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hubert-classifier-aug-fold-0

Audio Classification
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hubert
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End of training

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  1. README.md +77 -53
  2. model.safetensors +1 -1
  3. runs/Jul20_11-11-23_LAPTOP-1GID9RGH/events.out.tfevents.1721451133.LAPTOP-1GID9RGH.23448.1 +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: 0.5649
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- - Accuracy: 0.8679
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- - Precision: 0.8791
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- - Recall: 0.8679
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- - F1: 0.8667
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- - Binary: 0.9082
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  ## Model description
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@@ -52,6 +52,7 @@ 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: 30
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  - mixed_precision_training: Native AMP
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@@ -59,53 +60,76 @@ 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 | 3.8775 | 0.0512 | 0.0179 | 0.0512 | 0.0103 | 0.3205 |
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- | No log | 0.27 | 100 | 3.4967 | 0.0526 | 0.0224 | 0.0526 | 0.0156 | 0.3290 |
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- | No log | 0.4 | 150 | 3.2301 | 0.0970 | 0.0222 | 0.0970 | 0.0291 | 0.3659 |
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- | No log | 0.54 | 200 | 3.0363 | 0.1361 | 0.0437 | 0.1361 | 0.0582 | 0.3920 |
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- | No log | 0.67 | 250 | 2.7181 | 0.2385 | 0.1581 | 0.2385 | 0.1495 | 0.4625 |
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- | No log | 0.81 | 300 | 2.3875 | 0.3019 | 0.1996 | 0.3019 | 0.2063 | 0.5101 |
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- | No log | 0.94 | 350 | 2.1936 | 0.3895 | 0.3562 | 0.3895 | 0.3154 | 0.5706 |
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- | 3.2299 | 1.08 | 400 | 1.9583 | 0.4555 | 0.3983 | 0.4555 | 0.3887 | 0.6168 |
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- | 3.2299 | 1.21 | 450 | 1.7394 | 0.5 | 0.4895 | 0.5 | 0.4446 | 0.6497 |
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- | 3.2299 | 1.35 | 500 | 1.5140 | 0.6065 | 0.6096 | 0.6065 | 0.5686 | 0.7244 |
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- | 3.2299 | 1.48 | 550 | 1.3457 | 0.6361 | 0.6197 | 0.6361 | 0.5975 | 0.7460 |
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- | 3.2299 | 1.62 | 600 | 1.2439 | 0.6644 | 0.6718 | 0.6644 | 0.6320 | 0.7644 |
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- | 3.2299 | 1.75 | 650 | 1.0984 | 0.7035 | 0.6871 | 0.7035 | 0.6718 | 0.7927 |
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- | 3.2299 | 1.89 | 700 | 1.0212 | 0.7480 | 0.7557 | 0.7480 | 0.7313 | 0.8225 |
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- | 1.7016 | 2.02 | 750 | 0.9402 | 0.7412 | 0.7504 | 0.7412 | 0.7217 | 0.8187 |
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- | 1.7016 | 2.16 | 800 | 0.9117 | 0.7412 | 0.7498 | 0.7412 | 0.7285 | 0.8178 |
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- | 1.7016 | 2.29 | 850 | 0.8195 | 0.7763 | 0.8018 | 0.7763 | 0.7704 | 0.8439 |
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- | 1.7016 | 2.43 | 900 | 0.7736 | 0.7898 | 0.8195 | 0.7898 | 0.7817 | 0.8531 |
