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hubert-classifier-aug

Audio Classification
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hubert
Generated from Trainer
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  1. README.md +76 -59
  2. model.safetensors +1 -1
  3. runs/Jul11_01-27-19_LAPTOP-1GID9RGH/events.out.tfevents.1720636039.LAPTOP-1GID9RGH.23200.0 +2 -2
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5264
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- - Accuracy: 0.8625
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- - Precision: 0.8662
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- - Recall: 0.8625
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- - F1: 0.8517
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- - Binary: 0.9030
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  ## Model description
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@@ -52,65 +52,82 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 10
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | No log | 0.19 | 50 | 3.8945 | 0.0566 | 0.0077 | 0.0566 | 0.0127 | 0.3326 |
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- | No log | 0.38 | 100 | 3.4610 | 0.0701 | 0.0208 | 0.0701 | 0.0174 | 0.3418 |
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- | No log | 0.58 | 150 | 3.2223 | 0.1051 | 0.0294 | 0.1051 | 0.0364 | 0.3720 |
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- | No log | 0.77 | 200 | 3.1153 | 0.1294 | 0.0504 | 0.1294 | 0.0565 | 0.3795 |
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- | No log | 0.96 | 250 | 2.8292 | 0.1914 | 0.1010 | 0.1914 | 0.1073 | 0.4315 |
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- | No log | 1.15 | 300 | 2.7080 | 0.2264 | 0.1522 | 0.2264 | 0.1461 | 0.4496 |
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- | No log | 1.34 | 350 | 2.4083 | 0.2776 | 0.1986 | 0.2776 | 0.1896 | 0.4935 |
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- | No log | 1.53 | 400 | 2.2517 | 0.3720 | 0.2762 | 0.3720 | 0.2845 | 0.5580 |
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- | No log | 1.73 | 450 | 2.1201 | 0.3908 | 0.3501 | 0.3908 | 0.3098 | 0.5712 |
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- | 3.1927 | 1.92 | 500 | 1.9149 | 0.4582 | 0.3806 | 0.4582 | 0.3781 | 0.6210 |
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- | 3.1927 | 2.11 | 550 | 1.7920 | 0.5013 | 0.4684 | 0.5013 | 0.4456 | 0.6515 |
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- | 3.1927 | 2.3 | 600 | 1.5973 | 0.5418 | 0.4910 | 0.5418 | 0.4765 | 0.6803 |
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- | 3.1927 | 2.49 | 650 | 1.5067 | 0.5957 | 0.5572 | 0.5957 | 0.5409 | 0.7162 |
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- | 3.1927 | 2.68 | 700 | 1.3985 | 0.6253 | 0.6046 | 0.6253 | 0.5740 | 0.7361 |
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- | 3.1927 | 2.88 | 750 | 1.3198 | 0.6604 | 0.6224 | 0.6604 | 0.6114 | 0.7623 |
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- | 3.1927 | 3.07 | 800 | 1.2483 | 0.6685 | 0.6709 | 0.6685 | 0.6273 | 0.7674 |
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- | 3.1927 | 3.26 | 850 | 1.1560 | 0.7116 | 0.7063 | 0.7116 | 0.6710 | 0.7973 |
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- | 3.1927 | 3.45 | 900 | 1.0992 | 0.7197 | 0.7345 | 0.7197 | 0.6872 | 0.8030 |
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- | 3.1927 | 3.64 | 950 | 1.1148 | 0.7143 | 0.7477 | 0.7143 | 0.6918 | 0.7992 |
