readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - wer
  model-index:
  - name: model_broadclass_onSet0.1
    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. -->
  
  # model_broadclass_onSet0.1
  
  This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1129
  - 0 Precision: 1.0
  - 0 Recall: 1.0
  - 0 F1-score: 1.0
  - 0 Support: 31
  - 1 Precision: 0.9259
  - 1 Recall: 1.0
  - 1 F1-score: 0.9615
  - 1 Support: 25
  - 2 Precision: 1.0
  - 2 Recall: 0.9259
  - 2 F1-score: 0.9615
  - 2 Support: 27
  - 3 Precision: 1.0
  - 3 Recall: 1.0
  - 3 F1-score: 1.0
  - 3 Support: 15
  - Accuracy: 0.9796
  - Macro avg Precision: 0.9815
  - Macro avg Recall: 0.9815
  - Macro avg F1-score: 0.9808
  - Macro avg Support: 98
  - Weighted avg Precision: 0.9811
  - Weighted avg Recall: 0.9796
  - Weighted avg F1-score: 0.9796
  - Weighted avg Support: 98
  - Wer: 0.0859
  - Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 2, 25, 0], [3, 0, 0, 0, 15]]
  
  ## 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: 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: 200
  - num_epochs: 80
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer    | Mtrix                                                                                   |
  |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:|
  | 2.343         | 4.16  | 100  | 2.2083          | 0.3163      | 1.0      | 0.4806     | 31        | 0.0         | 0.0      | 0.0        | 25        | 0.0         | 0.0      | 0.0        | 27        | 0.0         | 0.0      | 0.0        | 15        | 0.3163   | 0.0791              | 0.25             | 0.1202             | 98                | 0.1001                 | 0.3163              | 0.1520                | 98                   | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]  |
  | 2.2769        | 8.33  | 200  | 2.1649          | 0.3163      | 1.0      | 0.4806     | 31        | 0.0         | 0.0      | 0.0        | 25        | 0.0         | 0.0      | 0.0        | 27        | 0.0         | 0.0      | 0.0        | 15        | 0.3163   | 0.0791              | 0.25             | 0.1202             | 98                | 0.1001                 | 0.3163              | 0.1520                | 98                   | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]  |
  | 1.9687        | 12.49 | 300  | 1.8723          | 0.3163      | 1.0      | 0.4806     | 31        | 0.0         | 0.0      | 0.0        | 25        | 0.0         | 0.0      | 0.0        | 27        | 0.0         | 0.0      | 0.0        | 15        | 0.3163   | 0.0791              | 0.25             | 0.1202             | 98                | 0.1001                 | 0.3163              | 0.1520                | 98                   | 0.9847 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]  |
  | 1.8046        | 16.65 | 400  | 1.6982          | 0.3163      | 1.0      | 0.4806     | 31        | 0.0         | 0.0      | 0.0        | 25        | 0.0         | 0.0      | 0.0        | 27        | 0.0         | 0.0      | 0.0        | 15        | 0.3163   | 0.0791              | 0.25             | 0.1202             | 98                | 0.1001                 | 0.3163              | 0.1520                | 98                   | 0.9847 | [[0, 1, 2, 3], [0, 31,