readme: ---
  license: apache-2.0
  tags:
  - audio-classification
  - generated_from_trainer
  datasets:
  - superb
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-base-keyword-spotting
    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-keyword-spotting
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0746
  - Accuracy: 0.9843
  
  ## 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: 0
  - 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: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8279        | 1.0   | 399  | 0.6792          | 0.8558   |
  | 0.2961        | 2.0   | 798  | 0.1383          | 0.9798   |
  | 0.2069        | 3.0   | 1197 | 0.0972          | 0.9809   |
  | 0.1757        | 4.0   | 1596 | 0.0843          | 0.9825   |
  | 0.1607        | 5.0   | 1995 | 0.0746          | 0.9843   |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.1+cu111
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  