readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - vivos_dataset
  model-index:
  - name: wav2vec2-dataset-vios
    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-dataset-vios
  
  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: 0.5423
  - Wer: 0.4075
  
  ## 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: 0.0003
  - 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: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 5.0963        | 1.1   | 400   | 1.1336          | 0.7374 |
  | 0.6576        | 2.2   | 800   | 0.4716          | 0.3727 |
  | 0.4099        | 3.3   | 1200  | 0.3907          | 0.3100 |
  | 0.3332        | 4.4   | 1600  | 0.3735          | 0.2766 |
  | 0.2976        | 5.49  | 2000  | 0.3932          | 0.2801 |
  | 0.2645        | 6.59  | 2400  | 0.3628          | 0.2542 |
  | 0.2395        | 7.69  | 2800  | 0.3702          | 0.2734 |
  | 0.2208        | 8.79  | 3200  | 0.3667          | 0.2467 |
  | 0.1974        | 9.89  | 3600  | 0.3688          | 0.2398 |
  | 0.1772        | 10.99 | 4000  | 0.3819          | 0.2457 |
  | 0.1695        | 12.09 | 4400  | 0.3840          | 0.2451 |
  | 0.319         | 13.19 | 4800  | 0.6531          | 0.4084 |
  | 0.7305        | 14.29 | 5200  | 0.9883          | 0.6348 |
  | 0.5787        | 15.38 | 5600  | 0.5260          | 0.3063 |
  | 0.8558        | 16.48 | 6000  | 1.2870          | 0.7692 |
  | 1.155         | 17.58 | 6400  | 1.0568          | 0.6353 |
  | 0.8393        | 18.68 | 6800  | 0.7360          | 0.4486 |
  | 0.6094        | 19.78 | 7200  | 0.6072          | 0.4108 |
  | 0.5346        | 20.88 | 7600  | 0.5749          | 0.4095 |
  | 0.5073        | 21.98 | 8000  | 0.5588          | 0.4056 |
  | 0.4859        | 23.08 | 8400  | 0.5475          | 0.4015 |
  | 0.475         | 24.18 | 8800  | 0.5430          | 0.4011 |
  | 0.4683        | 25.27 | 9200  | 0.5400          | 0.3990 |
  | 0.4673        | 26.37 | 9600  | 0.5407          | 0.4011 |
  | 0.4665        | 27.47 | 10000 | 0.5408          | 0.3992 |
  | 0.4703        | 28.57 | 10400 | 0.5420          | 0.4070 |
  | 0.4709        | 29.67 | 10800 | 0.5423          | 0.4075 |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.10.3
  