readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - audiofolder
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-base-finetuned-stop-classification
    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-finetuned-stop-classification
  
  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: 0.1647
  - Accuracy: 0.9470
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.671         | 0.98  | 26   | 0.5553          | 0.8347   |
  | 0.3525        | 1.98  | 52   | 0.2647          | 0.9163   |
  | 0.291         | 2.98  | 78   | 0.2474          | 0.9070   |
  | 0.2733        | 3.98  | 104  | 0.1729          | 0.9439   |
  | 0.2467        | 4.98  | 130  | 0.1647          | 0.9470   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  