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hubert-classifier-aug-fold-3-old

Audio Classification
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hubert
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  1. README.md +91 -23
  2. model.safetensors +1 -1
  3. runs/Jun28_10-59-46_LAPTOP-1GID9RGH/events.out.tfevents.1719547188.LAPTOP-1GID9RGH.24468.0 +2 -2
  4. runs/Jun28_10-59-46_LAPTOP-1GID9RGH/events.out.tfevents.1719549251.LAPTOP-1GID9RGH.24468.1 +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: 4.4196
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- - Accuracy: 0.0135
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- - Precision: 0.0002
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- - Recall: 0.0135
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- - F1: 0.0004
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- - Binary: 0.1361
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  ## Model description
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@@ -59,23 +59,91 @@ 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 | 0.19 | 50 | 4.4282 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1296 |
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- | No log | 0.38 | 100 | 4.4319 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1270 |
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- | No log | 0.58 | 150 | 4.4266 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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- | No log | 0.77 | 200 | 4.4271 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1270 |
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- | No log | 0.96 | 250 | 4.4262 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1270 |
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- | 4.4401 | 1.15 | 300 | 4.4287 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1353 |
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- | 4.4401 | 1.34 | 350 | 4.4283 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1270 |
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- | 4.4401 | 1.53 | 400 | 4.4279 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1226 |
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- | 4.4401 | 1.73 | 450 | 4.4260 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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- | 4.4401 | 1.92 | 500 | 4.4257 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1353 |
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- | 4.4198 | 2.11 | 550 | 4.4258 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1251 |
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- | 4.4198 | 2.3 | 600 | 4.4257 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1245 |
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- | 4.4198 | 2.49 | 650 | 4.4275 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1270 |
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- | 4.4198 | 2.68 | 700 | 4.4266 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4198 | 2.88 | 750 | 4.4256 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1353 |
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- | 4.4186 | 3.07 | 800 | 4.4268 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1353 |
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- | 4.4186 | 3.26 | 850 | 4.4263 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1353 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5105
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+ - Accuracy: 0.8639
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+ - Precision: 0.8778
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+ - Recall: 0.8639
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+ - F1: 0.8624
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+ - Binary: 0.9049
