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: 0.5206
  - Wer: 0.3388
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5597        | 1.0   | 500   | 2.3415          | 0.9991 |
  | 0.9759        | 2.01  | 1000  | 0.5556          | 0.5382 |
  | 0.4587        | 3.01  | 1500  | 0.7690          | 0.4781 |
  | 0.3156        | 4.02  | 2000  | 0.7994          | 0.4412 |
  | 0.2272        | 5.02  | 2500  | 0.8948          | 0.4120 |
  | 0.1921        | 6.02  | 3000  | 0.7065          | 0.3940 |
  | 0.1618        | 7.03  | 3500  | 0.4333          | 0.3855 |
  | 0.1483        | 8.03  | 4000  | 0.4232          | 0.3872 |
  | 0.156         | 9.04  | 4500  | 0.4172          | 0.3749 |
  | 0.1138        | 10.04 | 5000  | 0.4084          | 0.3758 |
  | 0.1045        | 11.04 | 5500  | 0.4665          | 0.3623 |
  | 0.0908        | 12.05 | 6000  | 0.4416          | 0.3684 |
  | 0.0788        | 13.05 | 6500  | 0.4801          | 0.3659 |
  | 0.0773        | 14.06 | 7000  | 0.4560          | 0.3583 |
  | 0.0684        | 15.06 | 7500  | 0.4878          | 0.3610 |
  | 0.0645        | 16.06 | 8000  | 0.4635          | 0.3567 |
  | 0.0577        | 17.07 | 8500  | 0.5245          | 0.3548 |
  | 0.0547        | 18.07 | 9000  | 0.5265          | 0.3639 |
  | 0.0466        | 19.08 | 9500  | 0.5161          | 0.3546 |
  | 0.0432        | 20.08 | 10000 | 0.5263          | 0.3558 |
  | 0.0414        | 21.08 | 10500 | 0.4874          | 0.3500 |
  | 0.0365        | 22.09 | 11000 | 0.5266          | 0.3472 |
  | 0.0321        | 23.09 | 11500 | 0.5422          | 0.3458 |
  | 0.0325        | 24.1  | 12000 | 0.5201          | 0.3428 |
  | 0.0262        | 25.1  | 12500 | 0.5208          | 0.3398 |
  | 0.0249        | 26.1  | 13000 | 0.5034          | 0.3429 |
  | 0.0262        | 27.11 | 13500 | 0.5055          | 0.3396 |
  | 0.0248        | 28.11 | 14000 | 0.5164          | 0.3404 |
  | 0.0222        | 29.12 | 14500 | 0.5206          | 0.3388 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  