readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53-torgo-demo-f04-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-f04-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: 0.0139
  - Wer: 0.4976
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.2981        | 0.89  | 500   | 4.2044          | 1.0    |
  | 2.8779        | 1.79  | 1000  | 3.0752          | 1.0    |
  | 2.5952        | 2.68  | 1500  | 2.5703          | 1.2968 |
  | 1.9456        | 3.57  | 2000  | 1.6902          | 1.3749 |
  | 1.35          | 4.46  | 2500  | 1.0368          | 1.2687 |
  | 1.0567        | 5.36  | 3000  | 0.7006          | 1.1703 |
  | 0.8172        | 6.25  | 3500  | 0.4751          | 1.0399 |
  | 0.7228        | 7.14  | 4000  | 0.3549          | 0.9343 |
  | 0.5735        | 8.04  | 4500  | 0.2778          | 0.8606 |
  | 0.5164        | 8.93  | 5000  | 0.2151          | 0.8142 |
  | 0.4465        | 9.82  | 5500  | 0.1823          | 0.7394 |
  | 0.3773        | 10.71 | 6000  | 0.1550          | 0.7232 |
  | 0.4436        | 11.61 | 6500  | 0.1434          | 0.7015 |
  | 0.3438        | 12.5  | 7000  | 0.1139          | 0.6764 |
  | 0.3163        | 13.39 | 7500  | 0.1079          | 0.6446 |
  | 0.309         | 14.29 | 8000  | 0.0975          | 0.6243 |
  | 0.2345        | 15.18 | 8500  | 0.0826          | 0.6198 |
  | 0.2846        | 16.07 | 9000  | 0.0803          | 0.5960 |
  | 0.2706        | 16.96 | 9500  | 0.0586          | 0.5958 |
  | 0.2497        | 17.86 | 10000 | 0.0558          | 0.5714 |
  | 0.2285        | 18.75 | 10500 | 0.0465          | 0.5599 |
  | 0.1838        | 19.64 | 11000 | 0.0429          | 0.5467 |
  | 0.1807        | 20.54 | 11500 | 0.0404          | 0.5387 |
  | 0.156         | 21.43 | 12000 | 0.0306          | 0.5269 |
  | 0.173         | 22.32 | 12500 | 0.0267          | 0.5191 |
  | 0.1618        | 23.21 | 13000 | 0.0263          | 0.5139 |
  | 0.1677        | 24.11 | 13500 | 0.0225          | 0.5090 |
  | 0.1472        | 25.0  | 14000 | 0.0200          | 0.5068 |
  | 0.1656        | 25.89 | 14500 | 0.0174          | 0.5046 |
  | 0.1501        | 26.79 | 15000 | 0.0164          | 0.4999 |
  | 0.1191        | 27.68 | 15500 | 0.0147          | 0.4980 |
  | 0.1138        | 28.57 | 16000 | 0.0145          | 0.4978 |
  | 0.1311        | 29.46 | 16500 | 0.0139          | 0.4976 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.0.0
  - Tokenizers 0.13.2
  