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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 | 0.22 | 50 | 3.8852 | 0.0445 | 0.0072 | 0.0445 | 0.0108 | 0.3195 |
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+ | No log | 0.43 | 100 | 3.4756 | 0.0499 | 0.0263 | 0.0499 | 0.0140 | 0.3247 |
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+ | No log | 0.65 | 150 | 3.2680 | 0.0916 | 0.0193 | 0.0916 | 0.0272 | 0.3611 |
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+ | No log | 0.86 | 200 | 3.1133 | 0.1038 | 0.0266 | 0.1038 | 0.0369 | 0.3695 |
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+ | 3.7026 | 1.08 | 250 | 2.9266 | 0.1765 | 0.0816 | 0.1765 | 0.0972 | 0.4212 |
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+ | 3.7026 | 1.29 | 300 | 2.7455 | 0.2412 | 0.1540 | 0.2412 | 0.1516 | 0.4644 |
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+ | 3.7026 | 1.51 | 350 | 2.4869 | 0.2898 | 0.1860 | 0.2898 | 0.1860 | 0.5008 |
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+ | 3.7026 | 1.73 | 400 | 2.2598 | 0.3275 | 0.2377 | 0.3275 | 0.2426 | 0.5284 |
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+ | 3.7026 | 1.94 | 450 | 2.0416 | 0.4232 | 0.3176 | 0.4232 | 0.3356 | 0.5947 |
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+ | 2.8767 | 2.16 | 500 | 1.8498 | 0.4461 | 0.3751 | 0.4461 | 0.3752 | 0.6124 |
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+ | 2.8767 | 2.37 | 550 | 1.6887 | 0.5202 | 0.4826 | 0.5202 | 0.4522 | 0.6639 |
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+ | 2.8767 | 2.59 | 600 | 1.5765 | 0.5256 | 0.4880 | 0.5256 | 0.4630 | 0.6677 |
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+ | 2.8767 | 2.8 | 650 | 1.4405 | 0.5930 | 0.5650 | 0.5930 | 0.5478 | 0.7152 |
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+ | 2.2257 | 3.02 | 700 | 1.3663 | 0.6253 | 0.6037 | 0.6253 | 0.5834 | 0.7371 |
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+ | 2.2257 | 3.24 | 750 | 1.2404 | 0.6577 | 0.6668 | 0.6577 | 0.6159 | 0.7602 |
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+ | 2.2257 | 3.45 | 800 | 1.1946 | 0.6887 | 0.7128 | 0.6887 | 0.6654 | 0.7807 |
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+ | 2.2257 | 3.67 | 850 | 1.0658 | 0.7251 | 0.7319 | 0.7251 | 0.7025 | 0.8073 |
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+ | 2.2257 | 3.88 | 900 | 1.0567 | 0.7129 | 0.7529 | 0.7129 | 0.6874 | 0.7982 |
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+ | 1.8296 | 4.1 | 950 | 1.0013 | 0.7372 | 0.7537 | 0.7372 | 0.7209 | 0.8151 |
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+ | 1.8296 | 4.31 | 1000 | 0.9092 | 0.7534 | 0.7863 | 0.7534 | 0.7388 | 0.8284 |
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+ | 1.8296 | 4.53 | 1050 | 0.8869 | 0.7574 | 0.7865 | 0.7574 | 0.7467 | 0.8313 |
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+ | 1.8296 | 4.75 | 1100 | 0.8033 | 0.7763 | 0.8084 | 0.7763 | 0.7677 | 0.8446 |
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+ | 1.8296 | 4.96 | 1150 | 0.7981 | 0.7790 | 0.8161 | 0.7790 | 0.7664 | 0.8451 |
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+ | 1.6084 | 5.18 | 1200 | 0.7499 | 0.7978 | 0.8090 | 0.7978 | 0.7889 | 0.8588 |
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+ | 1.6084 | 5.39 | 1250 | 0.7066 | 0.7938 | 0.8039 | 0.7938 | 0.7807 | 0.8550 |
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+ | 1.6084 | 5.61 | 1300 | 0.7537 | 0.7938 | 0.8163 | 0.7938 | 0.7871 | 0.8555 |
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+ | 1.6084 | 5.83 | 1350 | 0.7293 | 0.8113 | 0.8328 | 0.8113 | 0.8065 | 0.8687 |
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+ | 1.4253 | 6.04 | 1400 | 0.7055 | 0.7992 | 0.8276 | 0.7992 | 0.7905 | 0.8593 |
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+ | 1.4253 | 6.26 | 1450 | 0.6732 | 0.8059 | 0.8226 | 0.8059 | 0.8003 | 0.8648 |
