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wav2vec2-classifier-aug

Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
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  1. README.md +86 -60
  2. runs/Jul19_15-49-40_LAPTOP-1GID9RGH/events.out.tfevents.1721378981.LAPTOP-1GID9RGH.3860.0 +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/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0269
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- - Accuracy: 0.7790
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- - Precision: 0.7971
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- - Recall: 0.7790
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- - F1: 0.7657
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- - Binary: 0.8453
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  ## Model description
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@@ -44,7 +44,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -52,65 +52,91 @@ 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 | 4.2076 | 0.0512 | 0.0054 | 0.0512 | 0.0092 | 0.3245 |
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- | No log | 0.38 | 100 | 3.9082 | 0.0566 | 0.0053 | 0.0566 | 0.0092 | 0.3364 |
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- | No log | 0.58 | 150 | 3.7121 | 0.0809 | 0.0281 | 0.0809 | 0.0302 | 0.3553 |
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- | No log | 0.77 | 200 | 3.5137 | 0.1671 | 0.0794 | 0.1671 | 0.0870 | 0.4164 |
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- | No log | 0.96 | 250 | 3.3647 | 0.2049 | 0.1259 | 0.2049 | 0.1261 | 0.4429 |
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- | 3.9294 | 1.15 | 300 | 3.2045 | 0.2534 | 0.1676 | 0.2534 | 0.1635 | 0.4765 |
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- | 3.9294 | 1.34 | 350 | 3.0682 | 0.2695 | 0.1968 | 0.2695 | 0.1841 | 0.4879 |
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- | 3.9294 | 1.53 | 400 | 2.9211 | 0.3154 | 0.2301 | 0.3154 | 0.2250 | 0.5191 |
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- | 3.9294 | 1.73 | 450 | 2.7973 | 0.3720 | 0.2756 | 0.3720 | 0.2815 | 0.5615 |
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- | 3.9294 | 1.92 | 500 | 2.6856 | 0.4232 | 0.3382 | 0.4232 | 0.3413 | 0.5973 |
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- | 3.1851 | 2.11 | 550 | 2.5696 | 0.4582 | 0.4161 | 0.4582 | 0.3827 | 0.6208 |
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- | 3.1851 | 2.3 | 600 | 2.4666 | 0.4987 | 0.4339 | 0.4987 | 0.4215 | 0.6501 |
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- | 3.1851 | 2.49 | 650 | 2.3548 | 0.5202 | 0.4502 | 0.5202 | 0.4542 | 0.6633 |
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- | 3.1851 | 2.68 | 700 | 2.2498 | 0.5229 | 0.4593 | 0.5229 | 0.4574 | 0.6660 |
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- | 3.1851 | 2.88 | 750 | 2.1579 | 0.5660 | 0.5162 | 0.5660 | 0.4993 | 0.6962 |
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- | 2.7 | 3.07 | 800 | 2.0626 | 0.5903 | 0.5465 | 0.5903 | 0.5332 | 0.7143 |
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- | 2.7 | 3.26 | 850 | 1.9820 | 0.6092 | 0.5576 | 0.6092 | 0.5470 | 0.7264 |
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- | 2.7 | 3.45 | 900 | 1.9158 | 0.6011 | 0.5636 | 0.6011 | 0.5466 | 0.7208 |
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- | 2.7 | 3.64 | 950 | 1.8432 | 0.6092 | 0.5631 | 0.6092 | 0.5499 | 0.7264 |
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- | 2.7 | 3.84 | 1000 | 1.7732 | 0.6280 | 0.5956 | 0.6280 | 0.5816 | 0.7396 |
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- | 2.3404 | 4.03 | 1050 | 1.7214 | 0.6523 | 0.6167 | 0.6523 | 0.6000 | 0.7566 |
