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: 0.5261
  - Wer: 0.3351
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5764        | 1.0   | 500   | 2.3358          | 1.0    |
  | 0.9494        | 2.01  | 1000  | 0.6086          | 0.5448 |
  | 0.4527        | 3.01  | 1500  | 0.4731          | 0.4685 |
  | 0.307         | 4.02  | 2000  | 0.4432          | 0.4341 |
  | 0.2366        | 5.02  | 2500  | 0.4343          | 0.4025 |
  | 0.1934        | 6.02  | 3000  | 0.4284          | 0.4105 |
  | 0.154         | 7.03  | 3500  | 0.4709          | 0.3936 |
  | 0.14          | 8.03  | 4000  | 0.4296          | 0.3889 |
  | 0.1189        | 9.04  | 4500  | 0.4864          | 0.3862 |
  | 0.1057        | 10.04 | 5000  | 0.4903          | 0.3776 |
  | 0.1034        | 11.04 | 5500  | 0.4889          | 0.3838 |
  | 0.0899        | 12.05 | 6000  | 0.4680          | 0.3701 |
  | 0.0864        | 13.05 | 6500  | 0.4981          | 0.3608 |
  | 0.0714        | 14.06 | 7000  | 0.4608          | 0.3589 |
  | 0.0673        | 15.06 | 7500  | 0.4970          | 0.3754 |
  | 0.0606        | 16.06 | 8000  | 0.5344          | 0.3618 |
  | 0.0603        | 17.07 | 8500  | 0.4980          | 0.3675 |
  | 0.0588        | 18.07 | 9000  | 0.5339          | 0.3601 |
  | 0.0453        | 19.08 | 9500  | 0.4973          | 0.3526 |
  | 0.0433        | 20.08 | 10000 | 0.5359          | 0.3572 |
  | 0.0421        | 21.08 | 10500 | 0.4885          | 0.3532 |
  | 0.0359        | 22.09 | 11000 | 0.5184          | 0.3471 |
  | 0.032         | 23.09 | 11500 | 0.5230          | 0.3483 |
  | 0.0333        | 24.1  | 12000 | 0.5512          | 0.3474 |
  | 0.0279        | 25.1  | 12500 | 0.5102          | 0.3437 |
  | 0.0232        | 26.1  | 13000 | 0.5195          | 0.3384 |
  | 0.0237        | 27.11 | 13500 | 0.5350          | 0.3355 |
  | 0.0209        | 28.11 | 14000 | 0.5432          | 0.3368 |
  | 0.023         | 29.12 | 14500 | 0.5261          | 0.3351 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.12.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  