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hubert-classifier-aug-fold-3

Audio Classification
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hubert
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  1. README.md +83 -53
  2. runs/Jul20_05-03-03_LAPTOP-1GID9RGH/events.out.tfevents.1721426585.LAPTOP-1GID9RGH.19084.6 +2 -2
  3. runs/Jul20_05-03-03_LAPTOP-1GID9RGH/events.out.tfevents.1721429603.LAPTOP-1GID9RGH.19084.7 +3 -0
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: 0.6883
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- - Accuracy: 0.8585
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- - Precision: 0.8724
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- - Recall: 0.8585
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- - F1: 0.8561
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- - Binary: 0.9018
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  ## Model description
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@@ -59,53 +59,83 @@ The following hyperparameters were used during training:
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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 | 0.13 | 50 | 3.8036 | 0.0621 | 0.0384 | 0.0621 | 0.0218 | 0.3371 |
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- | No log | 0.27 | 100 | 3.3343 | 0.0931 | 0.0379 | 0.0931 | 0.0294 | 0.3615 |
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- | No log | 0.4 | 150 | 3.0247 | 0.2132 | 0.1301 | 0.2132 | 0.1310 | 0.4425 |
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- | No log | 0.54 | 200 | 2.6612 | 0.3009 | 0.2088 | 0.3009 | 0.2123 | 0.5108 |
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- | No log | 0.67 | 250 | 2.2571 | 0.4359 | 0.3738 | 0.4359 | 0.3578 | 0.6065 |
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- | No log | 0.81 | 300 | 1.9161 | 0.5250 | 0.4966 | 0.5250 | 0.4671 | 0.6684 |
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- | No log | 0.94 | 350 | 1.6226 | 0.5938 | 0.5933 | 0.5938 | 0.5397 | 0.7143 |
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- | 2.9621 | 1.08 | 400 | 1.4796 | 0.6370 | 0.6154 | 0.6370 | 0.5972 | 0.7470 |
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- | 2.9621 | 1.21 | 450 | 1.2098 | 0.7126 | 0.7208 | 0.7126 | 0.6872 | 0.7992 |
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- | 2.9621 | 1.35 | 500 | 1.0618 | 0.7193 | 0.7228 | 0.7193 | 0.6875 | 0.8024 |
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- | 2.9621 | 1.48 | 550 | 0.9730 | 0.7652 | 0.7719 | 0.7652 | 0.7471 | 0.8360 |
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- | 2.9621 | 1.62 | 600 | 0.9097 | 0.7679 | 0.7847 | 0.7679 | 0.7565 | 0.8394 |
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- | 2.9621 | 1.75 | 650 | 0.9550 | 0.7517 | 0.7668 | 0.7517 | 0.7445 | 0.8262 |
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- | 2.9621 | 1.89 | 700 | 0.8710 | 0.7530 | 0.7540 | 0.7530 | 0.7421 | 0.8281 |
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- | 1.3311 | 2.02 | 750 | 0.8722 | 0.7800 | 0.7998 | 0.7800 | 0.7707 | 0.8475 |
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- | 1.3311 | 2.16 | 800 | 0.7156 | 0.8057 | 0.8186 | 0.8057 | 0.8021 | 0.8640 |
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- | 1.3311 | 2.29 | 850 | 0.6967 | 0.8165 | 0.8317 | 0.8165 | 0.8133 | 0.8718 |
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- | 1.3311 | 2.43 | 900 | 0.7749 | 0.7881 | 0.8028 | 0.7881 | 0.7818 | 0.8526 |
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- | 1.3311 | 2.56 | 950 | 0.7012 | 0.8097 | 0.8255 | 0.8097 | 0.8038 | 0.8672 |
