readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - vivos_dataset
  model-index:
  - name: wav2vec2-base-vios-v2
    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-v2
  
  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: 0.6056
  - Wer: 0.2442
  
  ## 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: 20
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 7.8344        | 0.69  | 500   | 3.5012          | 1.0    |
  | 3.4505        | 1.37  | 1000  | 3.4081          | 1.0    |
  | 2.1426        | 2.06  | 1500  | 0.8761          | 0.6241 |
  | 0.8801        | 2.74  | 2000  | 0.5476          | 0.4241 |
  | 0.6453        | 3.43  | 2500  | 0.4384          | 0.3495 |
  | 0.5449        | 4.12  | 3000  | 0.4055          | 0.3160 |
  | 0.4862        | 4.8   | 3500  | 0.3815          | 0.3002 |
  | 0.4435        | 5.49  | 4000  | 0.3525          | 0.2776 |
  | 0.4205        | 6.17  | 4500  | 0.3660          | 0.2725 |
  | 0.3974        | 6.86  | 5000  | 0.3386          | 0.2565 |
  | 0.3758        | 7.54  | 5500  | 0.3492          | 0.2607 |
  | 0.3595        | 8.23  | 6000  | 0.3391          | 0.2441 |
  | 0.3438        | 8.92  | 6500  | 0.3255          | 0.2354 |
  | 0.3308        | 9.6   | 7000  | 0.3379          | 0.2422 |
  | 0.3265        | 10.29 | 7500  | 0.3375          | 0.2349 |
  | 0.311         | 10.97 | 8000  | 0.3356          | 0.2306 |
  | 0.3071        | 11.66 | 8500  | 0.3286          | 0.2249 |
  | 0.2941        | 12.35 | 9000  | 0.3176          | 0.2211 |
  | 0.296         | 13.03 | 9500  | 0.3268          | 0.2257 |
  | 0.2852        | 13.72 | 10000 | 0.3265          | 0.2196 |
  | 0.3102        | 14.4  | 10500 | 0.3390          | 0.2209 |
  | 0.2974        | 15.09 | 11000 | 0.3493          | 0.2199 |
  | 0.3433        | 15.78 | 11500 | 0.3687          | 0.2199 |
  | 0.3526        | 16.46 | 12000 | 0.3698          | 0.2170 |
  | 0.36          | 17.15 | 12500 | 0.4110          | 0.2227 |
  | 0.4322        | 17.83 | 13000 | 0.4830          | 0.2290 |
  | 0.4973        | 18.52 | 13500 | 0.5280          | 0.2356 |
  | 0.5701        | 19.2  | 14000 | 0.5990          | 0.2370 |
  | 0.6014        | 19.89 | 14500 | 0.6056          | 0.2442 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.3
  - Tokenizers 0.12.1
  