readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-small
    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-small
  
  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: 0.5361
  - Wer: 0.3380
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.571         | 1.0   | 500   | 1.9252          | 1.0022 |
  | 0.8969        | 2.01  | 1000  | 0.5066          | 0.5292 |
  | 0.4326        | 3.01  | 1500  | 0.4523          | 0.4562 |
  | 0.2993        | 4.02  | 2000  | 0.4228          | 0.4202 |
  | 0.2335        | 5.02  | 2500  | 0.4252          | 0.4178 |
  | 0.2009        | 6.02  | 3000  | 0.4136          | 0.3910 |
  | 0.1552        | 7.03  | 3500  | 0.4747          | 0.3863 |
  | 0.1388        | 8.03  | 4000  | 0.4359          | 0.3859 |
  | 0.1226        | 9.04  | 4500  | 0.4367          | 0.3879 |
  | 0.1109        | 10.04 | 5000  | 0.4360          | 0.3760 |
  | 0.0991        | 11.04 | 5500  | 0.4899          | 0.3672 |
  | 0.0882        | 12.05 | 6000  | 0.4608          | 0.3653 |
  | 0.0792        | 13.05 | 6500  | 0.4882          | 0.3703 |
  | 0.0745        | 14.06 | 7000  | 0.4716          | 0.3625 |
  | 0.065         | 15.06 | 7500  | 0.4896          | 0.3651 |
  | 0.0596        | 16.06 | 8000  | 0.4831          | 0.3659 |
  | 0.0563        | 17.07 | 8500  | 0.5092          | 0.3585 |
  | 0.0536        | 18.07 | 9000  | 0.5376          | 0.3675 |
  | 0.0465        | 19.08 | 9500  | 0.5019          | 0.3534 |
  | 0.049         | 20.08 | 10000 | 0.4869          | 0.3723 |
  | 0.0423        | 21.08 | 10500 | 0.4947          | 0.3501 |
  | 0.0348        | 22.09 | 11000 | 0.5524          | 0.3453 |
  | 0.0315        | 23.09 | 11500 | 0.5369          | 0.3499 |
  | 0.0312        | 24.1  | 12000 | 0.5283          | 0.3519 |
  | 0.0258        | 25.1  | 12500 | 0.5202          | 0.3461 |
  | 0.0249        | 26.1  | 13000 | 0.5270          | 0.3449 |
  | 0.0236        | 27.11 | 13500 | 0.5388          | 0.3408 |
  | 0.0206        | 28.11 | 14000 | 0.5361          | 0.3388 |
  | 0.0224        | 29.12 | 14500 | 0.5361          | 0.3380 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.13.1+cu116
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  