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- | 2.0117 | 3.84 | 1000 | 0.9688 | 0.7682 | 0.7634 | 0.7682 | 0.7404 | 0.8369 |
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- | 2.0117 | 4.03 | 1050 | 0.9990 | 0.7062 | 0.7148 | 0.7062 | 0.6717 | 0.7927 |
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- | 2.0117 | 4.22 | 1100 | 0.9516 | 0.7412 | 0.7619 | 0.7412 | 0.7229 | 0.8199 |
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- | 2.0117 | 4.41 | 1150 | 0.8740 | 0.7763 | 0.7947 | 0.7763 | 0.7582 | 0.8426 |
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- | 2.0117 | 4.6 | 1200 | 0.8611 | 0.7682 | 0.7800 | 0.7682 | 0.7469 | 0.8388 |
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- | 2.0117 | 4.79 | 1250 | 0.7992 | 0.7898 | 0.8228 | 0.7898 | 0.7775 | 0.8539 |
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- | 2.0117 | 4.99 | 1300 | 0.8161 | 0.7898 | 0.8209 | 0.7898 | 0.7756 | 0.8512 |
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- | 2.0117 | 5.18 | 1350 | 0.7420 | 0.7925 | 0.8144 | 0.7925 | 0.7768 | 0.8539 |
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- | 2.0117 | 5.37 | 1400 | 0.7420 | 0.7925 | 0.8070 | 0.7925 | 0.7712 | 0.8550 |
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- | 2.0117 | 5.56 | 1450 | 0.7126 | 0.8140 | 0.8187 | 0.8140 | 0.8017 | 0.8701 |
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- | 1.5617 | 5.75 | 1500 | 0.6797 | 0.8194 | 0.8436 | 0.8194 | 0.8086 | 0.8739 |
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- | 1.5617 | 5.94 | 1550 | 0.6877 | 0.8221 | 0.8279 | 0.8221 | 0.8028 | 0.8747 |
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- | 1.5617 | 6.14 | 1600 | 0.6547 | 0.8329 | 0.8525 | 0.8329 | 0.8230 | 0.8822 |
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- | 1.5617 | 6.33 | 1650 | 0.5935 | 0.8410 | 0.8589 | 0.8410 | 0.8270 | 0.8879 |
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- | 1.5617 | 6.52 | 1700 | 0.6423 | 0.8194 | 0.8255 | 0.8194 | 0.8052 | 0.8728 |
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- | 1.5617 | 6.71 | 1750 | 0.5980 | 0.8464 | 0.8610 | 0.8464 | 0.8322 | 0.8916 |
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- | 1.5617 | 6.9 | 1800 | 0.6111 | 0.8437 | 0.8543 | 0.8437 | 0.8287 | 0.8916 |
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- | 1.5617 | 7.09 | 1850 | 0.5835 | 0.8437 | 0.8588 | 0.8437 | 0.8336 | 0.8927 |
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- | 1.5617 | 7.29 | 1900 | 0.5804 | 0.8329 | 0.8461 | 0.8329 | 0.8210 | 0.8822 |
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- | 1.5617 | 7.48 | 1950 | 0.5711 | 0.8410 | 0.8580 | 0.8410 | 0.8290 | 0.8908 |
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- | 1.3255 | 7.67 | 2000 | 0.5468 | 0.8571 | 0.8633 | 0.8571 | 0.8457 | 0.9011 |
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- | 1.3255 | 7.86 | 2050 | 0.5384 | 0.8652 | 0.8720 | 0.8652 | 0.8553 | 0.9049 |
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- | 1.3255 | 8.05 | 2100 | 0.5673 | 0.8625 | 0.8684 | 0.8625 | 0.8547 | 0.9030 |
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- | 1.3255 | 8.25 | 2150 | 0.5450 | 0.8491 | 0.8582 | 0.8491 | 0.8403 | 0.8935 |
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- | 1.3255 | 8.44 | 2200 | 0.5278 | 0.8706 | 0.8770 | 0.8706 | 0.8630 | 0.9086 |
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- | 1.3255 | 8.63 | 2250 | 0.5339 | 0.8652 | 0.8692 | 0.8652 | 0.8542 | 0.9049 |
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- | 1.3255 | 8.82 | 2300 | 0.5469 | 0.8598 | 0.8648 | 0.8598 | 0.8489 | 0.9011 |
