readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-m03-nolm
    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-large-xlsr-53-torgo-demo-m03-nolm
  
  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: 0.0191
  - Wer: 0.4978
  
  ## 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.3416        | 0.92  | 500   | 4.1988          | 1.0    |
  | 3.0162        | 1.85  | 1000  | 4.2155          | 1.0    |
  | 2.8505        | 2.77  | 1500  | 3.3644          | 1.0    |
  | 2.5676        | 3.69  | 2000  | 2.7188          | 1.0    |
  | 1.9316        | 4.61  | 2500  | 1.6584          | 1.3807 |
  | 1.4131        | 5.54  | 3000  | 0.9672          | 1.2920 |
  | 1.0554        | 6.46  | 3500  | 0.6630          | 1.1193 |
  | 0.7956        | 7.38  | 4000  | 0.4932          | 1.0106 |
  | 0.6453        | 8.3   | 4500  | 0.3441          | 0.9024 |
  | 0.5637        | 9.23  | 5000  | 0.2947          | 0.8437 |
  | 0.5264        | 10.15 | 5500  | 0.2328          | 0.7915 |
  | 0.4551        | 11.07 | 6000  | 0.1766          | 0.7410 |
  | 0.4426        | 11.99 | 6500  | 0.1511          | 0.6978 |
  | 0.3635        | 12.92 | 7000  | 0.1229          | 0.6854 |
  | 0.3336        | 13.84 | 7500  | 0.1077          | 0.6329 |
  | 0.315         | 14.76 | 8000  | 0.1073          | 0.6238 |
  | 0.2468        | 15.68 | 8500  | 0.0775          | 0.5938 |
  | 0.2589        | 16.61 | 9000  | 0.0809          | 0.5777 |
  | 0.2209        | 17.53 | 9500  | 0.0655          | 0.5585 |
  | 0.2392        | 18.45 | 10000 | 0.0592          | 0.5503 |
  | 0.2423        | 19.37 | 10500 | 0.0521          | 0.5387 |
  | 0.2347        | 20.3  | 11000 | 0.0428          | 0.5318 |
  | 0.1834        | 21.22 | 11500 | 0.0370          | 0.5243 |
  | 0.1495        | 22.14 | 12000 | 0.0359          | 0.5138 |
  | 0.1676        | 23.06 | 12500 | 0.0305          | 0.5111 |
  | 0.1653        | 23.99 | 13000 | 0.0246          | 0.5041 |
  | 0.1624        | 24.91 | 13500 | 0.0259          | 0.5065 |
  | 0.187         | 25.83 | 14000 | 0.0213          | 0.5008 |
  | 0.1452        | 26.75 | 14500 | 0.0223          | 0.5012 |
  | 0.1555        | 27.68 | 15000 | 0.0208          | 0.5003 |
  | 0.1134        | 28.6  | 15500 | 0.0196          | 0.4990 |
  | 0.1845        | 29.52 | 16000 | 0.0191          | 0.4978 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  