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: 0.4732
  - Wer: 0.3300
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.2982        | 1.0   | 500   | 1.3852          | 0.9990 |
  | 0.8067        | 2.01  | 1000  | 0.5318          | 0.5140 |
  | 0.4393        | 3.01  | 1500  | 0.4500          | 0.4570 |
  | 0.3007        | 4.02  | 2000  | 0.4259          | 0.4091 |
  | 0.2306        | 5.02  | 2500  | 0.4092          | 0.3962 |
  | 0.1845        | 6.02  | 3000  | 0.3949          | 0.3834 |
  | 0.1516        | 7.03  | 3500  | 0.4144          | 0.3759 |
  | 0.1347        | 8.03  | 4000  | 0.3958          | 0.3689 |
  | 0.1217        | 9.04  | 4500  | 0.4455          | 0.3754 |
  | 0.1039        | 10.04 | 5000  | 0.4228          | 0.3684 |
  | 0.0921        | 11.04 | 5500  | 0.4310          | 0.3566 |
  | 0.082         | 12.05 | 6000  | 0.4549          | 0.3617 |
  | 0.078         | 13.05 | 6500  | 0.4535          | 0.3661 |
  | 0.0668        | 14.06 | 7000  | 0.4726          | 0.3557 |
  | 0.0648        | 15.06 | 7500  | 0.4414          | 0.3512 |
  | 0.0581        | 16.06 | 8000  | 0.4781          | 0.3548 |
  | 0.057         | 17.07 | 8500  | 0.4626          | 0.3588 |
  | 0.0532        | 18.07 | 9000  | 0.5065          | 0.3495 |
  | 0.0442        | 19.08 | 9500  | 0.4645          | 0.3390 |
  | 0.0432        | 20.08 | 10000 | 0.4786          | 0.3466 |
  | 0.0416        | 21.08 | 10500 | 0.4487          | 0.3425 |
  | 0.0337        | 22.09 | 11000 | 0.4878          | 0.3416 |
  | 0.0305        | 23.09 | 11500 | 0.4787          | 0.3413 |
  | 0.0319        | 24.1  | 12000 | 0.4707          | 0.3395 |
  | 0.0262        | 25.1  | 12500 | 0.4875          | 0.3345 |
  | 0.0266        | 26.1  | 13000 | 0.4801          | 0.3343 |
  | 0.025         | 27.11 | 13500 | 0.4926          | 0.3320 |
  | 0.022         | 28.11 | 14000 | 0.4894          | 0.3313 |
  | 0.0227        | 29.12 | 14500 | 0.4732          | 0.3300 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  