readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: timit-supervised
    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. -->
  
  # timit-supervised
  
  This model is a fine-tuned version of [Experiments/single_dataset/timit-supervised/checkpoint-3500](https://huggingface.co/Experiments/single_dataset/timit-supervised/checkpoint-3500) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1272
  - Wer: 0.0532
  
  ## 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: 16
  - 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: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 0.0554        | 1.77  | 500  | 0.1310          | 0.0697 |
  | 0.0509        | 3.53  | 1000 | 0.1497          | 0.0710 |
  | 0.038         | 5.3   | 1500 | 0.1190          | 0.0659 |
  | 0.0328        | 7.07  | 2000 | 0.0926          | 0.0596 |
  | 0.0247        | 8.83  | 2500 | 0.0873          | 0.0570 |
  | 0.0229        | 10.6  | 3000 | 0.0890          | 0.0532 |
  | 0.0183        | 12.37 | 3500 | 0.0969          | 0.0532 |
  | 0.0326        | 14.13 | 4000 | 0.0809          | 0.0469 |
  | 0.03          | 15.9  | 4500 | 0.0758          | 0.0444 |
  | 0.0264        | 17.67 | 5000 | 0.0973          | 0.0520 |
  | 0.0244        | 19.43 | 5500 | 0.1272          | 0.0532 |
  
  
  ### Framework versions
  
  - Transformers 4.14.1
  - Pytorch 1.10.2
  - Datasets 1.18.2
  - Tokenizers 0.10.3
  