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- | 1.3311 | 2.7 | 1000 | 0.7839 | 0.7895 | 0.8093 | 0.7895 | 0.7839 | 0.8530 |
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- | 1.3311 | 2.83 | 1050 | 0.6796 | 0.8205 | 0.8405 | 0.8205 | 0.8162 | 0.8761 |
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- | 1.3311 | 2.96 | 1100 | 0.7223 | 0.7976 | 0.8156 | 0.7976 | 0.7952 | 0.8601 |
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- | 0.846 | 3.1 | 1150 | 0.7374 | 0.8178 | 0.8376 | 0.8178 | 0.8176 | 0.8738 |
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- | 0.846 | 3.23 | 1200 | 0.7266 | 0.8165 | 0.8393 | 0.8165 | 0.8145 | 0.8704 |
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- | 0.846 | 3.37 | 1250 | 0.6945 | 0.8246 | 0.8429 | 0.8246 | 0.8203 | 0.8785 |
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- | 0.846 | 3.5 | 1300 | 0.6475 | 0.8246 | 0.8455 | 0.8246 | 0.8216 | 0.8791 |
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- | 0.846 | 3.64 | 1350 | 0.8423 | 0.8057 | 0.8168 | 0.8057 | 0.7971 | 0.8629 |
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- | 0.846 | 3.77 | 1400 | 0.6308 | 0.8394 | 0.8547 | 0.8394 | 0.8384 | 0.8879 |
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- | 0.846 | 3.91 | 1450 | 0.6436 | 0.8408 | 0.8595 | 0.8408 | 0.8369 | 0.8899 |
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- | 0.624 | 4.04 | 1500 | 0.7371 | 0.8340 | 0.8547 | 0.8340 | 0.8318 | 0.8846 |
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- | 0.624 | 4.18 | 1550 | 0.6885 | 0.8300 | 0.8477 | 0.8300 | 0.8230 | 0.8814 |
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- | 0.624 | 4.31 | 1600 | 0.6714 | 0.8394 | 0.8542 | 0.8394 | 0.8361 | 0.8899 |
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- | 0.624 | 4.45 | 1650 | 0.6960 | 0.8246 | 0.8415 | 0.8246 | 0.8191 | 0.8800 |
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- | 0.624 | 4.58 | 1700 | 0.6460 | 0.8543 | 0.8694 | 0.8543 | 0.8520 | 0.8982 |
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- | 0.624 | 4.72 | 1750 | 0.6631 | 0.8421 | 0.8605 | 0.8421 | 0.8382 | 0.8901 |
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- | 0.624 | 4.85 | 1800 | 0.6207 | 0.8637 | 0.8709 | 0.8637 | 0.8609 | 0.9053 |
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- | 0.624 | 4.99 | 1850 | 0.6698 | 0.8516 | 0.8681 | 0.8516 | 0.8485 | 0.8974 |
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- | 0.5094 | 5.12 | 1900 | 0.6372 | 0.8502 | 0.8690 | 0.8502 | 0.8476 | 0.8960 |
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- | 0.5094 | 5.26 | 1950 | 0.6589 | 0.8556 | 0.8711 | 0.8556 | 0.8545 | 0.9003 |
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- | 0.5094 | 5.39 | 2000 | 0.6128 | 0.8650 | 0.8732 | 0.8650 | 0.8623 | 0.9062 |
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- | 0.5094 | 5.53 | 2050 | 0.7396 | 0.8381 | 0.8557 | 0.8381 | 0.8358 | 0.8880 |
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- | 0.5094 | 5.66 | 2100 | 0.7020 | 0.8448 | 0.8621 | 0.8448 | 0.8424 | 0.8935 |
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- | 0.5094 | 5.8 | 2150 | 0.7282 | 0.8421 | 0.8622 | 0.8421 | 0.8414 | 0.8901 |
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- | 0.5094 | 5.93 | 2200 | 0.6691 | 0.8475 | 0.8620 | 0.8475 | 0.8462 | 0.8955 |
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- | 0.4345 | 6.06 | 2250 | 0.6890 | 0.8543 | 0.8673 | 0.8543 | 0.8523 | 0.8977 |
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- | 0.4345 | 6.2 | 2300 | 0.6614 | 0.8462 | 0.8607 | 0.8462 | 0.8446 | 0.8926 |
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- | 0.4345 | 6.33 | 2350 | 0.7521 | 0.8462 | 0.8635 | 0.8462 | 0.8425 | 0.8926 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:
