readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: data-augmentation-whitenoise-timit-2310
    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. -->
  
  # data-augmentation-whitenoise-timit-2310
  
  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: 0.5916
  - Wer: 0.3408
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.6731        | 0.67  | 500   | 2.7553          | 1.0    |
  | 1.0656        | 1.34  | 1000  | 0.5963          | 0.5297 |
  | 0.5065        | 2.01  | 1500  | 0.4898          | 0.4654 |
  | 0.3212        | 2.68  | 2000  | 0.4265          | 0.4331 |
  | 0.2492        | 3.35  | 2500  | 0.4020          | 0.4073 |
  | 0.2116        | 4.02  | 3000  | 0.4152          | 0.3935 |
  | 0.1719        | 4.69  | 3500  | 0.4258          | 0.3858 |
  | 0.1544        | 5.36  | 4000  | 0.4542          | 0.3818 |
  | 0.1474        | 6.03  | 4500  | 0.4612          | 0.3821 |
  | 0.1248        | 6.7   | 5000  | 0.4813          | 0.3749 |
  | 0.1148        | 7.37  | 5500  | 0.5131          | 0.3772 |
  | 0.1145        | 8.04  | 6000  | 0.5383          | 0.3714 |
  | 0.0986        | 8.71  | 6500  | 0.5288          | 0.3777 |
  | 0.091         | 9.38  | 7000  | 0.5071          | 0.3869 |
  | 0.0789        | 10.05 | 7500  | 0.5256          | 0.3819 |
  | 0.0747        | 10.72 | 8000  | 0.5287          | 0.3711 |
  | 0.0687        | 11.39 | 8500  | 0.5179          | 0.3754 |
  | 0.072         | 12.06 | 9000  | 0.7438          | 0.3702 |
  | 0.0646        | 12.73 | 9500  | 0.5293          | 0.3777 |
  | 0.0621        | 13.4  | 10000 | 0.5536          | 0.3692 |
  | 0.0587        | 14.08 | 10500 | 0.5214          | 0.3712 |
  | 0.0538        | 14.75 | 11000 | 0.4853          | 0.3694 |
  | 0.0614        | 15.42 | 11500 | 0.5439          | 0.3637 |
  | 0.0493        | 16.09 | 12000 | 0.5087          | 0.3649 |
  | 0.0441        | 16.76 | 12500 | 0.5736          | 0.3621 |
  | 0.038         | 17.43 | 13000 | 0.7295          | 0.3650 |
  | 0.0397        | 18.1  | 13500 | 0.5722          | 0.3586 |
  | 0.0357        | 18.77 | 14000 | 0.5701          | 0.3616 |
  | 0.0349        | 19.44 | 14500 | 0.5661          | 0.3599 |
  | 0.0318        | 20.11 | 15000 | 0.5346          | 0.3572 |
  | 0.0288        | 20.78 | 15500 | 0.6972          | 0.3597 |
  | 0.0331        | 21.45 | 16000 | 0.5288          | 0.3576 |
  | 0.0304        | 22.12 | 16500 | 0.5813          | 0.3551 |
  | 0.0268        | 22.79 | 17000 | 0.5439          | 0.3557 |
  | 0.0255        | 23.46 | 17500 | 0.5790          | 0.3531 |
  | 0.0244        | 24.13 | 18000 | 0.5794          | 0.3493 |
  | 0.0335        | 24.8  | 18500 | 0.5943          | 0.3515 |
  | 0.026         | 25.47 | 19000 | 0.5737          | 0.3462 |
  | 0.0199        | 26.14 | 19500 | 0.5794          | 0.3469 |
  | 0.0213        | 26.81 | 20000 | 0.5955          | 0.3448 |
  | 0.0199        | 27.48 | 20500 | 0.5927          | 0.3407 |
  | 0.0143        | 28.15 | 21000 | 0.5975          | 0.3415 |
  | 0.0167        | 28.82 | 21500 | 0.5835          | 0.3411 |
  | 0.0141        | 29.49 | 22000 | 0.5916          | 0.3408 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  