readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  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: 0.5047
  - Wer: 0.2233
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5485        | 1.0   | 500   | 1.9954          | 1.0042 |
  | 0.9068        | 2.01  | 1000  | 0.6418          | 0.4572 |
  | 0.4398        | 3.01  | 1500  | 0.4586          | 0.3629 |
  | 0.3023        | 4.02  | 2000  | 0.4464          | 0.3248 |
  | 0.2328        | 5.02  | 2500  | 0.4019          | 0.2969 |
  | 0.1899        | 6.02  | 3000  | 0.4363          | 0.2961 |
  | 0.163         | 7.03  | 3500  | 0.4832          | 0.2872 |
  | 0.1442        | 8.03  | 4000  | 0.4421          | 0.2801 |
  | 0.1246        | 9.04  | 4500  | 0.4757          | 0.2659 |
  | 0.1122        | 10.04 | 5000  | 0.4693          | 0.2648 |
  | 0.102         | 11.04 | 5500  | 0.4834          | 0.2549 |
  | 0.0919        | 12.05 | 6000  | 0.4558          | 0.2633 |
  | 0.0866        | 13.05 | 6500  | 0.4527          | 0.2641 |
  | 0.0762        | 14.06 | 7000  | 0.4394          | 0.2565 |
  | 0.0705        | 15.06 | 7500  | 0.5240          | 0.2609 |
  | 0.0647        | 16.06 | 8000  | 0.4980          | 0.2522 |
  | 0.0608        | 17.07 | 8500  | 0.5163          | 0.2589 |
  | 0.0576        | 18.07 | 9000  | 0.4991          | 0.2565 |
  | 0.0499        | 19.08 | 9500  | 0.4750          | 0.2457 |
  | 0.047         | 20.08 | 10000 | 0.5162          | 0.2447 |
  | 0.0418        | 21.08 | 10500 | 0.4801          | 0.2413 |
  | 0.0383        | 22.09 | 11000 | 0.4961          | 0.2394 |
  | 0.0342        | 23.09 | 11500 | 0.5209          | 0.2386 |
  | 0.032         | 24.1  | 12000 | 0.4970          | 0.2364 |
  | 0.0293        | 25.1  | 12500 | 0.4789          | 0.2309 |
  | 0.0265        | 26.1  | 13000 | 0.4948          | 0.2302 |
  | 0.0269        | 27.11 | 13500 | 0.4917          | 0.2249 |
  | 0.0237        | 28.11 | 14000 | 0.4991          | 0.2238 |
  | 0.022         | 29.12 | 14500 | 0.5047          | 0.2233 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.13.0+cu116
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  