readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-f03-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-f03-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: 0.0220
  - Wer: 0.5303
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.3935        | 0.97  | 500   | 4.2877          | 1.0    |
  | 3.0106        | 1.94  | 1000  | 3.1321          | 1.0    |
  | 2.9323        | 2.91  | 1500  | 3.2662          | 1.0    |
  | 2.7359        | 3.88  | 2000  | 3.2751          | 1.0    |
  | 2.4803        | 4.85  | 2500  | 2.5467          | 1.3920 |
  | 2.0298        | 5.83  | 3000  | 1.8645          | 1.3701 |
  | 1.4024        | 6.8   | 3500  | 1.1290          | 1.3445 |
  | 1.1126        | 7.77  | 4000  | 0.7792          | 1.2031 |
  | 0.8674        | 8.74  | 4500  | 0.5452          | 1.0921 |
  | 0.668         | 9.71  | 5000  | 0.3812          | 0.9847 |
  | 0.5972        | 10.68 | 5500  | 0.3118          | 0.9197 |
  | 0.5227        | 11.65 | 6000  | 0.2359          | 0.8489 |
  | 0.4032        | 12.62 | 6500  | 0.1690          | 0.7781 |
  | 0.3923        | 13.59 | 7000  | 0.1490          | 0.7383 |
  | 0.3964        | 14.56 | 7500  | 0.1274          | 0.7069 |
  | 0.2967        | 15.53 | 8000  | 0.1158          | 0.6881 |
  | 0.2989        | 16.5  | 8500  | 0.0884          | 0.6520 |
  | 0.274         | 17.48 | 9000  | 0.0875          | 0.6510 |
  | 0.2968        | 18.45 | 9500  | 0.0701          | 0.6314 |
  | 0.1968        | 19.42 | 10000 | 0.0666          | 0.6117 |
  | 0.2212        | 20.39 | 10500 | 0.0556          | 0.5985 |
  | 0.2104        | 21.36 | 11000 | 0.0493          | 0.5865 |
  | 0.1999        | 22.33 | 11500 | 0.0409          | 0.5701 |
  | 0.2085        | 23.3  | 12000 | 0.0412          | 0.5690 |
  | 0.1842        | 24.27 | 12500 | 0.0338          | 0.5528 |
  | 0.1588        | 25.24 | 13000 | 0.0324          | 0.5540 |
  | 0.1558        | 26.21 | 13500 | 0.0284          | 0.5454 |
  | 0.1505        | 27.18 | 14000 | 0.0262          | 0.5350 |
  | 0.1741        | 28.16 | 14500 | 0.0232          | 0.5291 |
  | 0.158         | 29.13 | 15000 | 0.0220          | 0.5303 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  