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: 0.5436
  - Wer: 0.3401
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5276        | 1.0   | 500   | 1.9983          | 1.0066 |
  | 0.8606        | 2.01  | 1000  | 0.5323          | 0.5220 |
  | 0.4339        | 3.01  | 1500  | 0.4697          | 0.4512 |
  | 0.3026        | 4.02  | 2000  | 0.4342          | 0.4266 |
  | 0.2297        | 5.02  | 2500  | 0.5001          | 0.4135 |
  | 0.1939        | 6.02  | 3000  | 0.4350          | 0.3897 |
  | 0.1613        | 7.03  | 3500  | 0.4740          | 0.3883 |
  | 0.1452        | 8.03  | 4000  | 0.4289          | 0.3825 |
  | 0.1362        | 9.04  | 4500  | 0.4721          | 0.3927 |
  | 0.1146        | 10.04 | 5000  | 0.4707          | 0.3730 |
  | 0.1061        | 11.04 | 5500  | 0.4470          | 0.3701 |
  | 0.0947        | 12.05 | 6000  | 0.4694          | 0.3722 |
  | 0.0852        | 13.05 | 6500  | 0.5222          | 0.3733 |
  | 0.0741        | 14.06 | 7000  | 0.4881          | 0.3657 |
  | 0.069         | 15.06 | 7500  | 0.4957          | 0.3677 |
  | 0.0679        | 16.06 | 8000  | 0.5241          | 0.3634 |
  | 0.0618        | 17.07 | 8500  | 0.5091          | 0.3564 |
  | 0.0576        | 18.07 | 9000  | 0.5055          | 0.3557 |
  | 0.0493        | 19.08 | 9500  | 0.5013          | 0.3515 |
  | 0.0469        | 20.08 | 10000 | 0.5506          | 0.3530 |
  | 0.044         | 21.08 | 10500 | 0.5564          | 0.3528 |
  | 0.0368        | 22.09 | 11000 | 0.5213          | 0.3509 |
  | 0.0355        | 23.09 | 11500 | 0.5707          | 0.3495 |
  | 0.0357        | 24.1  | 12000 | 0.5558          | 0.3483 |
  | 0.0285        | 25.1  | 12500 | 0.5613          | 0.3455 |
  | 0.0285        | 26.1  | 13000 | 0.5533          | 0.3480 |
  | 0.0266        | 27.11 | 13500 | 0.5526          | 0.3462 |
  | 0.0249        | 28.11 | 14000 | 0.5488          | 0.3429 |
  | 0.0237        | 29.12 | 14500 | 0.5436          | 0.3401 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu115
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  