readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - audiofolder
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: wav2vec2-base-is_vinyl_scratched_or_not
    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-is_vinyl_scratched_or_not
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1039
  - Accuracy: 0.9752
  - F1: 0.9638
  - Recall: 0.9576
  - Precision: 0.9700
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.6671        | 0.98  | 21   | 0.6235          | 0.6560   | 0.0    | 0.0    | 0.0       |
  | 0.4954        | 1.98  | 42   | 0.2824          | 0.9417   | 0.9095 | 0.8517 | 0.9757    |
  | 0.2406        | 2.98  | 63   | 0.1755          | 0.9563   | 0.9336 | 0.8941 | 0.9769    |
  | 0.169         | 3.98  | 84   | 0.1545          | 0.9592   | 0.9386 | 0.9068 | 0.9727    |
  | 0.1287        | 4.98  | 105  | 0.1249          | 0.9606   | 0.9407 | 0.9068 | 0.9772    |
  | 0.1102        | 5.98  | 126  | 0.1159          | 0.9723   | 0.9595 | 0.9534 | 0.9657    |
  | 0.0923        | 6.98  | 147  | 0.1073          | 0.9665   | 0.9516 | 0.9576 | 0.9456    |
  | 0.0877        | 7.98  | 168  | 0.1039          | 0.9752   | 0.9638 | 0.9576 | 0.9700    |
  | 0.0807        | 8.98  | 189  | 0.1088          | 0.9679   | 0.9536 | 0.9576 | 0.9496    |
  | 0.0744        | 9.98  | 210  | 0.1041          | 0.9752   | 0.9638 | 0.9576 | 0.9700    |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.12.1
  - Datasets 2.8.0
  - Tokenizers 0.12.1
  