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Nugrahasetyaardi
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SER_model_xapiens

Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Inference Endpoints
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  1. README.md +103 -6
  2. model.safetensors +1 -1
  3. runs/Jun05_14-11-50_DESKTOP-6VKE8JE/events.out.tfevents.1717571510.DESKTOP-6VKE8JE.23756.6 +2 -2
README.md CHANGED
@@ -17,8 +17,8 @@ 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 the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7677
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- - Accuracy: 0.4076
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  ## Model description
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@@ -46,15 +46,112 @@ The following hyperparameters were used during training:
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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_ratio: 0.1
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 9 | 1.8705 | 0.4076 |
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- | 1.967 | 2.0 | 18 | 1.7793 | 0.4076 |
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- | 1.8203 | 3.0 | 27 | 1.7677 | 0.4076 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.5738
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+ - Accuracy: 0.3041
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  ## Model description
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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_ratio: 0.1
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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 9 | 1.7645 | 0.4076 |
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+ | 1.7866 | 2.0 | 18 | 1.7565 | 0.4076 |
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+ | 1.7798 | 3.0 | 27 | 1.7455 | 0.4076 |
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+ | 1.7644 | 4.0 | 36 | 1.7316 | 0.4076 |
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+ | 1.7493 | 5.0 | 45 | 1.7171 | 0.4181 |
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+ | 1.7117 | 6.0 | 54 | 1.7175 | 0.4233 |
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+ | 1.7218 | 7.0 | 63 | 1.7061 | 0.4220 |
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+ | 1.6752 | 8.0 | 72 | 1.7257 | 0.3958 |
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+ | 1.6892 | 9.0 | 81 | 1.7137 | 0.4181 |
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+ | 1.6567 | 10.0 | 90 | 1.7058 | 0.4220 |
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+ | 1.6567 | 11.0 | 99 | 1.7144 | 0.4299 |
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+ | 1.6334 | 12.0 | 108 | 1.7087 | 0.4076 |
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+ | 1.6437 | 13.0 | 117 | 1.7158 | 0.4273 |
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+ | 1.6167 | 14.0 | 126 | 1.7296 | 0.4076 |
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+ | 1.5712 | 15.0 | 135 | 1.7449 | 0.4168 |
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+ | 1.5781 | 16.0 | 144 | 1.7459 | 0.4246 |
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+ | 1.5872 | 17.0 | 153 | 1.7217 | 0.4168 |
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+ | 1.5484 | 18.0 | 162 | 1.7471 | 0.4128 |
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+ | 1.4936 | 19.0 | 171 | 1.7770 | 0.3893 |
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+ | 1.4869 | 20.0 | 180 | 1.7677 | 0.4076 |
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+ | 1.4869 | 21.0 | 189 | 1.8222 | 0.3709 |
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+ | 1.4602 | 22.0 | 198 | 1.7785 | 0.4024 |
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+ | 1.4563 | 23.0 | 207 | 1.8191 | 0.4024 |
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+ | 1.3762 | 24.0 | 216 | 1.7934 | 0.4115 |
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+ | 1.3903 | 25.0 | 225 | 1.8111 | 0.3919 |
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+ | 1.3899 | 26.0 | 234 | 1.9607 | 0.3185 |
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+ | 1.3592 | 27.0 | 243 | 1.8800 | 0.3775 |
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+ | 1.3378 | 28.0 | 252 | 1.8500 | 0.4063 |
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+ | 1.3135 | 29.0 | 261 | 1.8792 | 0.3840 |
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+ | 1.2669 | 30.0 | 270 | 1.9259 | 0.3735 |
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+ | 1.2669 | 31.0 | 279 | 2.0078 | 0.3224 |
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+ | 1.2779 | 32.0 | 288 | 1.9653 | 0.3421 |
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+ | 1.2368 | 33.0 | 297 | 1.9823 | 0.3499 |
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+ | 1.2116 | 34.0 | 306 | 1.9602 | 0.3775 |
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+ | 1.1851 | 35.0 | 315 | 2.0362 | 0.3447 |
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+ | 1.1703 | 36.0 | 324 | 2.0193 | 0.3434 |
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+ | 1.1224 | 37.0 | 333 | 2.0363 | 0.3552 |