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+ | 1.4253 | 6.47 | 1500 | 0.6510 | 0.8315 | 0.8461 | 0.8315 | 0.8272 | 0.8838 |
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+ | 1.4253 | 6.69 | 1550 | 0.6113 | 0.8329 | 0.8565 | 0.8329 | 0.8314 | 0.8814 |
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+ | 1.4253 | 6.9 | 1600 | 0.6299 | 0.8248 | 0.8435 | 0.8248 | 0.8215 | 0.8787 |
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+ | 1.2904 | 7.12 | 1650 | 0.6132 | 0.8410 | 0.8553 | 0.8410 | 0.8377 | 0.8900 |
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+ | 1.2904 | 7.34 | 1700 | 0.5883 | 0.8464 | 0.8592 | 0.8464 | 0.8437 | 0.8938 |
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+ | 1.2904 | 7.55 | 1750 | 0.5995 | 0.8437 | 0.8565 | 0.8437 | 0.8363 | 0.8908 |
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+ | 1.2904 | 7.77 | 1800 | 0.5861 | 0.8450 | 0.8612 | 0.8450 | 0.8400 | 0.8923 |
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+ | 1.2904 | 7.98 | 1850 | 0.6007 | 0.8410 | 0.8613 | 0.8410 | 0.8370 | 0.8889 |
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+ | 1.2105 | 8.2 | 1900 | 0.5518 | 0.8437 | 0.8594 | 0.8437 | 0.8407 | 0.8904 |
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+ | 1.2105 | 8.41 | 1950 | 0.5739 | 0.8342 | 0.8522 | 0.8342 | 0.8294 | 0.8846 |
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+ | 1.2105 | 8.63 | 2000 | 0.5998 | 0.8275 | 0.8475 | 0.8275 | 0.8222 | 0.8806 |
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+ | 1.2105 | 8.85 | 2050 | 0.5662 | 0.8531 | 0.8716 | 0.8531 | 0.8471 | 0.8978 |
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+ | 1.1291 | 9.06 | 2100 | 0.5144 | 0.8639 | 0.8818 | 0.8639 | 0.8607 | 0.9040 |
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+ | 1.1291 | 9.28 | 2150 | 0.4782 | 0.8625 | 0.8843 | 0.8625 | 0.8584 | 0.9036 |
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+ | 1.1291 | 9.49 | 2200 | 0.4787 | 0.8612 | 0.8736 | 0.8612 | 0.8571 | 0.9026 |
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+ | 1.1291 | 9.71 | 2250 | 0.4866 | 0.8666 | 0.8800 | 0.8666 | 0.8629 | 0.9050 |
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+ | 1.1291 | 9.92 | 2300 | 0.5999 | 0.8342 | 0.8468 | 0.8342 | 0.8279 | 0.8846 |
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+ | 1.0646 | 10.14 | 2350 | 0.5397 | 0.8518 | 0.8723 | 0.8518 | 0.8496 | 0.8962 |
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+ | 1.0646 | 10.36 | 2400 | 0.4718 | 0.8585 | 0.8761 | 0.8585 | 0.8541 | 0.9005 |
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+ | 1.0646 | 10.57 | 2450 | 0.4909 | 0.8625 | 0.8772 | 0.8625 | 0.8603 | 0.9035 |
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+ | 1.0646 | 10.79 | 2500 | 0.4706 | 0.8544 | 0.8729 | 0.8544 | 0.8510 | 0.8988 |
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+ | 1.0145 | 11.0 | 2550 | 0.4830 | 0.8558 | 0.8692 | 0.8558 | 0.8522 | 0.8993 |
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+ | 1.0145 | 11.22 | 2600 | 0.4926 | 0.8652 | 0.8774 | 0.8652 | 0.8622 | 0.9055 |
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+ | 1.0145 | 11.43 | 2650 | 0.4879 | 0.8693 | 0.8827 | 0.8693 | 0.8672 | 0.9086 |
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+ | 1.0145 | 11.65 | 2700 | 0.5248 | 0.8666 | 0.8811 | 0.8666 | 0.8634 | 0.9063 |
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+ | 1.0145 | 11.87 | 2750 | 0.4835 | 0.8585 | 0.8733 | 0.8585 | 0.8550 | 0.9012 |
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+ | 0.9883 | 12.08 | 2800 | 0.4525 | 0.8814 | 0.8929 | 0.8814 | 0.8807 | 0.9177 |
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+ | 0.9883 | 12.3 | 2850 | 0.4808 | 0.8706 | 0.8856 | 0.8706 | 0.8667 | 0.9101 |