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- | 1.7016 | 2.56 | 950 | 0.7723 | 0.7965 | 0.8227 | 0.7965 | 0.7855 | 0.8597 |
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- | 1.7016 | 2.7 | 1000 | 0.7737 | 0.7857 | 0.8131 | 0.7857 | 0.7780 | 0.8512 |
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- | 1.7016 | 2.83 | 1050 | 0.6884 | 0.8113 | 0.8281 | 0.8113 | 0.8057 | 0.8679 |
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- | 1.7016 | 2.97 | 1100 | 0.7205 | 0.8032 | 0.8297 | 0.8032 | 0.7987 | 0.8646 |
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- | 1.0665 | 3.1 | 1150 | 0.6086 | 0.8342 | 0.8483 | 0.8342 | 0.8297 | 0.8864 |
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- | 1.0665 | 3.24 | 1200 | 0.5848 | 0.8531 | 0.8690 | 0.8531 | 0.8503 | 0.8991 |
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- | 1.0665 | 3.37 | 1250 | 0.6821 | 0.8369 | 0.8551 | 0.8369 | 0.8315 | 0.8861 |
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- | 1.0665 | 3.51 | 1300 | 0.6050 | 0.8315 | 0.8491 | 0.8315 | 0.8287 | 0.8833 |
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- | 1.0665 | 3.64 | 1350 | 0.5871 | 0.8504 | 0.8708 | 0.8504 | 0.8477 | 0.8960 |
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- | 1.0665 | 3.78 | 1400 | 0.6485 | 0.8329 | 0.8560 | 0.8329 | 0.8298 | 0.8837 |
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- | 1.0665 | 3.91 | 1450 | 0.5727 | 0.8518 | 0.8673 | 0.8518 | 0.8484 | 0.8970 |
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- | 0.7598 | 4.05 | 1500 | 0.5555 | 0.8652 | 0.8752 | 0.8652 | 0.8623 | 0.9069 |
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- | 0.7598 | 4.18 | 1550 | 0.5407 | 0.8585 | 0.8702 | 0.8585 | 0.8558 | 0.9031 |
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- | 0.7598 | 4.32 | 1600 | 0.5282 | 0.8679 | 0.8813 | 0.8679 | 0.8658 | 0.9082 |
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- | 0.7598 | 4.45 | 1650 | 0.5557 | 0.8558 | 0.8693 | 0.8558 | 0.8540 | 0.9008 |
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- | 0.7598 | 4.59 | 1700 | 0.5832 | 0.8693 | 0.8866 | 0.8693 | 0.8686 | 0.9097 |
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- | 0.7598 | 4.72 | 1750 | 0.5623 | 0.8720 | 0.8818 | 0.8720 | 0.8707 | 0.9097 |
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- | 0.7598 | 4.86 | 1800 | 0.5598 | 0.8612 | 0.8811 | 0.8612 | 0.8614 | 0.9040 |
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- | 0.7598 | 4.99 | 1850 | 0.6415 | 0.8504 | 0.8627 | 0.8504 | 0.8479 | 0.8970 |
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- | 0.5874 | 5.12 | 1900 | 0.5866 | 0.8652 | 0.8788 | 0.8652 | 0.8620 | 0.9074 |
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- | 0.5874 | 5.26 | 1950 | 0.6092 | 0.8612 | 0.8725 | 0.8612 | 0.8603 | 0.9036 |
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- | 0.5874 | 5.39 | 2000 | 0.5544 | 0.8733 | 0.8870 | 0.8733 | 0.8730 | 0.9125 |
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- | 0.5874 | 5.53 | 2050 | 0.5478 | 0.8598 | 0.8726 | 0.8598 | 0.8591 | 0.9026 |
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- | 0.5874 | 5.66 | 2100 | 0.5683 | 0.8733 | 0.8908 | 0.8733 | 0.8713 | 0.9116 |
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- | 0.5874 | 5.8 | 2150 | 0.5644 | 0.8679 | 0.8782 | 0.8679 | 0.8643 | 0.9102 |
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- | 0.5874 | 5.93 | 2200 | 0.5471 | 0.8666 | 0.8759 | 0.8666 | 0.8639 | 0.9093 |
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- | 0.4963 | 6.07 | 2250 | 0.6050 | 0.8612 | 0.8757 | 0.8612 | 0.8603 | 0.9044 |