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- | 2.3404 | 4.22 | 1100 | 1.6562 | 0.6550 | 0.6312 | 0.6550 | 0.6114 | 0.7585 |
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- | 2.3404 | 4.41 | 1150 | 1.5909 | 0.6792 | 0.6402 | 0.6792 | 0.6301 | 0.7755 |
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- | 2.3404 | 4.6 | 1200 | 1.5455 | 0.6900 | 0.6806 | 0.6900 | 0.6534 | 0.7830 |
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- | 2.3404 | 4.79 | 1250 | 1.5123 | 0.6739 | 0.6415 | 0.6739 | 0.6330 | 0.7717 |
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- | 2.3404 | 4.99 | 1300 | 1.4662 | 0.7089 | 0.6954 | 0.7089 | 0.6741 | 0.7962 |
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- | 2.089 | 5.18 | 1350 | 1.4212 | 0.6981 | 0.6739 | 0.6981 | 0.6585 | 0.7887 |
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- | 2.089 | 5.37 | 1400 | 1.3848 | 0.7008 | 0.6700 | 0.7008 | 0.6572 | 0.7906 |
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- | 2.089 | 5.56 | 1450 | 1.3435 | 0.7305 | 0.7289 | 0.7305 | 0.7017 | 0.8113 |
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- | 2.089 | 5.75 | 1500 | 1.3324 | 0.7251 | 0.7331 | 0.7251 | 0.7008 | 0.8075 |
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- | 2.089 | 5.94 | 1550 | 1.3030 | 0.7116 | 0.7242 | 0.7116 | 0.6841 | 0.7981 |
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- | 1.8929 | 6.14 | 1600 | 1.2662 | 0.7358 | 0.7356 | 0.7358 | 0.7065 | 0.8151 |
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- | 1.8929 | 6.33 | 1650 | 1.2341 | 0.7332 | 0.7665 | 0.7332 | 0.7128 | 0.8132 |
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- | 1.8929 | 6.52 | 1700 | 1.2299 | 0.7224 | 0.7255 | 0.7224 | 0.6950 | 0.8057 |
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- | 1.8929 | 6.71 | 1750 | 1.1984 | 0.7574 | 0.7758 | 0.7574 | 0.7409 | 0.8302 |
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- | 1.8929 | 6.9 | 1800 | 1.1810 | 0.7547 | 0.7710 | 0.7547 | 0.7390 | 0.8283 |
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- | 1.7722 | 7.09 | 1850 | 1.1510 | 0.7763 | 0.7979 | 0.7763 | 0.7613 | 0.8434 |
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- | 1.7722 | 7.29 | 1900 | 1.1476 | 0.7466 | 0.7541 | 0.7466 | 0.7259 | 0.8226 |
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- | 1.7722 | 7.48 | 1950 | 1.1326 | 0.7601 | 0.7798 | 0.7601 | 0.7456 | 0.8321 |
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- | 1.7722 | 7.67 | 2000 | 1.1218 | 0.7655 | 0.7778 | 0.7655 | 0.7493 | 0.8358 |
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- | 1.7722 | 7.86 | 2050 | 1.0964 | 0.7736 | 0.7844 | 0.7736 | 0.7597 | 0.8415 |
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- | 1.6621 | 8.05 | 2100 | 1.0895 | 0.7682 | 0.7778 | 0.7682 | 0.7505 | 0.8377 |
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- | 1.6621 | 8.25 | 2150 | 1.0731 | 0.7682 | 0.7925 | 0.7682 | 0.7546 | 0.8377 |
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- | 1.6621 | 8.44 | 2200 | 1.0673 | 0.7628 | 0.7771 | 0.7628 | 0.7497 | 0.8340 |
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- | 1.6621 | 8.63 | 2250 | 1.0728 | 0.7682 | 0.7890 | 0.7682 | 0.7552 | 0.8377 |
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- | 1.6621 | 8.82 | 2300 | 1.0449 | 0.7898 | 0.8032 | 0.7898 | 0.7779 | 0.8528 |
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- | 1.6107 | 9.01 | 2350 | 1.0386 | 0.7871 | 0.8037 | 0.7871 | 0.7744 | 0.8509 |
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- | 1.6107 | 9.2 | 2400 | 1.0398 | 0.7763 | 0.7912 | 0.7763 | 0.7635 | 0.8434 |
