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: 0.5351
  - Wer: 0.3384
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.6311        | 1.0   | 500   | 2.6700          | 1.0    |
  | 1.0104        | 2.01  | 1000  | 0.5289          | 0.5277 |
  | 0.4483        | 3.01  | 1500  | 0.4576          | 0.4623 |
  | 0.3089        | 4.02  | 2000  | 0.4483          | 0.4255 |
  | 0.2278        | 5.02  | 2500  | 0.4463          | 0.4022 |
  | 0.1886        | 6.02  | 3000  | 0.4653          | 0.3938 |
  | 0.1578        | 7.03  | 3500  | 0.4624          | 0.3855 |
  | 0.1429        | 8.03  | 4000  | 0.4420          | 0.3854 |
  | 0.1244        | 9.04  | 4500  | 0.4980          | 0.3787 |
  | 0.1126        | 10.04 | 5000  | 0.4311          | 0.3785 |
  | 0.1082        | 11.04 | 5500  | 0.5114          | 0.3782 |
  | 0.0888        | 12.05 | 6000  | 0.5392          | 0.3725 |
  | 0.0835        | 13.05 | 6500  | 0.6011          | 0.3941 |
  | 0.074         | 14.06 | 7000  | 0.5030          | 0.3652 |
  | 0.0667        | 15.06 | 7500  | 0.5041          | 0.3583 |
  | 0.0595        | 16.06 | 8000  | 0.5125          | 0.3605 |
  | 0.0578        | 17.07 | 8500  | 0.5206          | 0.3592 |
  | 0.0573        | 18.07 | 9000  | 0.5208          | 0.3643 |
  | 0.0469        | 19.08 | 9500  | 0.4670          | 0.3537 |
  | 0.0442        | 20.08 | 10000 | 0.5388          | 0.3497 |
  | 0.0417        | 21.08 | 10500 | 0.5213          | 0.3581 |
  | 0.0361        | 22.09 | 11000 | 0.5096          | 0.3465 |
  | 0.0338        | 23.09 | 11500 | 0.5178          | 0.3459 |
  | 0.0333        | 24.1  | 12000 | 0.5240          | 0.3490 |
  | 0.0256        | 25.1  | 12500 | 0.5438          | 0.3464 |
  | 0.0248        | 26.1  | 13000 | 0.5182          | 0.3412 |
  | 0.0231        | 27.11 | 13500 | 0.5628          | 0.3423 |
  | 0.0228        | 28.11 | 14000 | 0.5416          | 0.3419 |
  | 0.0223        | 29.12 | 14500 | 0.5351          | 0.3384 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  