readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - wer
  model-index:
  - name: model_syllable_onSet2
    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_syllable_onSet2
  
  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: 0.4231
  - 0 Precision: 1.0
  - 0 Recall: 0.96
  - 0 F1-score: 0.9796
  - 0 Support: 25
  - 1 Precision: 0.9643
  - 1 Recall: 0.9643
  - 1 F1-score: 0.9643
  - 1 Support: 28
  - 2 Precision: 1.0
  - 2 Recall: 0.9643
  - 2 F1-score: 0.9818
  - 2 Support: 28
  - 3 Precision: 0.8889
  - 3 Recall: 1.0
  - 3 F1-score: 0.9412
  - 3 Support: 16
  - Accuracy: 0.9691
  - Macro avg Precision: 0.9633
  - Macro avg Recall: 0.9721
  - Macro avg F1-score: 0.9667
  - Macro avg Support: 97
  - Weighted avg Precision: 0.9714
  - Weighted avg Recall: 0.9691
  - Weighted avg F1-score: 0.9695
  - Weighted avg Support: 97
  - Wer: 0.2827
  - Mtrix: [[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
  
  ## 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: 70
  - 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                                                                                  |
  |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:|
  | 1.3102        | 4.16  | 100  | 1.2133          | 0.125       | 0.04     | 0.0606     | 25        | 0.0         | 0.0      | 0.0        | 28        | 0.3146      | 1.0      | 0.4786     | 28        | 0.0         | 0.0      | 0.0        | 16        | 0.2990   | 0.1099              | 0.26             | 0.1348             | 97                | 0.1230                 | 0.2990              | 0.1538                | 97                   | 0.9676 | [[0, 1, 2, 3], [0, 1, 0, 24, 0], [1, 7, 0, 21, 0], [2, 0, 0, 28, 0], [3, 0, 0, 16, 0]] |
  | 0.7368        | 8.33  | 200  | 0.7100          | 1.0         | 0.72     | 0.8372     | 25        | 0.3333      | 0.0357   | 0.0645     | 28        | 0.3684      | 1.0      | 0.5385     | 28        | 0.0         | 0.0      | 0.0        | 16        | 0.4845   | 0.4254              | 0.4389           | 0.3600             | 97                | 0.4603                 | 0.4845              | 0.3898                | 97                   | 0.8227 | [[0, 1, 2, 3], [0, 18, 2, 5, 0], [1, 0, 1, 27, 0], [2, 0, 0, 28, 0], [3, 0, 0, 16, 0]] |
  | 0.3813        | 12.49 | 300  | 0.3802          | 0.8519      | 0.92     | 0.8846     | 25        | 0.7333      | 0.7857   | 0.7586     | 28        | 0.9231      | 0.8571   | 0.8889     | 28        | 0.9286      | 0.8125   | 0.8667     | 16        | 0.8454   | 0.8592              | 0.8438           | 0.8497             | 97                | 0.8509                 | 0.8454              | 0.8465                | 97                   | 0.7694 | [[0, 1, 2, 3], [0, 23, 2, 0, 0], [1, 4, 22, 2, 0], [2, 0, 3, 24, 1], [3, 0, 3, 0, 13]] |
  | 0.2761        | 16.65 | 400  | 0.2263          | 1.0         | 1.0      | 1.0        | 25        | 1.0         | 0.9643   | 0.9818     | 28        | 1.0         | 0.9643   | 0.9818     | 28        | 0.8889      | 1.0      | 0.9412     | 16        | 0.9794   | 0.9722              | 0.9821           | 0.9762             | 97                | 0.9817                 | 0.9794              | 0.9798                | 97                   | 0.4392 | [[0, 1, 2, 3], [0, 25,