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- | 1.6107 | 9.4 | 2450 | 1.0324 | 0.7844 | 0.8025 | 0.7844 | 0.7726 | 0.8491 |
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- | 1.6107 | 9.59 | 2500 | 1.0323 | 0.7790 | 0.8006 | 0.7790 | 0.7651 | 0.8453 |
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- | 1.6107 | 9.78 | 2550 | 1.0274 | 0.7790 | 0.7980 | 0.7790 | 0.7648 | 0.8453 |
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- | 1.6107 | 9.97 | 2600 | 1.0269 | 0.7790 | 0.7971 | 0.7790 | 0.7657 | 0.8453 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.5202
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+ - Accuracy: 0.8679
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+ - Precision: 0.8908
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+ - Recall: 0.8679
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+ - F1: 0.8667
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+ - Binary: 0.9067
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30
  ## Model description
31
 
 
44
  ### Training hyperparameters
45
 
46
  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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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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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 30
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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 | 4.4234 | 0.0162 | 0.0017 | 0.0162 | 0.0031 | 0.1558 |
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+ | No log | 0.38 | 100 | 4.2882 | 0.0350 | 0.0028 | 0.0350 | 0.0047 | 0.3075 |
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+ | No log | 0.58 | 150 | 3.9749 | 0.0404 | 0.0024 | 0.0404 | 0.0043 | 0.3170 |
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+ | No log | 0.77 | 200 | 3.7072 | 0.0458 | 0.0070 | 0.0458 | 0.0109 | 0.3296 |
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+ | No log | 0.96 | 250 | 3.4794 | 0.0836 | 0.0233 | 0.0836 | 0.0217 | 0.3580 |
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+ | 4.1218 | 1.15 | 300 | 3.2647 | 0.1321 | 0.0526 | 0.1321 | 0.0640 | 0.3930 |
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+ | 4.1218 | 1.34 | 350 | 3.0118 | 0.2318 | 0.1558 | 0.2318 | 0.1503 | 0.4623 |
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+ | 4.1218 | 1.53 | 400 | 2.7772 | 0.2642 | 0.1570 | 0.2642 | 0.1752 | 0.4849 |
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+ | 4.1218 | 1.73 | 450 | 2.5522 | 0.3585 | 0.3222 | 0.3585 | 0.2848 | 0.5520 |
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+ | 4.1218 | 1.92 | 500 | 2.3428 | 0.3342 | 0.2725 | 0.3342 | 0.2563 | 0.5372 |
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+ | 3.1065 | 2.11 | 550 | 2.0580 | 0.4124 | 0.3332 | 0.4124 | 0.3326 | 0.5887 |
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+ | 3.1065 | 2.3 | 600 | 1.8454 | 0.4771 | 0.4322 | 0.4771 | 0.4131 | 0.6323 |
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+ | 3.1065 | 2.49 | 650 | 1.6830 | 0.5310 | 0.4926 | 0.5310 | 0.4771 | 0.6733 |
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+ | 3.1065 | 2.68 | 700 | 1.5545 | 0.5580 | 0.5326 | 0.5580 | 0.5096 | 0.6898 |
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+ | 3.1065 | 2.88 | 750 | 1.3593 | 0.6253 | 0.5975 | 0.6253 | 0.5812 | 0.7388 |
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+ | 2.2273 | 3.07 | 800 | 1.2047 | 0.6927 | 0.6715 | 0.6927 | 0.6535 | 0.7849 |
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+ | 2.2273 | 3.26 | 850 | 1.1223 | 0.6765 | 0.6662 | 0.6765 | 0.6461 | 0.7728 |
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+ | 2.2273 | 3.45 | 900 | 1.0296 | 0.7062 | 0.7121 | 0.7062 | 0.6756 | 0.7943 |
