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: 0.5079
  - Wer: 0.3365
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.4933        | 1.0   | 500   | 1.7711          | 0.9978 |
  | 0.8658        | 2.01  | 1000  | 0.6262          | 0.5295 |
  | 0.4405        | 3.01  | 1500  | 0.4841          | 0.4845 |
  | 0.3062        | 4.02  | 2000  | 0.4897          | 0.4215 |
  | 0.233         | 5.02  | 2500  | 0.4326          | 0.4101 |
  | 0.1896        | 6.02  | 3000  | 0.4924          | 0.4078 |
  | 0.1589        | 7.03  | 3500  | 0.4430          | 0.3896 |
  | 0.1391        | 8.03  | 4000  | 0.4334          | 0.3889 |
  | 0.1216        | 9.04  | 4500  | 0.4691          | 0.3828 |
  | 0.1063        | 10.04 | 5000  | 0.4726          | 0.3705 |
  | 0.0992        | 11.04 | 5500  | 0.4333          | 0.3690 |
  | 0.0872        | 12.05 | 6000  | 0.4986          | 0.3771 |
  | 0.0829        | 13.05 | 6500  | 0.4903          | 0.3685 |
  | 0.0713        | 14.06 | 7000  | 0.5293          | 0.3655 |
  | 0.068         | 15.06 | 7500  | 0.5039          | 0.3612 |
  | 0.0621        | 16.06 | 8000  | 0.5314          | 0.3665 |
  | 0.0571        | 17.07 | 8500  | 0.5038          | 0.3572 |
  | 0.0585        | 18.07 | 9000  | 0.4718          | 0.3550 |
  | 0.0487        | 19.08 | 9500  | 0.5482          | 0.3626 |
  | 0.0459        | 20.08 | 10000 | 0.5239          | 0.3545 |
  | 0.0419        | 21.08 | 10500 | 0.5096          | 0.3473 |
  | 0.0362        | 22.09 | 11000 | 0.5222          | 0.3500 |
  | 0.0331        | 23.09 | 11500 | 0.5062          | 0.3489 |
  | 0.0352        | 24.1  | 12000 | 0.4913          | 0.3459 |
  | 0.0315        | 25.1  | 12500 | 0.4701          | 0.3412 |
  | 0.028         | 26.1  | 13000 | 0.5178          | 0.3402 |
  | 0.0255        | 27.11 | 13500 | 0.5168          | 0.3405 |
  | 0.0228        | 28.11 | 14000 | 0.5154          | 0.3368 |
  | 0.0232        | 29.12 | 14500 | 0.5079          | 0.3365 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  