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+ - Loss: 0.6500
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+ - Accuracy: 0.8571
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+ - Precision: 0.8732
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+ - Recall: 0.8571
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+ - F1: 0.8566
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+ - Binary: 0.9011
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  ## Model description
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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 | 0.13 | 50 | 3.8835 | 0.0472 | 0.0200 | 0.0472 | 0.0107 | 0.3140 |
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+ | No log | 0.27 | 100 | 3.4346 | 0.0661 | 0.0210 | 0.0661 | 0.0193 | 0.3418 |
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+ | No log | 0.4 | 150 | 3.0499 | 0.2227 | 0.1148 | 0.2227 | 0.1285 | 0.4540 |
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+ | No log | 0.54 | 200 | 2.6619 | 0.3077 | 0.2105 | 0.3077 | 0.2226 | 0.5130 |
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+ | No log | 0.67 | 250 | 2.2918 | 0.3617 | 0.2675 | 0.3617 | 0.2712 | 0.5529 |
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+ | No log | 0.81 | 300 | 2.0249 | 0.4116 | 0.3239 | 0.4116 | 0.3323 | 0.5879 |
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+ | No log | 0.94 | 350 | 1.8232 | 0.5020 | 0.4680 | 0.5020 | 0.4455 | 0.6507 |
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+ | 3.0314 | 1.08 | 400 | 1.6659 | 0.5533 | 0.5307 | 0.5533 | 0.5035 | 0.6885 |
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+ | 3.0314 | 1.21 | 450 | 1.5066 | 0.5803 | 0.5666 | 0.5803 | 0.5382 | 0.7067 |
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+ | 3.0314 | 1.35 | 500 | 1.3766 | 0.6397 | 0.6122 | 0.6397 | 0.5917 | 0.7482 |
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+ | 3.0314 | 1.48 | 550 | 1.3364 | 0.6599 | 0.6541 | 0.6599 | 0.6313 | 0.7630 |
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+ | 3.0314 | 1.62 | 600 | 1.1819 | 0.7031 | 0.6905 | 0.7031 | 0.6686 | 0.7918 |
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+ | 3.0314 | 1.75 | 650 | 1.0738 | 0.7072 | 0.7068 | 0.7072 | 0.6857 | 0.7966 |
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+ | 3.0314 | 1.89 | 700 | 1.0101 | 0.7436 | 0.7536 | 0.7436 | 0.7270 | 0.8212 |
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+ | 1.5683 | 2.02 | 750 | 0.9439 | 0.7679 | 0.7939 | 0.7679 | 0.7532 | 0.8377 |
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+ | 1.5683 | 2.16 | 800 | 0.9175 | 0.7652 | 0.7904 | 0.7652 | 0.7558 | 0.8372 |
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+ | 1.5683 | 2.29 | 850 | 0.8568 | 0.7679 | 0.7864 | 0.7679 | 0.7529 | 0.8381 |
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+ | 1.5683 | 2.43 | 900 | 0.8420 | 0.7760 | 0.8043 | 0.7760 | 0.7681 | 0.8439 |
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+ | 1.5683 | 2.56 | 950 | 0.7376 | 0.7976 | 0.8186 | 0.7976 | 0.7924 | 0.8579 |
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+ | 1.5683 | 2.7 | 1000 | 0.8290 | 0.7854 | 0.8188 | 0.7854 | 0.7745 | 0.8495 |
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+ | 1.5683 | 2.83 | 1050 | 0.7332 | 0.8219 | 0.8326 | 0.8219 | 0.8138 | 0.8765 |
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+ | 1.5683 | 2.96 | 1100 | 0.7811 | 0.7962 | 0.8091 | 0.7962 | 0.7887 | 0.8584 |
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+ | 1.0193 | 3.1 | 1150 | 0.6732 | 0.8138 | 0.8392 | 0.8138 | 0.8054 | 0.8692 |