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+ | 2.2273 | 3.64 | 950 | 1.0001 | 0.7251 | 0.7388 | 0.7251 | 0.7074 | 0.8081 |
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+ | 2.2273 | 3.84 | 1000 | 0.9879 | 0.7466 | 0.7650 | 0.7466 | 0.7265 | 0.8229 |
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+ | 1.734 | 4.03 | 1050 | 0.9078 | 0.7466 | 0.7590 | 0.7466 | 0.7323 | 0.8237 |
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+ | 1.734 | 4.22 | 1100 | 0.8344 | 0.7898 | 0.8284 | 0.7898 | 0.7794 | 0.8550 |
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+ | 1.734 | 4.41 | 1150 | 0.8199 | 0.7925 | 0.8029 | 0.7925 | 0.7749 | 0.8558 |
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+ | 1.734 | 4.6 | 1200 | 0.7227 | 0.7951 | 0.8309 | 0.7951 | 0.7892 | 0.8566 |
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+ | 1.734 | 4.79 | 1250 | 0.7666 | 0.7871 | 0.8246 | 0.7871 | 0.7768 | 0.8520 |
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+ | 1.734 | 4.99 | 1300 | 0.7529 | 0.7871 | 0.7989 | 0.7871 | 0.7768 | 0.8531 |
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+ | 1.4492 | 5.18 | 1350 | 0.7035 | 0.8032 | 0.8287 | 0.8032 | 0.7986 | 0.8633 |
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+ | 1.4492 | 5.37 | 1400 | 0.6597 | 0.8194 | 0.8522 | 0.8194 | 0.8141 | 0.8739 |
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+ | 1.4492 | 5.56 | 1450 | 0.6592 | 0.8113 | 0.8472 | 0.8113 | 0.8108 | 0.8690 |
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+ | 1.4492 | 5.75 | 1500 | 0.6535 | 0.8248 | 0.8547 | 0.8248 | 0.8203 | 0.8784 |
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+ | 1.4492 | 5.94 | 1550 | 0.6343 | 0.8167 | 0.8568 | 0.8167 | 0.8116 | 0.8701 |
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+ | 1.2533 | 6.14 | 1600 | 0.5640 | 0.8356 | 0.8589 | 0.8356 | 0.8329 | 0.8860 |
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+ | 1.2533 | 6.33 | 1650 | 0.5465 | 0.8383 | 0.8669 | 0.8383 | 0.8341 | 0.8889 |
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+ | 1.2533 | 6.52 | 1700 | 0.5594 | 0.8248 | 0.8549 | 0.8248 | 0.8204 | 0.8776 |
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+ | 1.2533 | 6.71 | 1750 | 0.5765 | 0.8464 | 0.8776 | 0.8464 | 0.8463 | 0.8935 |
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+ | 1.2533 | 6.9 | 1800 | 0.5169 | 0.8571 | 0.8758 | 0.8571 | 0.8543 | 0.9000 |
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+ | 1.138 | 7.09 | 1850 | 0.5206 | 0.8410 | 0.8676 | 0.8410 | 0.8421 | 0.8887 |
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+ | 1.138 | 7.29 | 1900 | 0.5258 | 0.8544 | 0.8779 | 0.8544 | 0.8537 | 0.8992 |
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+ | 1.138 | 7.48 | 1950 | 0.5855 | 0.8383 | 0.8693 | 0.8383 | 0.8384 | 0.8879 |
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+ | 1.138 | 7.67 | 2000 | 0.5209 | 0.8491 | 0.8800 | 0.8491 | 0.8493 | 0.8943 |
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+ | 1.138 | 7.86 | 2050 | 0.5150 | 0.8410 | 0.8710 | 0.8410 | 0.8411 | 0.8889 |
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+ | 1.0249 | 8.05 | 2100 | 0.4937 | 0.8571 | 0.8840 | 0.8571 | 0.8568 | 0.9022 |
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+ | 1.0249 | 8.25 | 2150 | 0.5344 | 0.8518 | 0.8790 | 0.8518 | 0.8492 | 0.8995 |
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+ | 1.0249 | 8.44 | 2200 | 0.5322 | 0.8437 | 0.8751 | 0.8437 | 0.8428 | 0.8927 |
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+ | 1.0249 | 8.63 | 2250 | 0.5533 | 0.8248 | 0.8561 | 0.8248 | 0.8233 | 0.8774 |
