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: 0.5112
  - Wer: 0.9988
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.5557        | 1.0   | 500   | 1.6786          | 1.0    |
  | 0.8407        | 2.01  | 1000  | 0.5356          | 0.9988 |
  | 0.4297        | 3.01  | 1500  | 0.4431          | 0.9988 |
  | 0.2989        | 4.02  | 2000  | 0.4191          | 0.9988 |
  | 0.2338        | 5.02  | 2500  | 0.4251          | 0.9988 |
  | 0.1993        | 6.02  | 3000  | 0.4618          | 0.9988 |
  | 0.1585        | 7.03  | 3500  | 0.4577          | 0.9988 |
  | 0.1386        | 8.03  | 4000  | 0.4099          | 0.9982 |
  | 0.1234        | 9.04  | 4500  | 0.4945          | 0.9988 |
  | 0.1162        | 10.04 | 5000  | 0.4597          | 0.9988 |
  | 0.1008        | 11.04 | 5500  | 0.4563          | 0.9988 |
  | 0.0894        | 12.05 | 6000  | 0.5157          | 0.9988 |
  | 0.083         | 13.05 | 6500  | 0.5027          | 0.9988 |
  | 0.0735        | 14.06 | 7000  | 0.4905          | 0.9994 |
  | 0.0686        | 15.06 | 7500  | 0.4552          | 0.9988 |
  | 0.0632        | 16.06 | 8000  | 0.5522          | 0.9988 |
  | 0.061         | 17.07 | 8500  | 0.4874          | 0.9988 |
  | 0.0626        | 18.07 | 9000  | 0.5243          | 0.9988 |
  | 0.0475        | 19.08 | 9500  | 0.4798          | 0.9988 |
  | 0.0447        | 20.08 | 10000 | 0.5250          | 0.9988 |
  | 0.0432        | 21.08 | 10500 | 0.5195          | 0.9988 |
  | 0.0358        | 22.09 | 11000 | 0.5008          | 0.9988 |
  | 0.0319        | 23.09 | 11500 | 0.5376          | 0.9988 |
  | 0.0334        | 24.1  | 12000 | 0.5149          | 0.9988 |
  | 0.0269        | 25.1  | 12500 | 0.4911          | 0.9988 |
  | 0.0275        | 26.1  | 13000 | 0.4907          | 0.9988 |
  | 0.027         | 27.11 | 13500 | 0.4992          | 0.9988 |
  | 0.0239        | 28.11 | 14000 | 0.5021          | 0.9988 |
  | 0.0233        | 29.12 | 14500 | 0.5112          | 0.9988 |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  