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model_broadclass_onSet4

Automatic Speech Recognition
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wav2vec2
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - wer
model-index:
  - name: model_broadclass_onSet4
    results: []

model_broadclass_onSet4

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1340
  • 0 Precision: 1.0
  • 0 Recall: 0.9615
  • 0 F1-score: 0.9804
  • 0 Support: 26
  • 1 Precision: 1.0
  • 1 Recall: 1.0
  • 1 F1-score: 1.0
  • 1 Support: 32
  • 2 Precision: 1.0
  • 2 Recall: 0.9643
  • 2 F1-score: 0.9818
  • 2 Support: 28
  • 3 Precision: 0.8462
  • 3 Recall: 1.0
  • 3 F1-score: 0.9167
  • 3 Support: 11
  • Accuracy: 0.9794
  • Macro avg Precision: 0.9615
  • Macro avg Recall: 0.9815
  • Macro avg F1-score: 0.9697
  • Macro avg Support: 97
  • Weighted avg Precision: 0.9826
  • Weighted avg Recall: 0.9794
  • Weighted avg F1-score: 0.9800
  • Weighted avg Support: 97
  • Wer: 0.1098
  • Mtrix: [[0, 1, 2, 3], [0, 25, 0, 0, 1], [1, 0, 32, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 11]]

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
2.337 4.16 100 2.1761 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
2.2604 8.33 200 2.0783 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.9239 12.49 300 1.8395 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.7002 16.65 400 1.7194 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.611 20.82 500 1.5619 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.486 24.98 600 1.5283 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.6085 29.16 700 1.5041 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9869 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.5607 33.33 800 1.4456 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9945 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
1.3499 37.49 900 1.2898 0.2680 1.0 0.4228 26 0.0 0.0 0.0 32 0.0 0.0 0.0 28 0.0 0.0 0.0 11 0.2680 0.0670 0.25 0.1057 97 0.0718 0.2680 0.1133 97 0.9970 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 32, 0, 0, 0], [2, 28, 0, 0, 0], [3, 11, 0, 0, 0]]
0.9722 41.65 1000 0.9757 0.3133 1.0 0.4771 26 1.0 0.1562 0.2703 32 1.0 0.1786 0.3030 28 1.0 0.3636 0.5333 11 0.4124 0.8283 0.4246 0.3959 97 0.8159 0.4124 0.3650 97 0.9612 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 27, 5, 0, 0], [2, 23, 0, 5, 0], [3, 7, 0, 0, 4]]
0.9679 45.82 1100 0.9452 0.4333 1.0 0.6047 26 0.9630 0.8125 0.8814 32 1.0 0.3214 0.4865 28 1.0 0.0909 0.1667 11 0.6392 0.8491 0.5562 0.5348 97 0.8359 0.6392 0.6122 97 0.9406 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 6, 26, 0, 0], [2, 18, 1, 9, 0], [3, 10, 0, 0, 1]]
0.9206 49.98 1200 0.9031 0.5909 1.0 0.7429 26 1.0 0.9062 0.9508 32 1.0 0.7143 0.8333 28 1.0 0.3636 0.5333 11 0.8144 0.8977 0.7460 0.7651 97 0.8903 0.8144 0.8138 97 0.9250 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 3, 29, 0, 0], [2, 8, 0, 20, 0], [3, 7, 0, 0, 4]]
0.9223 54.16 1300 0.8607 0.8125 1.0 0.8966 26 1.0 0.875 0.9333 32 1.0 0.9643 0.9818 28 1.0 0.9091 0.9524 11 0.9381 0.9531 0.9371 0.9410 97 0.9497 0.9381 0.9396 97 0.9366 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 4, 28, 0, 0], [2, 1, 0, 27, 0], [3, 1, 0, 0, 10]]
0.8407 58.33 1400 0.8011 0.8929 0.9615 0.9259 26 1.0 0.9688 0.9841 32 1.0 0.8929 0.9434 28 0.8462 1.0 0.9167 11 0.9485 0.9348 0.9558 0.9425 97 0.9538 0.9485 0.9491 97 0.9381 [[0, 1, 2, 3], [0, 25, 0, 0, 1], [1, 1, 31, 0, 0], [2, 2, 0, 25, 1], [3, 0, 0, 0, 11]]
0.7359 62.49 1500 0.7210 0.8966 1.0 0.9455 26 1.0 0.9375 0.9677 32 1.0 0.9286 0.9630 28 0.9167 1.0 0.9565 11 0.9588 0.9533 0.9665 0.9582 97 0.9628 0.9588 0.9591 97 0.9220 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 2, 30, 0, 0], [2, 1, 0, 26, 1], [3, 0, 0, 0, 11]]
0.5479 66.65 1600 0.4813 1.0 1.0 1.0 26 1.0 1.0 1.0 32 1.0 0.9643 0.9818 28 0.9167 1.0 0.9565 11 0.9897 0.9792 0.9911 0.9846 97 0.9905 0.9897 0.9898 97 0.7447 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 32, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 11]]
0.2617 70.82 1700 0.2138 1.0 1.0 1.0 26 1.0 1.0 1.0 32 1.0 0.9643 0.9818 28 0.9167 1.0 0.9565 11 0.9897 0.9792 0.9911 0.9846 97 0.9905 0.9897 0.9898 97 0.1692 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 32, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 11]]
0.2186 74.98 1800 0.1412 1.0 1.0 1.0 26 1.0 1.0 1.0 32 1.0 0.9643 0.9818 28 0.9167 1.0 0.9565 11 0.9897 0.9792 0.9911 0.9846 97 0.9905 0.9897 0.9898 97 0.1269 [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 0, 32, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 11]]
0.2303 79.16 1900 0.1344 1.0 0.9615 0.9804 26 1.0 1.0 1.0 32 1.0 0.9643 0.9818 28 0.8462 1.0 0.9167 11 0.9794 0.9615 0.9815 0.9697 97 0.9826 0.9794 0.9800 97 0.1113 [[0, 1, 2, 3], [0, 25, 0, 0, 1], [1, 0, 32, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 11]]

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2