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: 0.5501
  - Wer: 0.3424
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5448        | 1.0   | 500   | 2.5044          | 1.0    |
  | 1.0167        | 2.01  | 1000  | 0.5435          | 0.5278 |
  | 0.4453        | 3.01  | 1500  | 0.4450          | 0.4534 |
  | 0.3           | 4.02  | 2000  | 0.4401          | 0.4245 |
  | 0.2304        | 5.02  | 2500  | 0.4146          | 0.4022 |
  | 0.1889        | 6.02  | 3000  | 0.4241          | 0.3927 |
  | 0.1573        | 7.03  | 3500  | 0.4545          | 0.3878 |
  | 0.1363        | 8.03  | 4000  | 0.4936          | 0.3940 |
  | 0.1213        | 9.04  | 4500  | 0.4964          | 0.3806 |
  | 0.108         | 10.04 | 5000  | 0.4931          | 0.3826 |
  | 0.0982        | 11.04 | 5500  | 0.5373          | 0.3778 |
  | 0.0883        | 12.05 | 6000  | 0.4978          | 0.3733 |
  | 0.0835        | 13.05 | 6500  | 0.5189          | 0.3728 |
  | 0.0748        | 14.06 | 7000  | 0.4608          | 0.3692 |
  | 0.068         | 15.06 | 7500  | 0.4827          | 0.3608 |
  | 0.0596        | 16.06 | 8000  | 0.5022          | 0.3661 |
  | 0.056         | 17.07 | 8500  | 0.5482          | 0.3646 |
  | 0.0565        | 18.07 | 9000  | 0.5158          | 0.3573 |
  | 0.0487        | 19.08 | 9500  | 0.4910          | 0.3513 |
  | 0.0444        | 20.08 | 10000 | 0.5771          | 0.3580 |
  | 0.045         | 21.08 | 10500 | 0.5160          | 0.3539 |
  | 0.0363        | 22.09 | 11000 | 0.5367          | 0.3503 |
  | 0.0313        | 23.09 | 11500 | 0.5773          | 0.3500 |
  | 0.0329        | 24.1  | 12000 | 0.5683          | 0.3508 |
  | 0.0297        | 25.1  | 12500 | 0.5355          | 0.3464 |
  | 0.0272        | 26.1  | 13000 | 0.5317          | 0.3450 |
  | 0.0256        | 27.11 | 13500 | 0.5602          | 0.3443 |
  | 0.0242        | 28.11 | 14000 | 0.5586          | 0.3419 |
  | 0.0239        | 29.12 | 14500 | 0.5501          | 0.3424 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  