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- | 0.4963 | 6.2 | 2300 | 0.5908 | 0.8625 | 0.8770 | 0.8625 | 0.8613 | 0.9054 |
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- | 0.4963 | 6.34 | 2350 | 0.5994 | 0.8598 | 0.8773 | 0.8598 | 0.8589 | 0.9036 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7030
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+ - Accuracy: 0.8571
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+ - Precision: 0.8685
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+ - Recall: 0.8571
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+ - F1: 0.8514
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+ - Binary: 0.9007
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  ## Model description
31
 
 
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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 Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.13 | 50 | 4.4206 | 0.0243 | 0.0145 | 0.0243 | 0.0115 | 0.1888 |
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+ | No log | 0.27 | 100 | 4.3046 | 0.0458 | 0.0194 | 0.0458 | 0.0171 | 0.3013 |
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+ | No log | 0.4 | 150 | 3.9493 | 0.0714 | 0.0250 | 0.0714 | 0.0284 | 0.3407 |
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+ | No log | 0.54 | 200 | 3.7625 | 0.0768 | 0.0518 | 0.0768 | 0.0328 | 0.3255 |
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+ | No log | 0.67 | 250 | 3.3324 | 0.1644 | 0.0956 | 0.1644 | 0.0982 | 0.4112 |
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+ | No log | 0.81 | 300 | 3.0658 | 0.2466 | 0.1774 | 0.2466 | 0.1719 | 0.4704 |
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+ | No log | 0.94 | 350 | 2.7642 | 0.3450 | 0.2437 | 0.3450 | 0.2520 | 0.5375 |
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+ | 3.7949 | 1.08 | 400 | 2.4739 | 0.3760 | 0.3261 | 0.3760 | 0.3038 | 0.5586 |
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+ | 3.7949 | 1.21 | 450 | 2.1757 | 0.4757 | 0.3860 | 0.4757 | 0.4022 | 0.6303 |
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+ | 3.7949 | 1.35 | 500 | 1.8422 | 0.5323 | 0.4744 | 0.5323 | 0.4677 | 0.6717 |
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+ | 3.7949 | 1.48 | 550 | 1.6818 | 0.5889 | 0.5464 | 0.5889 | 0.5402 | 0.7100 |
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+ | 3.7949 | 1.62 | 600 | 1.4944 | 0.6173 | 0.6054 | 0.6173 | 0.5761 | 0.7319 |
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+ | 3.7949 | 1.75 | 650 | 1.3503 | 0.6429 | 0.6457 | 0.6429 | 0.6069 | 0.7481 |
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+ | 3.7949 | 1.89 | 700 | 1.2159 | 0.6752 | 0.6724 | 0.6752 | 0.6372 | 0.7732 |
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+ | 1.9673 | 2.02 | 750 | 1.0849 | 0.7049 | 0.7117 | 0.7049 | 0.6748 | 0.7930 |
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+ | 1.9673 | 2.16 | 800 | 1.0424 | 0.7183 | 0.7257 | 0.7183 | 0.6987 | 0.8011 |
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+ | 1.9673 | 2.29 | 850 | 0.8607 | 0.7830 | 0.7911 | 0.7830 | 0.7694 | 0.8470 |
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+ | 1.9673 | 2.43 | 900 | 0.8606 | 0.7668 | 0.7813 | 0.7668 | 0.7506 | 0.8372 |
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+ | 1.9673 | 2.56 | 950 | 0.7939 | 0.7803 | 0.7739 | 0.7803 | 0.7606 | 0.8487 |
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+ | 1.9673 | 2.7 | 1000 | 0.7883 | 0.8059 | 0.8257 | 0.8059 | 0.7976 | 0.8656 |
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+ | 1.9673 | 2.83 | 1050 | 0.7567 | 0.8032 | 0.8214 | 0.8032 | 0.7947 | 0.8640 |
