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: 0.5816
  - Wer: 0.3533
  
  ## 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: 4
  - 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 2.243         | 0.5   | 500   | 1.0798          | 0.7752 |
  | 0.834         | 1.01  | 1000  | 0.6206          | 0.5955 |
  | 0.5503        | 1.51  | 1500  | 0.5387          | 0.5155 |
  | 0.4548        | 2.01  | 2000  | 0.4660          | 0.4763 |
  | 0.3412        | 2.51  | 2500  | 0.8381          | 0.4836 |
  | 0.3128        | 3.02  | 3000  | 0.4818          | 0.4519 |
  | 0.2547        | 3.52  | 3500  | 0.4415          | 0.4230 |
  | 0.2529        | 4.02  | 4000  | 0.4624          | 0.4219 |
  | 0.2103        | 4.52  | 4500  | 0.4714          | 0.4096 |
  | 0.2102        | 5.03  | 5000  | 0.4968          | 0.4087 |
  | 0.1838        | 5.53  | 5500  | 0.4643          | 0.4131 |
  | 0.1721        | 6.03  | 6000  | 0.4676          | 0.3979 |
  | 0.1548        | 6.53  | 6500  | 0.4765          | 0.4085 |
  | 0.1595        | 7.04  | 7000  | 0.4797          | 0.3941 |
  | 0.1399        | 7.54  | 7500  | 0.4753          | 0.3902 |
  | 0.1368        | 8.04  | 8000  | 0.4697          | 0.3945 |
  | 0.1276        | 8.54  | 8500  | 0.5438          | 0.3869 |
  | 0.1255        | 9.05  | 9000  | 0.5660          | 0.3841 |
  | 0.1077        | 9.55  | 9500  | 0.4964          | 0.3947 |
  | 0.1197        | 10.05 | 10000 | 0.5349          | 0.3849 |
  | 0.1014        | 10.55 | 10500 | 0.5558          | 0.3883 |
  | 0.0949        | 11.06 | 11000 | 0.5673          | 0.3785 |
  | 0.0882        | 11.56 | 11500 | 0.5589          | 0.3955 |
  | 0.0906        | 12.06 | 12000 | 0.5752          | 0.4120 |
  | 0.1064        | 12.56 | 12500 | 0.5080          | 0.3727 |
  | 0.0854        | 13.07 | 13000 | 0.5398          | 0.3798 |
  | 0.0754        | 13.57 | 13500 | 0.5237          | 0.3816 |
  | 0.0791        | 14.07 | 14000 | 0.4967          | 0.3725 |
  | 0.0731        | 14.57 | 14500 | 0.5287          | 0.3744 |
  | 0.0719        | 15.08 | 15000 | 0.5633          | 0.3596 |
  | 0.062         | 15.58 | 15500 | 0.5399          | 0.3752 |
  | 0.0681        | 16.08 | 16000 | 0.5151          | 0.3759 |
  | 0.0559        | 16.58 | 16500 | 0.5564          | 0.3709 |
  | 0.0533        | 17.09 | 17000 | 0.5933          | 0.3743 |
  | 0.0563        | 17.59 | 17500 | 0.5381          | 0.3670 |
  | 0.0527        | 18.09 | 18000 | 0.5685          | 0.3731 |
  | 0.0492        | 18.59 | 18500 | 0.5728          | 0.3725 |
  | 0.0509        | 19.1  | 19000 | 0.6074          | 0.3807 |
  | 0.0436        | 19.6  | 19500 | 0.5762          | 0.3628 |
  | 0.0434        | 20.1  | 20000 | 0.6721          | 0.3729 |
  | 0.0416        | 20.6  | 20500 | 0.5842          | 0.3700 |
  | 0.0431        | 21.11 | 21000 | 0.5374          | 0.3607 |
  | 0.037         | 21.61 | 21500 | 0.5556          | 0.3667 |
  | 0.036         | 22.11 | 22000 | 0.5608          | 0.3592 |
  | 0.04          | 22.61 | 22500 | 0.5272          | 0.3637 |
  | 0.047         | 23.12 | 23000 | 0.5234          | 0.3625 |
  | 0.0506        | 23.62 | 23500 | 0.5427          | 0.3629 |
  | 0.0418        | 24.12 | 24000 | 0.5590          | 0.3626 |
  | 0.037         | 24.62 | 24500 | 0.5615          | 0.3555 |
  | 0.0429        | 25.13 | 25000 | 0.5806          | 0.3616 |
  | 0.045         | 25.63 | 25500 | 0.5777          | 0.3639 |
  | 0.0283        | 26.13 | 26000 | 0.5987          | 0.3617 |
  | 0.0253        | 26.63 | 26500 | 0.5671          | 0.3551 |
  | 0.032         | 27.14 | 27000 | 0.5464          | 0.3582 |
  | 0.0321        | 27.64 | 27500 | 0.5634          | 0.3573 |
  | 0.0274        | 28.14 | 28000 | 0.5513          | 0.3575 |
  | 0.0245        | 28.64 | 28500 | 0.5745          | 0.3537 |
  | 0.0251        | 29.15 | 29000 | 0.5759          | 0.3547 |
  | 0.0222        | 29.65 | 29500 | 0.5816          | 0.3533 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  