readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-base-timit-eng
    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-eng
  
  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: 0.5195
  - Wer: 0.3418
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5159        | 1.0   | 500   | 1.7153          | 1.0291 |
  | 0.8502        | 2.01  | 1000  | 0.5204          | 0.5146 |
  | 0.431         | 3.01  | 1500  | 0.4491          | 0.4537 |
  | 0.3073        | 4.02  | 2000  | 0.3883          | 0.4190 |
  | 0.2338        | 5.02  | 2500  | 0.4453          | 0.4230 |
  | 0.1956        | 6.02  | 3000  | 0.4599          | 0.3981 |
  | 0.1594        | 7.03  | 3500  | 0.4240          | 0.3916 |
  | 0.1423        | 8.03  | 4000  | 0.4756          | 0.3975 |
  | 0.1252        | 9.04  | 4500  | 0.4427          | 0.3827 |
  | 0.1064        | 10.04 | 5000  | 0.4489          | 0.3809 |
  | 0.101         | 11.04 | 5500  | 0.4531          | 0.3961 |
  | 0.0877        | 12.05 | 6000  | 0.4881          | 0.3883 |
  | 0.0817        | 13.05 | 6500  | 0.5023          | 0.3774 |
  | 0.0703        | 14.06 | 7000  | 0.5078          | 0.3679 |
  | 0.0663        | 15.06 | 7500  | 0.5279          | 0.3620 |
  | 0.0584        | 16.06 | 8000  | 0.5112          | 0.3653 |
  | 0.0579        | 17.07 | 8500  | 0.4959          | 0.3633 |
  | 0.0572        | 18.07 | 9000  | 0.4676          | 0.3626 |
  | 0.0502        | 19.08 | 9500  | 0.5216          | 0.3503 |
  | 0.0432        | 20.08 | 10000 | 0.4946          | 0.3480 |
  | 0.0417        | 21.08 | 10500 | 0.4949          | 0.3532 |
  | 0.0335        | 22.09 | 11000 | 0.5485          | 0.3557 |
  | 0.032         | 23.09 | 11500 | 0.5087          | 0.3464 |
  | 0.0334        | 24.1  | 12000 | 0.5313          | 0.3498 |
  | 0.0263        | 25.1  | 12500 | 0.5148          | 0.3457 |
  | 0.0242        | 26.1  | 13000 | 0.5232          | 0.3442 |
  | 0.0235        | 27.11 | 13500 | 0.5122          | 0.3418 |
  | 0.0221        | 28.11 | 14000 | 0.5074          | 0.3407 |
  | 0.0215        | 29.12 | 14500 | 0.5195          | 0.3418 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu116
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  