readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: wav2vec2-base-asc-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-asc-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 None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9473
  - Wer: 0.8943
  
  ## 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: 100
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 10.9515       | 1.06  | 100  | 4.0400          | 1.0    |
  | 3.026         | 2.13  | 200  | 3.7680          | 1.0    |
  | 2.9349        | 3.19  | 300  | 3.8252          | 1.0    |
  | 2.7955        | 4.26  | 400  | 3.4536          | 1.0    |
  | 2.5002        | 5.32  | 500  | 3.1430          | 1.0    |
  | 1.8767        | 6.38  | 600  | 2.5167          | 1.0    |
  | 1.179         | 7.45  | 700  | 1.8392          | 0.9879 |
  | 0.7846        | 8.51  | 800  | 1.3472          | 0.9293 |
  | 0.4949        | 9.57  | 900  | 1.4321          | 0.94   |
  | 0.3398        | 10.64 | 1000 | 1.2133          | 0.9214 |
  | 0.3259        | 11.7  | 1100 | 1.6726          | 0.9207 |
  | 0.2736        | 12.77 | 1200 | 1.6282          | 0.9114 |
  | 0.224         | 13.83 | 1300 | 1.6113          | 0.9    |
  | 0.1962        | 14.89 | 1400 | 1.6028          | 0.8986 |
  | 0.1734        | 15.96 | 1500 | 1.7467          | 0.8979 |
  | 0.1761        | 17.02 | 1600 | 1.8196          | 0.8993 |
  | 0.1574        | 18.09 | 1700 | 1.8836          | 0.895  |
  | 0.1397        | 19.15 | 1800 | 1.7974          | 0.8921 |
  | 0.145         | 20.21 | 1900 | 1.7876          | 0.8971 |
  | 0.1311        | 21.28 | 2000 | 1.8534          | 0.9029 |
  | 0.1329        | 22.34 | 2100 | 1.8521          | 0.8914 |
  | 0.1141        | 23.4  | 2200 | 1.8889          | 0.8914 |
  | 0.1307        | 24.47 | 2300 | 1.9190          | 0.8957 |
  | 0.1094        | 25.53 | 2400 | 1.9745          | 0.8943 |
  | 0.1133        | 26.6  | 2500 | 1.9405          | 0.8936 |
  | 0.0967        | 27.66 | 2600 | 1.9461          | 0.8914 |
  | 0.1088        | 28.72 | 2700 | 1.9458          | 0.8943 |
  | 0.1066        | 29.79 | 2800 | 1.9473          | 0.8943 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  