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wav2vec2-classifier-aug

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metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: wav2vec2-classifier-aug
    results: []

wav2vec2-classifier-aug

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5202
  • Accuracy: 0.8679
  • Precision: 0.8908
  • Recall: 0.8679
  • F1: 0.8667
  • Binary: 0.9067

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: 0.0001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • 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_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.19 50 4.4234 0.0162 0.0017 0.0162 0.0031 0.1558
No log 0.38 100 4.2882 0.0350 0.0028 0.0350 0.0047 0.3075
No log 0.58 150 3.9749 0.0404 0.0024 0.0404 0.0043 0.3170
No log 0.77 200 3.7072 0.0458 0.0070 0.0458 0.0109 0.3296
No log 0.96 250 3.4794 0.0836 0.0233 0.0836 0.0217 0.3580
4.1218 1.15 300 3.2647 0.1321 0.0526 0.1321 0.0640 0.3930
4.1218 1.34 350 3.0118 0.2318 0.1558 0.2318 0.1503 0.4623
4.1218 1.53 400 2.7772 0.2642 0.1570 0.2642 0.1752 0.4849
4.1218 1.73 450 2.5522 0.3585 0.3222 0.3585 0.2848 0.5520
4.1218 1.92 500 2.3428 0.3342 0.2725 0.3342 0.2563 0.5372
3.1065 2.11 550 2.0580 0.4124 0.3332 0.4124 0.3326 0.5887
3.1065 2.3 600 1.8454 0.4771 0.4322 0.4771 0.4131 0.6323
3.1065 2.49 650 1.6830 0.5310 0.4926 0.5310 0.4771 0.6733
3.1065 2.68 700 1.5545 0.5580 0.5326 0.5580 0.5096 0.6898
3.1065 2.88 750 1.3593 0.6253 0.5975 0.6253 0.5812 0.7388
2.2273 3.07 800 1.2047 0.6927 0.6715 0.6927 0.6535 0.7849
2.2273 3.26 850 1.1223 0.6765 0.6662 0.6765 0.6461 0.7728
2.2273 3.45 900 1.0296 0.7062 0.7121 0.7062 0.6756 0.7943
2.2273 3.64 950 1.0001 0.7251 0.7388 0.7251 0.7074 0.8081
2.2273 3.84 1000 0.9879 0.7466 0.7650 0.7466 0.7265 0.8229
1.734 4.03 1050 0.9078 0.7466 0.7590 0.7466 0.7323 0.8237
1.734 4.22 1100 0.8344 0.7898 0.8284 0.7898 0.7794 0.8550
1.734 4.41 1150 0.8199 0.7925 0.8029 0.7925 0.7749 0.8558
1.734 4.6 1200 0.7227 0.7951 0.8309 0.7951 0.7892 0.8566
1.734 4.79 1250 0.7666 0.7871 0.8246 0.7871 0.7768 0.8520
1.734 4.99 1300 0.7529 0.7871 0.7989 0.7871 0.7768 0.8531
1.4492 5.18 1350 0.7035 0.8032 0.8287 0.8032 0.7986 0.8633
1.4492 5.37 1400 0.6597 0.8194 0.8522 0.8194 0.8141 0.8739
1.4492 5.56 1450 0.6592 0.8113 0.8472 0.8113 0.8108 0.8690
1.4492 5.75 1500 0.6535 0.8248 0.8547 0.8248 0.8203 0.8784
1.4492 5.94 1550 0.6343 0.8167 0.8568 0.8167 0.8116 0.8701
1.2533 6.14 1600 0.5640 0.8356 0.8589 0.8356 0.8329 0.8860
1.2533 6.33 1650 0.5465 0.8383 0.8669 0.8383 0.8341 0.8889
1.2533 6.52 1700 0.5594 0.8248 0.8549 0.8248 0.8204 0.8776
