readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: wav2vec2-large-xlsr-53_train_data_full
    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_train_data_full
  
  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: 0.4168
  - Wer: 0.3383
  
  ## 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: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 3.0459        | 0.73  | 500   | 3.2037          | 0.9995 |
  | 0.7938        | 1.45  | 1000  | 0.7432          | 0.6373 |
  | 0.503         | 2.18  | 1500  | 0.5517          | 0.5115 |
  | 0.4475        | 2.91  | 2000  | 0.4916          | 0.4624 |
  | 0.3575        | 3.63  | 2500  | 0.4612          | 0.4362 |
  | 0.3206        | 4.36  | 3000  | 0.4546          | 0.4198 |
  | 0.3155        | 5.09  | 3500  | 0.4073          | 0.3929 |
  | 0.2827        | 5.81  | 4000  | 0.4172          | 0.3808 |
  | 0.2575        | 6.54  | 4500  | 0.4183          | 0.3741 |
  | 0.2399        | 7.27  | 5000  | 0.4181          | 0.3680 |
  | 0.2455        | 7.99  | 5500  | 0.3981          | 0.3604 |
  | 0.2512        | 8.72  | 6000  | 0.4203          | 0.3612 |
  | 0.221         | 9.45  | 6500  | 0.4073          | 0.3560 |
  | 0.19          | 10.17 | 7000  | 0.4206          | 0.3547 |
  | 0.207         | 10.9  | 7500  | 0.3992          | 0.3517 |
  | 0.187         | 11.63 | 8000  | 0.4078          | 0.3517 |
  | 0.2029        | 12.35 | 8500  | 0.4143          | 0.3469 |
  | 0.171         | 13.08 | 9000  | 0.4007          | 0.3430 |
  | 0.1658        | 13.81 | 9500  | 0.3862          | 0.3422 |
  | 0.2021        | 14.53 | 10000 | 0.4132          | 0.3454 |
  | 0.165         | 15.26 | 10500 | 0.3997          | 0.3407 |
  | 0.1562        | 15.99 | 11000 | 0.4069          | 0.3416 |
  | 0.1613        | 16.71 | 11500 | 0.4040          | 0.3393 |
  | 0.1713        | 17.44 | 12000 | 0.4094          | 0.3411 |
  | 0.1541        | 18.17 | 12500 | 0.4043          | 0.3367 |
  | 0.144         | 18.89 | 13000 | 0.4086          | 0.3374 |
  | 0.1483        | 19.62 | 13500 | 0.4168          | 0.3383 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu102
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  