readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - common_voice
  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-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6342
  - Wer: 0.5808
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 9.1358        | 1.19  | 500   | 3.2710          | 1.0    |
  | 3.0499        | 2.38  | 1000  | 1.8976          | 1.0    |
  | 1.279         | 3.56  | 1500  | 0.7502          | 0.8228 |
  | 0.7953        | 4.75  | 2000  | 0.5914          | 0.7343 |
  | 0.6451        | 5.94  | 2500  | 0.6152          | 0.7280 |
  | 0.5351        | 7.13  | 3000  | 0.5948          | 0.7041 |
  | 0.4633        | 8.31  | 3500  | 0.5585          | 0.6712 |
  | 0.4272        | 9.5   | 4000  | 0.5372          | 0.6457 |
  | 0.3803        | 10.69 | 4500  | 0.5404          | 0.6402 |
  | 0.3462        | 11.88 | 5000  | 0.5862          | 0.6484 |
  | 0.3302        | 13.06 | 5500  | 0.5991          | 0.6426 |
  | 0.3096        | 14.25 | 6000  | 0.5687          | 0.6287 |
  | 0.2839        | 15.44 | 6500  | 0.5798          | 0.6384 |
  | 0.2701        | 16.63 | 7000  | 0.5775          | 0.6047 |
  | 0.2507        | 17.81 | 7500  | 0.5638          | 0.6065 |
  | 0.2376        | 19.0  | 8000  | 0.5937          | 0.6094 |
  | 0.2264        | 20.19 | 8500  | 0.5944          | 0.6065 |
  | 0.2146        | 21.38 | 9000  | 0.6050          | 0.6122 |
  | 0.1947        | 22.57 | 9500  | 0.6283          | 0.5992 |
  | 0.1982        | 23.75 | 10000 | 0.6126          | 0.6018 |
  | 0.1924        | 24.94 | 10500 | 0.6075          | 0.5962 |
  | 0.1855        | 26.13 | 11000 | 0.6344          | 0.5938 |
  | 0.1839        | 27.32 | 11500 | 0.6118          | 0.5880 |
  | 0.1741        | 28.5  | 12000 | 0.6381          | 0.5878 |
  | 0.1726        | 29.69 | 12500 | 0.6342          | 0.5808 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  