readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-demo-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-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: 0.0403
  - Wer: 0.0168
  
  ## 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: 64
  - eval_batch_size: 16
  - 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    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 4.5738        | 2.82  | 500  | 2.8712          | 1.0    |
  | 1.3905        | 5.65  | 1000 | 0.2342          | 0.2124 |
  | 0.1868        | 8.47  | 1500 | 0.1023          | 0.0697 |
  | 0.0831        | 11.3  | 2000 | 0.0603          | 0.0339 |
  | 0.0512        | 14.12 | 2500 | 0.0519          | 0.0263 |
  | 0.0363        | 16.95 | 3000 | 0.0478          | 0.0228 |
  | 0.0267        | 19.77 | 3500 | 0.0490          | 0.0228 |
  | 0.0205        | 22.6  | 4000 | 0.0390          | 0.0182 |
  | 0.0163        | 25.42 | 4500 | 0.0418          | 0.0184 |
  | 0.0145        | 28.25 | 5000 | 0.0403          | 0.0168 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu111
  - Datasets 1.13.3
  - Tokenizers 0.10.3
  