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: 0.5167
  - Wer: 0.3371
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5326        | 1.0   | 500   | 1.8362          | 1.0134 |
  | 0.8886        | 2.01  | 1000  | 0.5096          | 0.5255 |
  | 0.4265        | 3.01  | 1500  | 0.4255          | 0.4577 |
  | 0.2955        | 4.02  | 2000  | 0.4041          | 0.4206 |
  | 0.2251        | 5.02  | 2500  | 0.4508          | 0.4095 |
  | 0.1967        | 6.02  | 3000  | 0.4277          | 0.3969 |
  | 0.1651        | 7.03  | 3500  | 0.4357          | 0.3846 |
  | 0.1387        | 8.03  | 4000  | 0.4546          | 0.4123 |
  | 0.1235        | 9.04  | 4500  | 0.4817          | 0.3940 |
  | 0.1124        | 10.04 | 5000  | 0.5007          | 0.3855 |
  | 0.1046        | 11.04 | 5500  | 0.4883          | 0.3884 |
  | 0.1179        | 12.05 | 6000  | 0.4652          | 0.3798 |
  | 0.0858        | 13.05 | 6500  | 0.4652          | 0.3867 |
  | 0.0767        | 14.06 | 7000  | 0.4919          | 0.3722 |
  | 0.0658        | 15.06 | 7500  | 0.4844          | 0.3670 |
  | 0.0605        | 16.06 | 8000  | 0.5113          | 0.3667 |
  | 0.0579        | 17.07 | 8500  | 0.5369          | 0.3617 |
  | 0.0549        | 18.07 | 9000  | 0.5116          | 0.3635 |
  | 0.0452        | 19.08 | 9500  | 0.5099          | 0.3525 |
  | 0.0425        | 20.08 | 10000 | 0.5324          | 0.3566 |
  | 0.0405        | 21.08 | 10500 | 0.4881          | 0.3537 |
  | 0.0338        | 22.09 | 11000 | 0.5161          | 0.3544 |
  | 0.0388        | 23.09 | 11500 | 0.5216          | 0.3544 |
  | 0.0358        | 24.1  | 12000 | 0.4999          | 0.3428 |
  | 0.0284        | 25.1  | 12500 | 0.4905          | 0.3414 |
  | 0.0236        | 26.1  | 13000 | 0.5029          | 0.3406 |
  | 0.0245        | 27.11 | 13500 | 0.5087          | 0.3411 |
  | 0.0218        | 28.11 | 14000 | 0.5188          | 0.3361 |
  | 0.0217        | 29.12 | 14500 | 0.5167          | 0.3371 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  