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+ | 0.9883 | 12.51 | 2900 | 0.4736 | 0.8720 | 0.8848 | 0.8720 | 0.8699 | 0.9111 |
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+ | 0.9883 | 12.73 | 2950 | 0.4256 | 0.8774 | 0.8945 | 0.8774 | 0.8762 | 0.9146 |
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+ | 0.9883 | 12.94 | 3000 | 0.4400 | 0.8841 | 0.8953 | 0.8841 | 0.8816 | 0.9182 |
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+ | 0.9479 | 13.16 | 3050 | 0.4743 | 0.8666 | 0.8836 | 0.8666 | 0.8660 | 0.9067 |
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+ | 0.9479 | 13.38 | 3100 | 0.4729 | 0.8760 | 0.8926 | 0.8760 | 0.8749 | 0.9133 |
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+ | 0.9479 | 13.59 | 3150 | 0.4608 | 0.8733 | 0.8857 | 0.8733 | 0.8704 | 0.9105 |
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+ | 0.9479 | 13.81 | 3200 | 0.4206 | 0.8868 | 0.8948 | 0.8868 | 0.8841 | 0.9216 |
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+ | 0.8867 | 14.02 | 3250 | 0.4332 | 0.8881 | 0.8992 | 0.8881 | 0.8871 | 0.9201 |
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+ | 0.8867 | 14.24 | 3300 | 0.4635 | 0.8733 | 0.8860 | 0.8733 | 0.8718 | 0.9108 |
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+ | 0.8867 | 14.46 | 3350 | 0.4691 | 0.8827 | 0.8960 | 0.8827 | 0.8820 | 0.9170 |
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+ | 0.8867 | 14.67 | 3400 | 0.4282 | 0.8774 | 0.8882 | 0.8774 | 0.8754 | 0.9140 |
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+ | 0.8867 | 14.89 | 3450 | 0.4504 | 0.8801 | 0.8917 | 0.8801 | 0.8782 | 0.9162 |
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+ | 0.8593 | 15.1 | 3500 | 0.4781 | 0.8760 | 0.8884 | 0.8760 | 0.8736 | 0.9125 |
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+ | 0.8593 | 15.32 | 3550 | 0.4614 | 0.8895 | 0.8993 | 0.8895 | 0.8878 | 0.9230 |
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+ | 0.8593 | 15.53 | 3600 | 0.4659 | 0.8774 | 0.8918 | 0.8774 | 0.8766 | 0.9151 |
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+ | 0.8593 | 15.75 | 3650 | 0.4496 | 0.8814 | 0.8916 | 0.8814 | 0.8797 | 0.9164 |
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+ | 0.8593 | 15.97 | 3700 | 0.4648 | 0.8827 | 0.8941 | 0.8827 | 0.8817 | 0.9186 |
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+ | 0.8329 | 16.18 | 3750 | 0.4735 | 0.8827 | 0.8962 | 0.8827 | 0.8815 | 0.9182 |
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+ | 0.8329 | 16.4 | 3800 | 0.4255 | 0.8935 | 0.9057 | 0.8935 | 0.8928 | 0.9252 |
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+ | 0.8329 | 16.61 | 3850 | 0.4689 | 0.8747 | 0.8878 | 0.8747 | 0.8739 | 0.9117 |
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+ | 0.8329 | 16.83 | 3900 | 0.4596 | 0.8841 | 0.8948 | 0.8841 | 0.8834 | 0.9183 |
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+ | 0.8168 | 17.04 | 3950 | 0.4942 | 0.8760 | 0.8914 | 0.8760 | 0.8714 | 0.9123 |
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+ | 0.8168 | 17.26 | 4000 | 0.5265 | 0.8747 | 0.8899 | 0.8747 | 0.8733 | 0.9117 |
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+ | 0.8168 | 17.48 | 4050 | 0.4592 | 0.8787 | 0.8910 | 0.8787 | 0.8767 | 0.9150 |
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+ | 0.8168 | 17.69 | 4100 | 0.4839 | 0.8693 | 0.8824 | 0.8693 | 0.8678 | 0.9080 |
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+ | 0.8168 | 17.91 | 4150 | 0.4688 | 0.8827 | 0.8923 | 0.8827 | 0.8804 | 0.9173 |
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+ | 0.7604 | 18.12 | 4200 | 0.4593 | 0.8733 | 0.8834 | 0.8733 | 0.8708 | 0.9098 |
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+ | 0.7604 | 18.34 | 4250 | 0.4871 | 0.8760 | 0.8876 | 0.8760 | 0.8741 | 0.9117 |
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  ### Framework versions
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