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- | 1.3255 | 9.01 | 2350 | 0.5404 | 0.8706 | 0.8747 | 0.8706 | 0.8602 | 0.9086 |
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- | 1.3255 | 9.2 | 2400 | 0.5455 | 0.8491 | 0.8565 | 0.8491 | 0.8378 | 0.8935 |
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- | 1.3255 | 9.4 | 2450 | 0.5317 | 0.8598 | 0.8664 | 0.8598 | 0.8479 | 0.9011 |
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- | 1.1934 | 9.59 | 2500 | 0.5227 | 0.8760 | 0.8798 | 0.8760 | 0.8657 | 0.9124 |
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- | 1.1934 | 9.78 | 2550 | 0.5278 | 0.8598 | 0.8653 | 0.8598 | 0.8481 | 0.9011 |
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- | 1.1934 | 9.97 | 2600 | 0.5264 | 0.8625 | 0.8662 | 0.8625 | 0.8517 | 0.9030 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.5366
24
+ - Accuracy: 0.8706
25
+ - Precision: 0.8951
26
+ - Recall: 0.8706
27
+ - F1: 0.8682
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+ - Binary: 0.9078
29
 
30
  ## Model description
31
 
 
52
  - total_train_batch_size: 128
53
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
54
  - lr_scheduler_type: linear
55
+ - num_epochs: 100
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  - mixed_precision_training: Native AMP
57
 
58
  ### Training results
59
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
61
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.19 | 50 | 3.9567 | 0.0566 | 0.0103 | 0.0566 | 0.0141 | 0.3307 |
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+ | No log | 0.38 | 100 | 3.4847 | 0.0458 | 0.0044 | 0.0458 | 0.0076 | 0.3286 |
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+ | No log | 0.58 | 150 | 3.2446 | 0.0728 | 0.0102 | 0.0728 | 0.0173 | 0.3493 |
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+ | No log | 0.77 | 200 | 3.1428 | 0.1078 | 0.0310 | 0.1078 | 0.0409 | 0.3682 |
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+ | No log | 0.96 | 250 | 2.9478 | 0.1590 | 0.0774 | 0.1590 | 0.0809 | 0.4073 |
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+ | No log | 1.15 | 300 | 2.7731 | 0.2022 | 0.1264 | 0.2022 | 0.1206 | 0.4418 |
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+ | No log | 1.34 | 350 | 2.5200 | 0.2615 | 0.1732 | 0.2615 | 0.1606 | 0.4801 |
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+ | No log | 1.53 | 400 | 2.3855 | 0.3261 | 0.2286 | 0.3261 | 0.2292 | 0.5221 |
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+ | No log | 1.73 | 450 | 2.1667 | 0.3504 | 0.2931 | 0.3504 | 0.2719 | 0.5423 |
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+ | 3.2507 | 1.92 | 500 | 2.0399 | 0.4340 | 0.3626 | 0.4340 | 0.3608 | 0.6032 |
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+ | 3.2507 | 2.11 | 550 | 1.8119 | 0.4825 | 0.4560 | 0.4825 | 0.4230 | 0.6380 |
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+ | 3.2507 | 2.3 | 600 | 1.6704 | 0.5175 | 0.4294 | 0.5175 | 0.4487 | 0.6606 |
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+ | 3.2507 | 2.49 | 650 | 1.5691 | 0.5472 | 0.5141 | 0.5472 | 0.4955 | 0.6809 |
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+ | 3.2507 | 2.68 | 700 | 1.5136 | 0.6065 | 0.6009 | 0.6065 | 0.5648 | 0.7221 |
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+ | 3.2507 | 2.88 | 750 | 1.3633 | 0.6334 | 0.5725 | 0.6334 | 0.5806 | 0.7426 |
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+ | 3.2507 | 3.07 | 800 | 1.3163 | 0.6388 | 0.6464 | 0.6388 | 0.6012 | 0.7466 |
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+ | 3.2507 | 3.26 | 850 | 1.1000 | 0.7143 | 0.7039 | 0.7143 | 0.6741 | 0.7995 |
