          | 0.3736 |
  | 0.0461        | 19.08 | 9500  | 0.5489          | 0.3566 |
  | 0.0439        | 20.08 | 10000 | 0.5399          | 0.3559 |
  | 0.0397        | 21.08 | 10500 | 0.5154          | 0.3539 |
  | 0.0346        | 22.09 | 11000 | 0.5170          | 0.3513 |
  | 0.0338        | 23.09 | 11500 | 0.5236          | 0.3492 |
  | 0.0342        | 24.1  | 12000 | 0.5288          | 0.3493 |
  | 0.0282        | 25.1  | 12500 | 0.5147          | 0.3449 |
  | 0.0251        | 26.1  | 13000 | 0.5092          | 0.3442 |
  | 0.0268        | 27.11 | 13500 | 0.5093          | 0.3413 |
  | 0.021         | 28.11 | 14000 | 0.5310          | 0.3399 |
  | 0.022         | 29.12 | 14500 | 0.5185          | 0.3370 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  