readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-demo-M03-2
    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-demo-M03-2
  
  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: 0.2042
  - Wer: 0.4965
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 22.0914       | 0.92  | 500   | 3.4247          | 1.0    |
  | 3.3901        | 1.85  | 1000  | 2.9866          | 1.0    |
  | 2.8827        | 2.77  | 1500  | 2.7737          | 1.0    |
  | 2.5545        | 3.69  | 2000  | 2.0033          | 1.0    |
  | 1.8375        | 4.61  | 2500  | 0.9457          | 1.2411 |
  | 1.3094        | 5.54  | 3000  | 0.6196          | 1.0427 |
  | 1.0079        | 6.46  | 3500  | 0.4693          | 0.9623 |
  | 0.8174        | 7.38  | 4000  | 0.3809          | 0.8725 |
  | 0.7038        | 8.3   | 4500  | 0.3147          | 0.7991 |
  | 0.5961        | 9.23  | 5000  | 0.2672          | 0.7475 |
  | 0.5066        | 10.15 | 5500  | 0.2440          | 0.7505 |
  | 0.4589        | 11.07 | 6000  | 0.2259          | 0.6979 |
  | 0.4379        | 11.99 | 6500  | 0.2133          | 0.7138 |
  | 0.3835        | 12.92 | 7000  | 0.2315          | 0.6791 |
  | 0.3431        | 13.84 | 7500  | 0.1889          | 0.6200 |
  | 0.3273        | 14.76 | 8000  | 0.2021          | 0.6136 |
  | 0.3087        | 15.68 | 8500  | 0.2159          | 0.5967 |
  | 0.291         | 16.61 | 9000  | 0.2294          | 0.5828 |
  | 0.2662        | 17.53 | 9500  | 0.1736          | 0.5675 |
  | 0.2558        | 18.45 | 10000 | 0.1957          | 0.5531 |
  | 0.2388        | 19.37 | 10500 | 0.2047          | 0.5372 |
  | 0.2219        | 20.3  | 11000 | 0.1843          | 0.5208 |
  | 0.2024        | 21.22 | 11500 | 0.2039          | 0.5278 |
  | 0.187         | 22.14 | 12000 | 0.1810          | 0.5159 |
  | 0.1937        | 23.06 | 12500 | 0.1856          | 0.5188 |
  | 0.1792        | 23.99 | 13000 | 0.1769          | 0.5139 |
  | 0.1642        | 24.91 | 13500 | 0.1994          | 0.5089 |
  | 0.1665        | 25.83 | 14000 | 0.1911          | 0.5124 |
  | 0.1589        | 26.75 | 14500 | 0.2000          | 0.5074 |
  | 0.1599        | 27.68 | 15000 | 0.1912          | 0.4970 |
  | 0.135         | 28.6  | 15500 | 0.2028          | 0.4936 |
  | 0.1383        | 29.52 | 16000 | 0.2042          | 0.4965 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  