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+ | 1.9673 | 2.97 | 1100 | 0.6989 | 0.8181 | 0.8399 | 0.8181 | 0.8063 | 0.8745 |
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+ | 1.0987 | 3.1 | 1150 | 0.7500 | 0.8100 | 0.8223 | 0.8100 | 0.8043 | 0.8660 |
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+ | 1.0987 | 3.24 | 1200 | 0.6802 | 0.8261 | 0.8381 | 0.8261 | 0.8184 | 0.8794 |
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+ | 1.0987 | 3.37 | 1250 | 0.6614 | 0.8396 | 0.8558 | 0.8396 | 0.8359 | 0.8877 |
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+ | 1.0987 | 3.51 | 1300 | 0.6928 | 0.8261 | 0.8511 | 0.8261 | 0.8236 | 0.8791 |
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+ | 1.0987 | 3.64 | 1350 | 0.6146 | 0.8410 | 0.8588 | 0.8410 | 0.8401 | 0.8896 |
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+ | 1.0987 | 3.78 | 1400 | 0.6958 | 0.8248 | 0.8412 | 0.8248 | 0.8191 | 0.8796 |
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+ | 1.0987 | 3.91 | 1450 | 0.6785 | 0.8342 | 0.8556 | 0.8342 | 0.8309 | 0.8857 |
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+ | 0.7483 | 4.05 | 1500 | 0.7412 | 0.8261 | 0.8461 | 0.8261 | 0.8244 | 0.8784 |
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+ | 0.7483 | 4.18 | 1550 | 0.6778 | 0.8356 | 0.8538 | 0.8356 | 0.8317 | 0.8868 |
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+ | 0.7483 | 4.32 | 1600 | 0.7032 | 0.8437 | 0.8657 | 0.8437 | 0.8405 | 0.8946 |
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+ | 0.7483 | 4.45 | 1650 | 0.7373 | 0.8329 | 0.8564 | 0.8329 | 0.8299 | 0.8850 |
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+ | 0.7483 | 4.59 | 1700 | 0.6958 | 0.8423 | 0.8593 | 0.8423 | 0.8401 | 0.8915 |
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+ | 0.7483 | 4.72 | 1750 | 0.7395 | 0.8329 | 0.8513 | 0.8329 | 0.8327 | 0.8865 |
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+ | 0.7483 | 4.86 | 1800 | 0.7017 | 0.8477 | 0.8651 | 0.8477 | 0.8453 | 0.8953 |
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+ | 0.7483 | 4.99 | 1850 | 0.7240 | 0.8423 | 0.8582 | 0.8423 | 0.8410 | 0.8922 |
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+ | 0.5887 | 5.12 | 1900 | 0.6810 | 0.8464 | 0.8694 | 0.8464 | 0.8451 | 0.8943 |
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+ | 0.5887 | 5.26 | 1950 | 0.6091 | 0.8706 | 0.8828 | 0.8706 | 0.8688 | 0.9111 |
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+ | 0.5887 | 5.39 | 2000 | 0.6617 | 0.8491 | 0.8669 | 0.8491 | 0.8474 | 0.8974 |
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+ | 0.5887 | 5.53 | 2050 | 0.6712 | 0.8477 | 0.8662 | 0.8477 | 0.8458 | 0.8966 |
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+ | 0.5887 | 5.66 | 2100 | 0.6988 | 0.8437 | 0.8570 | 0.8437 | 0.8413 | 0.8915 |
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+ | 0.5887 | 5.8 | 2150 | 0.6644 | 0.8477 | 0.8624 | 0.8477 | 0.8455 | 0.8953 |
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+ | 0.5887 | 5.93 | 2200 | 0.6416 | 0.8652 | 0.8790 | 0.8652 | 0.8622 | 0.9073 |
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+ | 0.485 | 6.07 | 2250 | 0.6484 | 0.8585 | 0.8705 | 0.8585 | 0.8568 | 0.9030 |
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+ | 0.485 | 6.2 | 2300 | 0.6690 | 0.8585 | 0.8736 | 0.8585 | 0.8564 | 0.9019 |
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+ | 0.485 | 6.34 | 2350 | 0.6469 | 0.8639 | 0.8790 | 0.8639 | 0.8616 | 0.9071 |
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+ | 0.485 | 6.47 | 2400 | 0.7418 | 0.8518 | 0.8684 | 0.8518 | 0.8515 | 0.8968 |