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+ | 3.2507 | 3.45 | 900 | 1.0805 | 0.7062 | 0.7037 | 0.7062 | 0.6691 | 0.7935 |
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+ | 3.2507 | 3.64 | 950 | 1.0359 | 0.7385 | 0.7487 | 0.7385 | 0.7102 | 0.8164 |
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+ | 2.027 | 3.84 | 1000 | 0.9199 | 0.7790 | 0.7804 | 0.7790 | 0.7508 | 0.8445 |
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+ | 2.027 | 4.03 | 1050 | 0.9748 | 0.7224 | 0.7335 | 0.7224 | 0.6918 | 0.8040 |
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+ | 2.027 | 4.22 | 1100 | 0.8482 | 0.7682 | 0.7788 | 0.7682 | 0.7518 | 0.8361 |
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+ | 2.027 | 4.41 | 1150 | 0.8507 | 0.7574 | 0.7642 | 0.7574 | 0.7358 | 0.8294 |
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+ | 2.027 | 4.6 | 1200 | 0.8277 | 0.7682 | 0.7852 | 0.7682 | 0.7507 | 0.8380 |
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+ | 2.027 | 4.79 | 1250 | 0.7315 | 0.7709 | 0.7923 | 0.7709 | 0.7542 | 0.8418 |
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+ | 2.027 | 4.99 | 1300 | 0.7434 | 0.7978 | 0.8366 | 0.7978 | 0.7873 | 0.8596 |
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+ | 2.027 | 5.18 | 1350 | 0.7260 | 0.8059 | 0.8242 | 0.8059 | 0.7930 | 0.8625 |
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+ | 2.027 | 5.37 | 1400 | 0.7265 | 0.7898 | 0.8087 | 0.7898 | 0.7725 | 0.8531 |
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+ | 2.027 | 5.56 | 1450 | 0.6691 | 0.8059 | 0.8280 | 0.8059 | 0.7968 | 0.8636 |
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+ | 1.4992 | 5.75 | 1500 | 0.6508 | 0.8167 | 0.8331 | 0.8167 | 0.8077 | 0.8709 |
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+ | 1.4992 | 5.94 | 1550 | 0.6404 | 0.8167 | 0.8325 | 0.8167 | 0.8084 | 0.8712 |
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+ | 1.4992 | 6.14 | 1600 | 0.6606 | 0.8140 | 0.8385 | 0.8140 | 0.8055 | 0.8682 |
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+ | 1.4992 | 6.33 | 1650 | 0.5687 | 0.8356 | 0.8416 | 0.8356 | 0.8222 | 0.8833 |
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+ | 1.4992 | 6.52 | 1700 | 0.5381 | 0.8410 | 0.8610 | 0.8410 | 0.8322 | 0.8889 |
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+ | 1.4992 | 6.71 | 1750 | 0.6056 | 0.8356 | 0.8628 | 0.8356 | 0.8289 | 0.8852 |
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+ | 1.4992 | 6.9 | 1800 | 0.5403 | 0.8491 | 0.8629 | 0.8491 | 0.8416 | 0.8954 |
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+ | 1.4992 | 7.09 | 1850 | 0.4901 | 0.8625 | 0.8773 | 0.8625 | 0.8576 | 0.9030 |
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+ | 1.4992 | 7.29 | 1900 | 0.5177 | 0.8544 | 0.8765 | 0.8544 | 0.8506 | 0.8965 |
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+ | 1.4992 | 7.48 | 1950 | 0.5830 | 0.8329 | 0.8658 | 0.8329 | 0.8245 | 0.8822 |
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+ | 1.2457 | 7.67 | 2000 | 0.4986 | 0.8491 | 0.8715 | 0.8491 | 0.8440 | 0.8946 |
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+ | 1.2457 | 7.86 | 2050 | 0.6022 | 0.8113 | 0.8488 | 0.8113 | 0.8089 | 0.8693 |
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+ | 1.2457 | 8.05 | 2100 | 0.5857 | 0.8383 | 0.8613 | 0.8383 | 0.8348 | 0.8871 |
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+ | 1.2457 | 8.25 | 2150 | 0.5669 | 0.8194 | 0.8485 | 0.8194 | 0.8194 | 0.8752 |