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+ | 1.0193 | 3.23 | 1200 | 0.6632 | 0.8408 | 0.8576 | 0.8408 | 0.8354 | 0.8887 |
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+ | 1.0193 | 3.37 | 1250 | 0.7166 | 0.8300 | 0.8589 | 0.8300 | 0.8280 | 0.8815 |
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+ | 1.0193 | 3.5 | 1300 | 0.6498 | 0.8408 | 0.8600 | 0.8408 | 0.8371 | 0.8883 |
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+ | 1.0193 | 3.64 | 1350 | 0.5999 | 0.8502 | 0.8627 | 0.8502 | 0.8473 | 0.8957 |
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+ | 1.0193 | 3.77 | 1400 | 0.6742 | 0.8259 | 0.8449 | 0.8259 | 0.8223 | 0.8779 |
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+ | 1.0193 | 3.91 | 1450 | 0.6017 | 0.8435 | 0.8550 | 0.8435 | 0.8387 | 0.8904 |
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+ | 0.7413 | 4.04 | 1500 | 0.6352 | 0.8354 | 0.8533 | 0.8354 | 0.8328 | 0.8848 |
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+ | 0.7413 | 4.18 | 1550 | 0.6304 | 0.8556 | 0.8698 | 0.8556 | 0.8534 | 0.8981 |
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+ | 0.7413 | 4.31 | 1600 | 0.6754 | 0.8300 | 0.8441 | 0.8300 | 0.8281 | 0.8815 |
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+ | 0.7413 | 4.45 | 1650 | 0.7309 | 0.8138 | 0.8289 | 0.8138 | 0.8113 | 0.8696 |
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+ | 0.7413 | 4.58 | 1700 | 0.6392 | 0.8448 | 0.8589 | 0.8448 | 0.8430 | 0.8910 |
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+ | 0.7413 | 4.72 | 1750 | 0.7541 | 0.8205 | 0.8487 | 0.8205 | 0.8186 | 0.8754 |
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+ | 0.7413 | 4.85 | 1800 | 0.7061 | 0.8313 | 0.8545 | 0.8313 | 0.8277 | 0.8812 |
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+ | 0.7413 | 4.99 | 1850 | 0.7248 | 0.8394 | 0.8580 | 0.8394 | 0.8374 | 0.8872 |
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+ | 0.5888 | 5.12 | 1900 | 0.6656 | 0.8435 | 0.8635 | 0.8435 | 0.8419 | 0.8920 |
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+ | 0.5888 | 5.26 | 1950 | 0.6979 | 0.8340 | 0.8514 | 0.8340 | 0.8310 | 0.8854 |
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+ | 0.5888 | 5.39 | 2000 | 0.6792 | 0.8489 | 0.8670 | 0.8489 | 0.8475 | 0.8947 |
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+ | 0.5888 | 5.53 | 2050 | 0.6516 | 0.8516 | 0.8714 | 0.8516 | 0.8493 | 0.8966 |
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+ | 0.5888 | 5.66 | 2100 | 0.7691 | 0.8313 | 0.8564 | 0.8313 | 0.8301 | 0.8835 |
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+ | 0.5888 | 5.8 | 2150 | 0.7843 | 0.8219 | 0.8442 | 0.8219 | 0.8182 | 0.8765 |
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+ | 0.5888 | 5.93 | 2200 | 0.6041 | 0.8637 | 0.8810 | 0.8637 | 0.8618 | 0.9057 |
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+ | 0.4951 | 6.06 | 2250 | 0.6165 | 0.8489 | 0.8650 | 0.8489 | 0.8480 | 0.8949 |
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+ | 0.4951 | 6.2 | 2300 | 0.5761 | 0.8677 | 0.8845 | 0.8677 | 0.8674 | 0.9081 |
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+ | 0.4951 | 6.33 | 2350 | 0.7259 | 0.8394 | 0.8664 | 0.8394 | 0.8395 | 0.8887 |
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+ | 0.4951 | 6.47 | 2400 | 0.7164 | 0.8394 | 0.8603 | 0.8394 | 0.8373 | 0.8876 |
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+ | 0.4951 | 6.6 | 2450 | 0.6773 | 0.8529 | 0.8720 | 0.8529 | 0.8518 | 0.8966 |
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+ | 0.4951 | 6.74 | 2500 | 0.6292 | 0.8570 | 0.8728 | 0.8570 | 0.8564 | 0.8991 |
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+ | 0.4951 | 6.87 | 2550 | 0.8074 | 0.8286 | 0.8496 | 0.8286 | 0.8285 | 0.8792 |