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+ | 0.485 | 6.61 | 2450 | 0.6821 | 0.8625 | 0.8788 | 0.8625 | 0.8615 | 0.9075 |
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+ | 0.485 | 6.74 | 2500 | 0.7012 | 0.8652 | 0.8837 | 0.8652 | 0.8636 | 0.9090 |
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+ | 0.485 | 6.88 | 2550 | 0.6546 | 0.8652 | 0.8819 | 0.8652 | 0.8658 | 0.9085 |
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+ | 0.4243 | 7.01 | 2600 | 0.6619 | 0.8639 | 0.8769 | 0.8639 | 0.8631 | 0.9070 |
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+ | 0.4243 | 7.15 | 2650 | 0.7000 | 0.8531 | 0.8716 | 0.8531 | 0.8518 | 0.8995 |
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+ | 0.4243 | 7.28 | 2700 | 0.6560 | 0.8720 | 0.8864 | 0.8720 | 0.8715 | 0.9112 |
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+ | 0.4243 | 7.42 | 2750 | 0.6458 | 0.8639 | 0.8786 | 0.8639 | 0.8634 | 0.9074 |
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+ | 0.4243 | 7.55 | 2800 | 0.6701 | 0.8747 | 0.8896 | 0.8747 | 0.8742 | 0.9155 |
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+ | 0.4243 | 7.69 | 2850 | 0.7282 | 0.8477 | 0.8706 | 0.8477 | 0.8477 | 0.8970 |
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+ | 0.4243 | 7.82 | 2900 | 0.6578 | 0.8612 | 0.8726 | 0.8612 | 0.8597 | 0.9061 |
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+ | 0.4243 | 7.96 | 2950 | 0.6244 | 0.8720 | 0.8829 | 0.8720 | 0.8704 | 0.9142 |
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+ | 0.378 | 8.09 | 3000 | 0.6445 | 0.8733 | 0.8896 | 0.8733 | 0.8726 | 0.9140 |
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+ | 0.378 | 8.23 | 3050 | 0.6983 | 0.8612 | 0.8766 | 0.8612 | 0.8606 | 0.9055 |
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+ | 0.378 | 8.36 | 3100 | 0.6355 | 0.8760 | 0.8922 | 0.8760 | 0.8750 | 0.9154 |
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+ | 0.378 | 8.5 | 3150 | 0.6770 | 0.8747 | 0.8883 | 0.8747 | 0.8726 | 0.9135 |
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+ | 0.378 | 8.63 | 3200 | 0.6472 | 0.8706 | 0.8798 | 0.8706 | 0.8680 | 0.9097 |
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+ | 0.378 | 8.77 | 3250 | 0.7739 | 0.8544 | 0.8691 | 0.8544 | 0.8512 | 0.8970 |
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+ | 0.378 | 8.9 | 3300 | 0.6805 | 0.8612 | 0.8766 | 0.8612 | 0.8587 | 0.9046 |
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+ | 0.3491 | 9.04 | 3350 | 0.6382 | 0.8733 | 0.8829 | 0.8733 | 0.8717 | 0.9135 |
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+ | 0.3491 | 9.17 | 3400 | 0.6927 | 0.8652 | 0.8793 | 0.8652 | 0.8642 | 0.9080 |
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+ | 0.3491 | 9.31 | 3450 | 0.8407 | 0.8518 | 0.8711 | 0.8518 | 0.8488 | 0.8996 |
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+ | 0.3491 | 9.44 | 3500 | 0.6628 | 0.8733 | 0.8823 | 0.8733 | 0.8714 | 0.9121 |
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  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
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- oid sha256:bdfeb863ddc6df3a09013582c2cf2fe087b1343f256223b96ec147a4e8b0d0cf
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  size 378386248
 
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  size 378386248
runs/Jul20_11-11-23_LAPTOP-1GID9RGH/events.out.tfevents.1721451133.LAPTOP-1GID9RGH.23448.1 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:53f3739b8a972e99d6a7861b42ef13ed18c526fad1faa82f88bc875d3af8a36e
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