readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-demo-idrak-practice
    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-idrak-practice
  
  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: 0.3538
  - Wer: 0.3209
  
  ## 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 3.634         | 0.87  | 500  | 2.6452          | 1.0    |
  | 1.0497        | 1.73  | 1000 | 0.5711          | 0.5138 |
  | 0.4584        | 2.6   | 1500 | 0.4421          | 0.4492 |
  | 0.3198        | 3.46  | 2000 | 0.3818          | 0.3941 |
  | 0.2263        | 4.33  | 2500 | 0.3653          | 0.3767 |
  | 0.1845        | 5.19  | 3000 | 0.3424          | 0.3661 |
  | 0.1388        | 6.06  | 3500 | 0.3702          | 0.3519 |
  | 0.1214        | 6.92  | 4000 | 0.3515          | 0.3439 |
  | 0.1026        | 7.79  | 4500 | 0.3585          | 0.3292 |
  | 0.0834        | 8.65  | 5000 | 0.3474          | 0.3236 |
  | 0.0737        | 9.52  | 5500 | 0.3538          | 0.3209 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu116
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  