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: 0.5155
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5822        | 1.0   | 500   | 2.4127          | 1.0    |
  | 0.9838        | 2.01  | 1000  | 0.5401          | 0.5363 |
  | 0.4308        | 3.01  | 1500  | 0.4380          | 0.4592 |
  | 0.3086        | 4.02  | 2000  | 0.4409          | 0.4503 |
  | 0.2324        | 5.02  | 2500  | 0.4148          | 0.4041 |
  | 0.202         | 6.02  | 3000  | 0.4214          | 0.3882 |
  | 0.1595        | 7.03  | 3500  | 0.4489          | 0.3875 |
  | 0.1383        | 8.03  | 4000  | 0.4225          | 0.3858 |
  | 0.1246        | 9.04  | 4500  | 0.4512          | 0.3846 |
  | 0.104         | 10.04 | 5000  | 0.4676          | 0.3875 |
  | 0.0949        | 11.04 | 5500  | 0.4389          | 0.3683 |
  | 0.0899        | 12.05 | 6000  | 0.4964          | 0.3803 |
  | 0.0854        | 13.05 | 6500  | 0.5397          | 0.3798 |
  | 0.0728        | 14.06 | 7000  | 0.4823          | 0.3666 |
  | 0.065         | 15.06 | 7500  | 0.5187          | 0.3648 |
  | 0.0573        | 16.06 | 8000  | 0.5378          | 0.3715 |
  | 0.0546        | 17.07 | 8500  | 0.5239          | 0.3705 |
  | 0.0573        | 18.07 | 9000  | 0.5094          | 0.3554 |
  | 0.0478        | 19.08 | 9500  | 0.5334          | 0.3657 |
  | 0.0673        | 20.08 | 10000 | 0.5300          | 0.3528 |
  | 0.0434        | 21.08 | 10500 | 0.5314          | 0.3528 |
  | 0.0363        | 22.09 | 11000 | 0.5540          | 0.3512 |
  | 0.0326        | 23.09 | 11500 | 0.5514          | 0.3510 |
  | 0.0332        | 24.1  | 12000 | 0.5439          | 0.3492 |
  | 0.0275        | 25.1  | 12500 | 0.5273          | 0.3432 |
  | 0.0267        | 26.1  | 13000 | 0.5068          | 0.3430 |
  | 0.0243        | 27.11 | 13500 | 0.5131          | 0.3388 |
  | 0.0228        | 28.11 | 14000 | 0.5247          | 0.3406 |
  | 0.0227        | 29.12 | 14500 | 0.5155          | 0.3388 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  