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+ | 0.432 | 7.01 | 2600 | 0.7307 | 0.8394 | 0.8627 | 0.8394 | 0.8380 | 0.8872 |
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+ | 0.432 | 7.14 | 2650 | 0.6386 | 0.8529 | 0.8737 | 0.8529 | 0.8517 | 0.8966 |
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+ | 0.432 | 7.28 | 2700 | 0.5918 | 0.8516 | 0.8698 | 0.8516 | 0.8514 | 0.8966 |
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+ | 0.432 | 7.41 | 2750 | 0.6801 | 0.8596 | 0.8864 | 0.8596 | 0.8576 | 0.9018 |
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+ | 0.432 | 7.55 | 2800 | 0.6875 | 0.8570 | 0.8787 | 0.8570 | 0.8554 | 0.9001 |
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+ | 0.432 | 7.68 | 2850 | 0.5983 | 0.8623 | 0.8837 | 0.8623 | 0.8636 | 0.9047 |
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+ | 0.432 | 7.82 | 2900 | 0.6557 | 0.8623 | 0.8823 | 0.8623 | 0.8623 | 0.9032 |
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+ | 0.432 | 7.95 | 2950 | 0.6666 | 0.8543 | 0.8740 | 0.8543 | 0.8541 | 0.8981 |
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+ | 0.3923 | 8.09 | 3000 | 0.6942 | 0.8489 | 0.8663 | 0.8489 | 0.8487 | 0.8949 |
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+ | 0.3923 | 8.22 | 3050 | 0.5847 | 0.8718 | 0.8924 | 0.8718 | 0.8711 | 0.9099 |
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+ | 0.3923 | 8.36 | 3100 | 0.6560 | 0.8691 | 0.8827 | 0.8691 | 0.8673 | 0.9090 |
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+ | 0.3923 | 8.49 | 3150 | 0.7257 | 0.8556 | 0.8739 | 0.8556 | 0.8535 | 0.8981 |
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+ | 0.3923 | 8.63 | 3200 | 0.6361 | 0.8664 | 0.8794 | 0.8664 | 0.8656 | 0.9072 |
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+ | 0.3923 | 8.76 | 3250 | 0.7123 | 0.8596 | 0.8801 | 0.8596 | 0.8595 | 0.9019 |
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+ | 0.3923 | 8.89 | 3300 | 0.6568 | 0.8664 | 0.8827 | 0.8664 | 0.8654 | 0.9072 |
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+ | 0.3524 | 9.03 | 3350 | 0.7099 | 0.8556 | 0.8739 | 0.8556 | 0.8542 | 0.8995 |
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+ | 0.3524 | 9.16 | 3400 | 0.6693 | 0.8596 | 0.8759 | 0.8596 | 0.8586 | 0.9019 |
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+ | 0.3524 | 9.3 | 3450 | 0.7744 | 0.8354 | 0.8561 | 0.8354 | 0.8351 | 0.8845 |
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+ | 0.3524 | 9.43 | 3500 | 0.6985 | 0.8704 | 0.8887 | 0.8704 | 0.8705 | 0.9090 |
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+ | 0.3524 | 9.57 | 3550 | 0.8195 | 0.8435 | 0.8638 | 0.8435 | 0.8416 | 0.8907 |
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+ | 0.3524 | 9.7 | 3600 | 0.7067 | 0.8650 | 0.8756 | 0.8650 | 0.8636 | 0.9058 |
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+ | 0.3524 | 9.84 | 3650 | 0.7844 | 0.8529 | 0.8699 | 0.8529 | 0.8506 | 0.8958 |
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+ | 0.3524 | 9.97 | 3700 | 0.8100 | 0.8448 | 0.8627 | 0.8448 | 0.8428 | 0.8916 |
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+ | 0.3355 | 10.11 | 3750 | 0.7430 | 0.8704 | 0.8896 | 0.8704 | 0.8684 | 0.9086 |
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+ | 0.3355 | 10.24 | 3800 | 0.6455 | 0.8677 | 0.8815 | 0.8677 | 0.8656 | 0.9070 |
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+ | 0.3355 | 10.38 | 3850 | 0.6391 | 0.8650 | 0.8833 | 0.8650 | 0.8638 | 0.9051 |
139
 
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  ### Framework versions
runs/Jul20_05-03-03_LAPTOP-1GID9RGH/events.out.tfevents.1721426585.LAPTOP-1GID9RGH.19084.6 CHANGED
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