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+ | 1.2457 | 8.44 | 2200 | 0.5661 | 0.8571 | 0.8764 | 0.8571 | 0.8555 | 0.9003 |
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+ | 1.2457 | 8.63 | 2250 | 0.5170 | 0.8598 | 0.8919 | 0.8598 | 0.8547 | 0.9022 |
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+ | 1.2457 | 8.82 | 2300 | 0.5744 | 0.8248 | 0.8493 | 0.8248 | 0.8208 | 0.8757 |
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+ | 1.2457 | 9.01 | 2350 | 0.5577 | 0.8410 | 0.8650 | 0.8410 | 0.8344 | 0.8871 |
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+ | 1.2457 | 9.2 | 2400 | 0.5493 | 0.8275 | 0.8429 | 0.8275 | 0.8228 | 0.8784 |
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+ | 1.2457 | 9.4 | 2450 | 0.4822 | 0.8679 | 0.8913 | 0.8679 | 0.8654 | 0.9078 |
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+ | 1.0978 | 9.59 | 2500 | 0.4880 | 0.8464 | 0.8627 | 0.8464 | 0.8405 | 0.8938 |
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+ | 1.0978 | 9.78 | 2550 | 0.5233 | 0.8625 | 0.8771 | 0.8625 | 0.8520 | 0.9038 |
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+ | 1.0978 | 9.97 | 2600 | 0.4864 | 0.8733 | 0.8903 | 0.8733 | 0.8693 | 0.9108 |
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+ | 1.0978 | 10.16 | 2650 | 0.5167 | 0.8706 | 0.8932 | 0.8706 | 0.8649 | 0.9086 |
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+ | 1.0978 | 10.35 | 2700 | 0.4831 | 0.8706 | 0.8872 | 0.8706 | 0.8676 | 0.9086 |
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+ | 1.0978 | 10.55 | 2750 | 0.4824 | 0.8760 | 0.8982 | 0.8760 | 0.8741 | 0.9132 |
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+ | 1.0978 | 10.74 | 2800 | 0.5156 | 0.8598 | 0.8850 | 0.8598 | 0.8561 | 0.9011 |
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+ | 1.0978 | 10.93 | 2850 | 0.5065 | 0.8895 | 0.9124 | 0.8895 | 0.8873 | 0.9210 |
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+ | 1.0978 | 11.12 | 2900 | 0.4637 | 0.8787 | 0.8990 | 0.8787 | 0.8772 | 0.9143 |
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+ | 1.0978 | 11.31 | 2950 | 0.4574 | 0.8922 | 0.9056 | 0.8922 | 0.8908 | 0.9232 |
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+ | 0.9986 | 11.51 | 3000 | 0.5472 | 0.8760 | 0.9029 | 0.8760 | 0.8755 | 0.9124 |
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+ | 0.9986 | 11.7 | 3050 | 0.5353 | 0.8679 | 0.8911 | 0.8679 | 0.8642 | 0.9108 |
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+ | 0.9986 | 11.89 | 3100 | 0.4301 | 0.8679 | 0.8818 | 0.8679 | 0.8617 | 0.9067 |
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+ | 0.9986 | 12.08 | 3150 | 0.5122 | 0.8544 | 0.8746 | 0.8544 | 0.8520 | 0.8957 |
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+ | 0.9986 | 12.27 | 3200 | 0.4837 | 0.8922 | 0.9080 | 0.8922 | 0.8888 | 0.9229 |
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+ | 0.9986 | 12.46 | 3250 | 0.5032 | 0.8706 | 0.8908 | 0.8706 | 0.8669 | 0.9078 |
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+ | 0.9986 | 12.66 | 3300 | 0.5752 | 0.8544 | 0.8710 | 0.8544 | 0.8479 | 0.8957 |
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+ | 0.9986 | 12.85 | 3350 | 0.6008 | 0.8491 | 0.8737 | 0.8491 | 0.8428 | 0.8935 |
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+ | 0.9986 | 13.04 | 3400 | 0.4820 | 0.8733 | 0.8960 | 0.8733 | 0.8701 | 0.9127 |
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+ | 0.9986 | 13.23 | 3450 | 0.5366 | 0.8706 | 0.8951 | 0.8706 | 0.8682 | 0.9078 |
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  ### Framework versions
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