readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-STTTest
    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-STTTest
  
  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: 0.5198
  - Wer: 0.3393
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.231         | 1.0   | 500   | 0.4337          | 0.4100 |
  | 0.1845        | 2.01  | 1000  | 0.4296          | 0.3931 |
  | 0.1551        | 3.01  | 1500  | 0.4397          | 0.3770 |
  | 0.1479        | 4.02  | 2000  | 0.4524          | 0.3827 |
  | 0.1186        | 5.02  | 2500  | 0.5182          | 0.3795 |
  | 0.1079        | 6.02  | 3000  | 0.4799          | 0.3737 |
  | 0.0974        | 7.03  | 3500  | 0.4966          | 0.3860 |
  | 0.0878        | 8.03  | 4000  | 0.4993          | 0.3699 |
  | 0.0788        | 9.04  | 4500  | 0.5183          | 0.3678 |
  | 0.0732        | 10.04 | 5000  | 0.5064          | 0.3635 |
  | 0.0664        | 11.04 | 5500  | 0.5330          | 0.3663 |
  | 0.0596        | 12.05 | 6000  | 0.5147          | 0.3516 |
  | 0.0538        | 13.05 | 6500  | 0.5254          | 0.3581 |
  | 0.0535        | 14.06 | 7000  | 0.4902          | 0.3534 |
  | 0.0492        | 15.06 | 7500  | 0.5115          | 0.3488 |
  | 0.0455        | 16.06 | 8000  | 0.5250          | 0.3472 |
  | 0.0434        | 17.07 | 8500  | 0.5338          | 0.3515 |
  | 0.0351        | 18.07 | 9000  | 0.5365          | 0.3444 |
  | 0.0341        | 19.08 | 9500  | 0.4886          | 0.3439 |
  | 0.0332        | 20.08 | 10000 | 0.5234          | 0.3475 |
  | 0.0289        | 21.08 | 10500 | 0.5375          | 0.3464 |
  | 0.028         | 22.09 | 11000 | 0.5395          | 0.3478 |
  | 0.0225        | 23.09 | 11500 | 0.5236          | 0.3428 |
  | 0.0244        | 24.1  | 12000 | 0.5122          | 0.3402 |
  | 0.0246        | 25.1  | 12500 | 0.5212          | 0.3390 |
  | 0.0214        | 26.1  | 13000 | 0.5198          | 0.3393 |
  | 0.0179        | 27.11 | 13500 | 0.5198          | 0.3393 |
  | 0.0194        | 28.11 | 14000 | 0.5198          | 0.3393 |
  | 0.0193        | 29.12 | 14500 | 0.5198          | 0.3393 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.1+cu111
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  