readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
    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. -->
  
  # libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-5
  
  This model is a fine-tuned version of [rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3](https://huggingface.co/rohitp1/libri-smallw2v2-no-copy-mse-alpha-0.75-T-1-take-3) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 30.3050
  - Wer: 0.2650
  
  ## 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: 0.002
  - train_batch_size: 4
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 239.4865      | 1.12  | 400  | 31.0836         | 0.2908 |
  | 210.2046      | 2.25  | 800  | 29.7831         | 0.2742 |
  | 195.0478      | 3.37  | 1200 | 28.8794         | 0.2636 |
  | 188.8096      | 4.49  | 1600 | 28.7458         | 0.2600 |
  | 183.6592      | 5.62  | 2000 | 29.1159         | 0.2573 |
  | 181.5025      | 6.74  | 2400 | 29.0081         | 0.2564 |
  | 181.9954      | 7.86  | 2800 | 28.7132         | 0.2588 |
  | 181.8548      | 8.99  | 3200 | 29.3207         | 0.2630 |
  | 186.2524      | 10.11 | 3600 | 29.9119         | 0.2593 |
  | 188.3838      | 11.24 | 4000 | 30.2963         | 0.2627 |
  | 192.2623      | 12.36 | 4400 | 30.3050         | 0.2650 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  