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+ | 1.0249 | 8.82 | 2300 | 0.5242 | 0.8491 | 0.8797 | 0.8491 | 0.8469 | 0.8943 |
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+ | 0.9523 | 9.01 | 2350 | 0.4938 | 0.8679 | 0.8911 | 0.8679 | 0.8669 | 0.9075 |
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+ | 0.9523 | 9.2 | 2400 | 0.5037 | 0.8625 | 0.8888 | 0.8625 | 0.8627 | 0.9038 |
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+ | 0.9523 | 9.4 | 2450 | 0.4973 | 0.8571 | 0.8794 | 0.8571 | 0.8565 | 0.9000 |
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+ | 0.9523 | 9.59 | 2500 | 0.5343 | 0.8383 | 0.8705 | 0.8383 | 0.8384 | 0.8868 |
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+ | 0.9523 | 9.78 | 2550 | 0.5493 | 0.8491 | 0.8746 | 0.8491 | 0.8472 | 0.8943 |
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+ | 0.9523 | 9.97 | 2600 | 0.5226 | 0.8544 | 0.8783 | 0.8544 | 0.8537 | 0.8981 |
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+ | 0.8792 | 10.16 | 2650 | 0.4883 | 0.8625 | 0.8857 | 0.8625 | 0.8598 | 0.9038 |
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+ | 0.8792 | 10.35 | 2700 | 0.5178 | 0.8518 | 0.8784 | 0.8518 | 0.8503 | 0.8962 |
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+ | 0.8792 | 10.55 | 2750 | 0.6273 | 0.8383 | 0.8756 | 0.8383 | 0.8363 | 0.8879 |
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+ | 0.8792 | 10.74 | 2800 | 0.5229 | 0.8571 | 0.8855 | 0.8571 | 0.8576 | 0.9000 |
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+ | 0.8792 | 10.93 | 2850 | 0.4617 | 0.8706 | 0.8924 | 0.8706 | 0.8686 | 0.9094 |
120
+ | 0.8251 | 11.12 | 2900 | 0.5764 | 0.8625 | 0.8874 | 0.8625 | 0.8626 | 0.9038 |
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+ | 0.8251 | 11.31 | 2950 | 0.5111 | 0.8706 | 0.8960 | 0.8706 | 0.8689 | 0.9094 |
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+ | 0.8251 | 11.51 | 3000 | 0.6013 | 0.8437 | 0.8603 | 0.8437 | 0.8410 | 0.8906 |
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+ | 0.8251 | 11.7 | 3050 | 0.5968 | 0.8437 | 0.8682 | 0.8437 | 0.8405 | 0.8916 |
124
+ | 0.8251 | 11.89 | 3100 | 0.5467 | 0.8544 | 0.8806 | 0.8544 | 0.8542 | 0.8981 |
125
+ | 0.7578 | 12.08 | 3150 | 0.6015 | 0.8544 | 0.8774 | 0.8544 | 0.8523 | 0.8981 |
126
+ | 0.7578 | 12.27 | 3200 | 0.4897 | 0.8679 | 0.8814 | 0.8679 | 0.8632 | 0.9067 |
127
+ | 0.7578 | 12.46 | 3250 | 0.5395 | 0.8491 | 0.8765 | 0.8491 | 0.8460 | 0.8935 |
128
+ | 0.7578 | 12.66 | 3300 | 0.5873 | 0.8491 | 0.8767 | 0.8491 | 0.8489 | 0.8935 |
129
+ | 0.7578 | 12.85 | 3350 | 0.5386 | 0.8491 | 0.8735 | 0.8491 | 0.8498 | 0.8935 |
130
+ | 0.7295 | 13.04 | 3400 | 0.5826 | 0.8652 | 0.8949 | 0.8652 | 0.8663 | 0.9057 |
131
+ | 0.7295 | 13.23 | 3450 | 0.5358 | 0.8571 | 0.8859 | 0.8571 | 0.8562 | 0.9003 |
132
+ | 0.7295 | 13.42 | 3500 | 0.4802 | 0.8841 | 0.9017 | 0.8841 | 0.8838 | 0.9173 |
133
+ | 0.7295 | 13.61 | 3550 | 0.5709 | 0.8410 | 0.8692 | 0.8410 | 0.8404 | 0.8879 |
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+ | 0.7295 | 13.81 | 3600 | 0.5420 | 0.8544 | 0.8738 | 0.8544 | 0.8535 | 0.8992 |
135
+ | 0.7295 | 14.0 | 3650 | 0.5384 | 0.8652 | 0.8817 | 0.8652 | 0.8635 | 0.9049 |
136
+ | 0.6874 | 14.19 | 3700 | 0.4911 | 0.8598 | 0.8753 | 0.8598 | 0.8593 | 0.9019 |
137
+ | 0.6874 | 14.38 | 3750 | 0.5172 | 0.8598 | 0.8826 | 0.8598 | 0.8588 | 0.9011 |
138
+ | 0.6874 | 14.57 | 3800 | 0.5024 | 0.8598 | 0.8814 | 0.8598 | 0.8592 | 0.9019 |
139
+ | 0.6874 | 14.77 | 3850 | 0.5202 | 0.8679 | 0.8908 | 0.8679 | 0.8667 | 0.9067 |
140
 
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  ### Framework versions
runs/Jul19_15-49-40_LAPTOP-1GID9RGH/events.out.tfevents.1721378981.LAPTOP-1GID9RGH.3860.0 CHANGED
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