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: 0.5173
  - Wer: 0.3399
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5684        | 1.0   | 500   | 2.1662          | 1.0068 |
  | 0.9143        | 2.01  | 1000  | 0.5820          | 0.5399 |
  | 0.439         | 3.01  | 1500  | 0.4596          | 0.4586 |
  | 0.3122        | 4.02  | 2000  | 0.4623          | 0.4181 |
  | 0.2391        | 5.02  | 2500  | 0.4243          | 0.3938 |
  | 0.1977        | 6.02  | 3000  | 0.4421          | 0.3964 |
  | 0.1635        | 7.03  | 3500  | 0.5076          | 0.3977 |
  | 0.145         | 8.03  | 4000  | 0.4639          | 0.3754 |
  | 0.1315        | 9.04  | 4500  | 0.5181          | 0.3652 |
  | 0.1131        | 10.04 | 5000  | 0.4496          | 0.3778 |
  | 0.1005        | 11.04 | 5500  | 0.4438          | 0.3664 |
  | 0.0919        | 12.05 | 6000  | 0.4868          | 0.3865 |
  | 0.0934        | 13.05 | 6500  | 0.5163          | 0.3694 |
  | 0.076         | 14.06 | 7000  | 0.4543          | 0.3719 |
  | 0.0727        | 15.06 | 7500  | 0.5296          | 0.3807 |
  | 0.0657        | 16.06 | 8000  | 0.4715          | 0.3699 |
  | 0.0578        | 17.07 | 8500  | 0.4927          | 0.3699 |
  | 0.057         | 18.07 | 9000  | 0.4767          | 0.3660 |
  | 0.0493        | 19.08 | 9500  | 0.5306          | 0.3623 |
  | 0.0425        | 20.08 | 10000 | 0.4828          | 0.3561 |
  | 0.0431        | 21.08 | 10500 | 0.4875          | 0.3620 |
  | 0.0366        | 22.09 | 11000 | 0.4984          | 0.3482 |
  | 0.0332        | 23.09 | 11500 | 0.5375          | 0.3477 |
  | 0.0348        | 24.1  | 12000 | 0.5406          | 0.3361 |
  | 0.0301        | 25.1  | 12500 | 0.4954          | 0.3381 |
  | 0.0294        | 26.1  | 13000 | 0.5033          | 0.3424 |
  | 0.026         | 27.11 | 13500 | 0.5254          | 0.3384 |
  | 0.0243        | 28.11 | 14000 | 0.5189          | 0.3402 |
  | 0.0221        | 29.12 | 14500 | 0.5173          | 0.3399 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  