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: 0.5218
  - Wer: 0.3434
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5634        | 1.0   | 500   | 2.0727          | 1.0096 |
  | 0.9357        | 2.01  | 1000  | 0.6623          | 0.5634 |
  | 0.4536        | 3.01  | 1500  | 1.4421          | 0.4829 |
  | 0.3044        | 4.02  | 2000  | 0.4361          | 0.4363 |
  | 0.2369        | 5.02  | 2500  | 0.5098          | 0.4495 |
  | 0.1994        | 6.02  | 3000  | 0.4741          | 0.3711 |
  | 0.1699        | 7.03  | 3500  | 0.4652          | 0.3898 |
  | 0.1499        | 8.03  | 4000  | 0.4151          | 0.3949 |
  | 0.1308        | 9.04  | 4500  | 0.4685          | 0.3838 |
  | 0.1234        | 10.04 | 5000  | 0.5076          | 0.3794 |
  | 0.1055        | 11.04 | 5500  | 0.4492          | 0.3790 |
  | 0.0953        | 12.05 | 6000  | 0.4726          | 0.3679 |
  | 0.0863        | 13.05 | 6500  | 0.4797          | 0.3717 |
  | 0.0816        | 14.06 | 7000  | 0.4725          | 0.3655 |
  | 0.0842        | 15.06 | 7500  | 0.5181          | 0.3405 |
  | 0.0661        | 16.06 | 8000  | 0.5315          | 0.3510 |
  | 0.0593        | 17.07 | 8500  | 0.5024          | 0.3668 |
  | 0.0624        | 18.07 | 9000  | 0.5374          | 0.3663 |
  | 0.0535        | 19.08 | 9500  | 0.4861          | 0.3517 |
  | 0.0524        | 20.08 | 10000 | 0.4812          | 0.3574 |
  | 0.0461        | 21.08 | 10500 | 0.4976          | 0.3431 |
  | 0.0363        | 22.09 | 11000 | 0.5062          | 0.3476 |
  | 0.0351        | 23.09 | 11500 | 0.5094          | 0.3479 |
  | 0.0327        | 24.1  | 12000 | 0.5291          | 0.3455 |
  | 0.0319        | 25.1  | 12500 | 0.5209          | 0.3460 |
  | 0.0268        | 26.1  | 13000 | 0.5173          | 0.3481 |
  | 0.0263        | 27.11 | 13500 | 0.5362          | 0.3486 |
  | 0.0234        | 28.11 | 14000 | 0.5333          | 0.3444 |
  | 0.0237        | 29.12 | 14500 | 0.5218          | 0.3434 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.13.0
  