1.2533 6.71 1750 0.5765 0.8464 0.8776 0.8464 0.8463 0.8935
1.2533 6.9 1800 0.5169 0.8571 0.8758 0.8571 0.8543 0.9000
1.138 7.09 1850 0.5206 0.8410 0.8676 0.8410 0.8421 0.8887
1.138 7.29 1900 0.5258 0.8544 0.8779 0.8544 0.8537 0.8992
1.138 7.48 1950 0.5855 0.8383 0.8693 0.8383 0.8384 0.8879
1.138 7.67 2000 0.5209 0.8491 0.8800 0.8491 0.8493 0.8943
1.138 7.86 2050 0.5150 0.8410 0.8710 0.8410 0.8411 0.8889
1.0249 8.05 2100 0.4937 0.8571 0.8840 0.8571 0.8568 0.9022
1.0249 8.25 2150 0.5344 0.8518 0.8790 0.8518 0.8492 0.8995
1.0249 8.44 2200 0.5322 0.8437 0.8751 0.8437 0.8428 0.8927
1.0249 8.63 2250 0.5533 0.8248 0.8561 0.8248 0.8233 0.8774
1.0249 8.82 2300 0.5242 0.8491 0.8797 0.8491 0.8469 0.8943
0.9523 9.01 2350 0.4938 0.8679 0.8911 0.8679 0.8669 0.9075
0.9523 9.2 2400 0.5037 0.8625 0.8888 0.8625 0.8627 0.9038
0.9523 9.4 2450 0.4973 0.8571 0.8794 0.8571 0.8565 0.9000
0.9523 9.59 2500 0.5343 0.8383 0.8705 0.8383 0.8384 0.8868
0.9523 9.78 2550 0.5493 0.8491 0.8746 0.8491 0.8472 0.8943
0.9523 9.97 2600 0.5226 0.8544 0.8783 0.8544 0.8537 0.8981
0.8792 10.16 2650 0.4883 0.8625 0.8857 0.8625 0.8598 0.9038
0.8792 10.35 2700 0.5178 0.8518 0.8784 0.8518 0.8503 0.8962
0.8792 10.55 2750 0.6273 0.8383 0.8756 0.8383 0.8363 0.8879
0.8792 10.74 2800 0.5229 0.8571 0.8855 0.8571 0.8576 0.9000
0.8792 10.93 2850 0.4617 0.8706 0.8924 0.8706 0.8686 0.9094
0.8251 11.12 2900 0.5764 0.8625 0.8874 0.8625 0.8626 0.9038
0.8251 11.31 2950 0.5111 0.8706 0.8960 0.8706 0.8689 0.9094
0.8251 11.51 3000 0.6013 0.8437 0.8603 0.8437 0.8410 0.8906
0.8251 11.7 3050 0.5968 0.8437 0.8682 0.8437 0.8405 0.8916
0.8251 11.89 3100 0.5467 0.8544 0.8806 0.8544 0.8542 0.8981
0.7578 12.08 3150 0.6015 0.8544 0.8774 0.8544 0.8523 0.8981
0.7578 12.27 3200 0.4897 0.8679 0.8814 0.8679 0.8632 0.9067
0.7578 12.46 3250 0.5395 0.8491 0.8765 0.8491 0.8460 0.8935
0.7578 12.66 3300 0.5873 0.8491 0.8767 0.8491 0.8489 0.8935
0.7578 12.85 3350 0.5386 0.8491 0.8735 0.8491 0.8498 0.8935
0.7295 13.04 3400 0.5826 0.8652 0.8949 0.8652 0.8663 0.9057
0.7295 13.23 3450 0.5358 0.8571 0.8859 0.8571 0.8562 0.9003
0.7295 13.42 3500 0.4802 0.8841 0.9017 0.8841 0.8838 0.9173
0.7295 13.61 3550 0.5709 0.8410 0.8692 0.8410 0.8404 0.8879
0.7295 13.81 3600 0.5420 0.8544 0.8738 0.8544 0.8535 0.8992
0.7295 14.0 3650 0.5384 0.8652 0.8817 0.8652 0.8635 0.9049
0.6874 14.19 3700 0.4911 0.8598 0.8753 0.8598 0.8593 0.9019
0.6874 14.38 3750 0.5172 0.8598 0.8826 0.8598 0.8588 0.9011
0.6874 14.57 3800 0.5024 0.8598 0.8814 0.8598 0.8592 0.9019
0.6874 14.77 3850 0.5202 0.8679 0.8908 0.8679 0.8667 0.9067

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1