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: 0.5320
  - Wer: 0.3362
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5752        | 1.0   | 500   | 2.2162          | 1.0392 |
  | 0.9682        | 2.01  | 1000  | 0.5208          | 0.5191 |
  | 0.4414        | 3.01  | 1500  | 0.4623          | 0.4528 |
  | 0.2971        | 4.02  | 2000  | 0.4553          | 0.4421 |
  | 0.2328        | 5.02  | 2500  | 0.4960          | 0.4208 |
  | 0.1891        | 6.02  | 3000  | 0.4213          | 0.3812 |
  | 0.1571        | 7.03  | 3500  | 0.4863          | 0.3865 |
  | 0.1544        | 8.03  | 4000  | 0.4677          | 0.3910 |
  | 0.1302        | 9.04  | 4500  | 0.6305          | 0.4042 |
  | 0.1111        | 10.04 | 5000  | 0.5104          | 0.3830 |
  | 0.0986        | 11.04 | 5500  | 0.5332          | 0.3808 |
  | 0.088         | 12.05 | 6000  | 0.4494          | 0.3674 |
  | 0.0827        | 13.05 | 6500  | 0.4779          | 0.3748 |
  | 0.074         | 14.06 | 7000  | 0.5315          | 0.3738 |
  | 0.0664        | 15.06 | 7500  | 0.5367          | 0.3661 |
  | 0.0568        | 16.06 | 8000  | 0.5707          | 0.3817 |
  | 0.057         | 17.07 | 8500  | 0.5381          | 0.3719 |
  | 0.0561        | 18.07 | 9000  | 0.5353          | 0.3705 |
  | 0.0487        | 19.08 | 9500  | 0.5087          | 0.3579 |
  | 0.0444        | 20.08 | 10000 | 0.4910          | 0.3596 |
  | 0.0433        | 21.08 | 10500 | 0.4931          | 0.3497 |
  | 0.0363        | 22.09 | 11000 | 0.5414          | 0.3488 |
  | 0.0318        | 23.09 | 11500 | 0.5405          | 0.3472 |
  | 0.033         | 24.1  | 12000 | 0.5476          | 0.3449 |
  | 0.0262        | 25.1  | 12500 | 0.5529          | 0.3443 |
  | 0.0255        | 26.1  | 13000 | 0.5299          | 0.3417 |
  | 0.0252        | 27.11 | 13500 | 0.5092          | 0.3363 |
  | 0.0221        | 28.11 | 14000 | 0.5309          | 0.3357 |
  | 0.0223        | 29.12 | 14500 | 0.5320          | 0.3362 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  