readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-base-timit-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-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: 0.5506
  - Wer: 0.3355
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.4326        | 1.0   | 500   | 1.5832          | 1.0063 |
  | 0.8235        | 2.01  | 1000  | 0.5310          | 0.5134 |
  | 0.4224        | 3.01  | 1500  | 0.4488          | 0.4461 |
  | 0.2978        | 4.02  | 2000  | 0.4243          | 0.4191 |
  | 0.232         | 5.02  | 2500  | 0.4532          | 0.4149 |
  | 0.1902        | 6.02  | 3000  | 0.4732          | 0.3912 |
  | 0.1628        | 7.03  | 3500  | 0.4807          | 0.3868 |
  | 0.1437        | 8.03  | 4000  | 0.5295          | 0.3670 |
  | 0.1241        | 9.04  | 4500  | 0.4602          | 0.3810 |
  | 0.1206        | 10.04 | 5000  | 0.4691          | 0.3783 |
  | 0.0984        | 11.04 | 5500  | 0.4500          | 0.3710 |
  | 0.0929        | 12.05 | 6000  | 0.5247          | 0.3550 |
  | 0.0914        | 13.05 | 6500  | 0.5546          | 0.3821 |
  | 0.0742        | 14.06 | 7000  | 0.4874          | 0.3646 |
  | 0.0729        | 15.06 | 7500  | 0.5327          | 0.3934 |
  | 0.0663        | 16.06 | 8000  | 0.5769          | 0.3661 |
  | 0.0575        | 17.07 | 8500  | 0.5191          | 0.3524 |
  | 0.0588        | 18.07 | 9000  | 0.5155          | 0.3360 |
  | 0.0456        | 19.08 | 9500  | 0.5135          | 0.3539 |
  | 0.0444        | 20.08 | 10000 | 0.5380          | 0.3603 |
  | 0.0419        | 21.08 | 10500 | 0.5275          | 0.3467 |
  | 0.0366        | 22.09 | 11000 | 0.5072          | 0.3487 |
  | 0.0331        | 23.09 | 11500 | 0.5450          | 0.3437 |
  | 0.0345        | 24.1  | 12000 | 0.5138          | 0.3431 |
  | 0.029         | 25.1  | 12500 | 0.5067          | 0.3413 |
  | 0.0274        | 26.1  | 13000 | 0.5421          | 0.3422 |
  | 0.0243        | 27.11 | 13500 | 0.5456          | 0.3392 |
  | 0.0226        | 28.11 | 14000 | 0.5665          | 0.3368 |
  | 0.0216        | 29.12 | 14500 | 0.5506          | 0.3355 |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.13.3
  - Tokenizers 0.12.1
  