readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - wer
  model-index:
  - name: model_syllable_onSet4
    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_onSet4
  
  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: 0.1349
  - 0 Precision: 1.0
  - 0 Recall: 1.0
  - 0 F1-score: 1.0
  - 0 Support: 26
  - 1 Precision: 1.0
  - 1 Recall: 0.9677
  - 1 F1-score: 0.9836
  - 1 Support: 31
  - 2 Precision: 0.9630
  - 2 Recall: 1.0
  - 2 F1-score: 0.9811
  - 2 Support: 26
  - 3 Precision: 1.0
  - 3 Recall: 1.0
  - 3 F1-score: 1.0
  - 3 Support: 14
  - Accuracy: 0.9897
  - Macro avg Precision: 0.9907
  - Macro avg Recall: 0.9919
  - Macro avg F1-score: 0.9912
  - Macro avg Support: 97
  - Weighted avg Precision: 0.9901
  - Weighted avg Recall: 0.9897
  - Weighted avg F1-score: 0.9897
  - Weighted avg Support: 97
  - Wer: 0.2258
  - Mtrix: [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 30, 1, 0], [2, 0, 0, 26, 0], [3, 0, 0, 0, 14]]
  
  ## 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                                                                                  |
  |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:|
  | 1.6602        | 4.16  | 100  | 1.5639          | 0.0         | 0.0      | 0.0        | 26        | 0.0         | 0.0      | 0.0        | 31        | 0.2584      | 0.8846   | 0.4        | 26        | 0.0         | 0.0      | 0.0        | 14        | 0.2371   | 0.0646              | 0.2212           | 0.1                | 97                | 0.0693                 | 0.2371              | 0.1072                | 97                   | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]]  |
  | 1.616         | 8.33  | 200  | 1.4203          | 0.0         | 0.0      | 0.0        | 26        | 0.0         | 0.0      | 0.0        | 31        | 0.2584      | 0.8846   | 0.4        | 26        | 0.0         | 0.0      | 0.0        | 14        | 0.2371   | 0.0646              | 0.2212           | 0.1                | 97                | 0.0693                 | 0.2371              | 0.1072                | 97                   | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]]  |
  | 1.2107        | 12.49 | 300  | 1.1249          | 0.0         | 0.0      | 0.0        | 26        | 0.0         | 0.0      | 0.0        | 31        | 0.2584      | 0.8846   | 0.4        | 26        | 0.0         | 0.0      | 0.0        | 14        | 0.2371   | 0.0646              | 0.2212           | 0.1                | 97                | 0.0693                 | 0.2371              | 0.1072                | 97                   | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1, 0, 0, 31, 0], [2, 3, 0, 23, 0], [3, 5, 0, 9, 0]]  |
  | 1.1283        | 16.65 | 400  | 1.0201          | 0.0         | 0.0      | 0.0        | 26        | 0.0         | 0.0      | 0.0        | 31        | 0.2584      | 0.8846   | 0.4        | 26        | 0.0         | 0.0      | 0.0        | 14        | 0.2371   | 0.0646              | 0.2212           | 0.1                | 97                | 0.0693                 | 0.2371              | 0.1072                | 97                   | 0.9732 | [[0, 1, 2, 3], [0, 0, 0, 26, 0], [1