readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - vivos_dataset
  model-index:
  - name: wav2vec2-base-vios-v4
    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-base-vios-v4
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the vivos_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3198
  - Wer: 0.2169
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 7.8138        | 0.69  | 500   | 3.5011          | 1.0    |
  | 3.4372        | 1.37  | 1000  | 3.3447          | 1.0    |
  | 1.9519        | 2.06  | 1500  | 0.8356          | 0.5944 |
  | 0.8581        | 2.74  | 2000  | 0.5280          | 0.4038 |
  | 0.6405        | 3.43  | 2500  | 0.4410          | 0.3410 |
  | 0.5417        | 4.12  | 3000  | 0.3990          | 0.3140 |
  | 0.4804        | 4.8   | 3500  | 0.3804          | 0.2973 |
  | 0.4384        | 5.49  | 4000  | 0.3644          | 0.2808 |
  | 0.4162        | 6.17  | 4500  | 0.3542          | 0.2648 |
  | 0.3941        | 6.86  | 5000  | 0.3436          | 0.2529 |
  | 0.3733        | 7.54  | 5500  | 0.3355          | 0.2520 |
  | 0.3564        | 8.23  | 6000  | 0.3294          | 0.2415 |
  | 0.3412        | 8.92  | 6500  | 0.3311          | 0.2332 |
  | 0.3266        | 9.6   | 7000  | 0.3217          | 0.2325 |
  | 0.3226        | 10.29 | 7500  | 0.3317          | 0.2303 |
  | 0.3115        | 10.97 | 8000  | 0.3226          | 0.2279 |
  | 0.3094        | 11.66 | 8500  | 0.3157          | 0.2236 |
  | 0.2967        | 12.35 | 9000  | 0.3109          | 0.2202 |
  | 0.2995        | 13.03 | 9500  | 0.3129          | 0.2156 |
  | 0.2895        | 13.72 | 10000 | 0.3195          | 0.2146 |
  | 0.3089        | 14.4  | 10500 | 0.3198          | 0.2169 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  