readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-demo-google-colab
    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-demo-google-colab
  
  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: 0.5480
  - Wer: 0.3437
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5237        | 1.0   | 500   | 1.7277          | 0.9752 |
  | 0.8339        | 2.01  | 1000  | 0.5413          | 0.5316 |
  | 0.4277        | 3.01  | 1500  | 0.4732          | 0.4754 |
  | 0.2907        | 4.02  | 2000  | 0.4571          | 0.4476 |
  | 0.2254        | 5.02  | 2500  | 0.4611          | 0.4105 |
  | 0.1911        | 6.02  | 3000  | 0.4448          | 0.4072 |
  | 0.1595        | 7.03  | 3500  | 0.4517          | 0.3843 |
  | 0.1377        | 8.03  | 4000  | 0.4551          | 0.3881 |
  | 0.1197        | 9.04  | 4500  | 0.4853          | 0.3772 |
  | 0.1049        | 10.04 | 5000  | 0.4617          | 0.3707 |
  | 0.097         | 11.04 | 5500  | 0.4633          | 0.3622 |
  | 0.0872        | 12.05 | 6000  | 0.4635          | 0.3690 |
  | 0.0797        | 13.05 | 6500  | 0.5196          | 0.3749 |
  | 0.0731        | 14.06 | 7000  | 0.5029          | 0.3639 |
  | 0.0667        | 15.06 | 7500  | 0.5053          | 0.3614 |
  | 0.0618        | 16.06 | 8000  | 0.5627          | 0.3638 |
  | 0.0562        | 17.07 | 8500  | 0.5484          | 0.3577 |
  | 0.0567        | 18.07 | 9000  | 0.5163          | 0.3560 |
  | 0.0452        | 19.08 | 9500  | 0.5012          | 0.3538 |
  | 0.044         | 20.08 | 10000 | 0.4931          | 0.3534 |
  | 0.0424        | 21.08 | 10500 | 0.5147          | 0.3519 |
  | 0.0356        | 22.09 | 11000 | 0.5540          | 0.3521 |
  | 0.0322        | 23.09 | 11500 | 0.5565          | 0.3509 |
  | 0.0333        | 24.1  | 12000 | 0.5315          | 0.3428 |
  | 0.0281        | 25.1  | 12500 | 0.5284          | 0.3425 |
  | 0.0261        | 26.1  | 13000 | 0.5101          | 0.3446 |
  | 0.0256        | 27.11 | 13500 | 0.5432          | 0.3415 |
  | 0.0229        | 28.11 | 14000 | 0.5484          | 0.3446 |
  | 0.0212        | 29.12 | 14500 | 0.5480          | 0.3437 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  