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+ | 1.1211 | 38.0 | 342 | 2.0817 | 0.3277 |
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+ | 1.0862 | 39.0 | 351 | 2.0775 | 0.3355 |
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+ | 1.0847 | 40.0 | 360 | 2.1078 | 0.3303 |
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+ | 1.0847 | 41.0 | 369 | 2.1608 | 0.3067 |
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+ | 1.0448 | 42.0 | 378 | 2.0996 | 0.3591 |
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+ | 1.0356 | 43.0 | 387 | 2.1416 | 0.3329 |
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+ | 1.008 | 44.0 | 396 | 2.1892 | 0.3250 |
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+ | 1.0018 | 45.0 | 405 | 2.1390 | 0.3408 |
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+ | 0.9606 | 46.0 | 414 | 2.2137 | 0.3250 |
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+ | 0.9489 | 47.0 | 423 | 2.1843 | 0.3329 |
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+ | 0.9404 | 48.0 | 432 | 2.2826 | 0.2988 |
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+ | 0.9483 | 49.0 | 441 | 2.2073 | 0.3355 |
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+ | 0.9101 | 50.0 | 450 | 2.1827 | 0.3565 |
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+ | 0.9101 | 51.0 | 459 | 2.2765 | 0.3185 |
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+ | 0.896 | 52.0 | 468 | 2.2909 | 0.3080 |
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+ | 0.8558 | 53.0 | 477 | 2.2816 | 0.3014 |
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+ | 0.8433 | 54.0 | 486 | 2.2215 | 0.3342 |
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+ | 0.8194 | 55.0 | 495 | 2.2022 | 0.3565 |
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+ | 0.8436 | 56.0 | 504 | 2.2139 | 0.3630 |
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+ | 0.7808 | 57.0 | 513 | 2.2970 | 0.3054 |
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+ | 0.8009 | 58.0 | 522 | 2.3081 | 0.3329 |
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+ | 0.7575 | 59.0 | 531 | 2.3105 | 0.3119 |
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+ | 0.7894 | 60.0 | 540 | 2.3786 | 0.3028 |
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+ | 0.7894 | 61.0 | 549 | 2.3072 | 0.3185 |
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+ | 0.7532 | 62.0 | 558 | 2.3318 | 0.3014 |
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+ | 0.7222 | 63.0 | 567 | 2.3697 | 0.3172 |
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+ | 0.7116 | 64.0 | 576 | 2.4125 | 0.3198 |
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+ | 0.7286 | 65.0 | 585 | 2.3694 | 0.3119 |
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+ | 0.6906 | 66.0 | 594 | 2.3974 | 0.3028 |
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+ | 0.6642 | 67.0 | 603 | 2.4020 | 0.3001 |
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+ | 0.6737 | 68.0 | 612 | 2.3513 | 0.3316 |
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+ | 0.6571 | 69.0 | 621 | 2.3638 | 0.3447 |
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+ | 0.6482 | 70.0 | 630 | 2.4507 | 0.3014 |
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+ | 0.6482 | 71.0 | 639 | 2.4796 | 0.2831 |
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+ | 0.6407 | 72.0 | 648 | 2.4475 | 0.3119 |
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+ | 0.6198 | 73.0 | 657 | 2.5029 | 0.2883 |
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+ | 0.6087 | 74.0 | 666 | 2.4490 | 0.3119 |
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+ | 0.6074 | 75.0 | 675 | 2.4068 | 0.3316 |
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+ | 0.5941 | 76.0 | 684 | 2.4878 | 0.3106 |
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+ | 0.5922 | 77.0 | 693 | 2.5032 | 0.2988 |
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+ | 0.6028 | 78.0 | 702 | 2.4904 | 0.3054 |
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+ | 0.5867 | 79.0 | 711 | 2.4592 | 0.3224 |
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+ | 0.5677 | 80.0 | 720 | 2.5277 | 0.2936 |
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+ | 0.5677 | 81.0 | 729 | 2.4852 | 0.3211 |
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+ | 0.5459 | 82.0 | 738 | 2.5210 | 0.3014 |
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+ | 0.5346 | 83.0 | 747 | 2.5432 | 0.3001 |
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+ | 0.5572 | 84.0 | 756 | 2.4974 | 0.3172 |
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+ | 0.5246 | 85.0 | 765 | 2.5649 | 0.3001 |
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+ | 0.5163 | 86.0 | 774 | 2.5256 | 0.3198 |
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+ | 0.5231 | 87.0 | 783 | 2.5010 | 0.3224 |
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+ | 0.5141 | 88.0 | 792 | 2.5648 | 0.2975 |
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+ | 0.5014 | 89.0 | 801 | 2.5405 | 0.3080 |
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+ | 0.5125 | 90.0 | 810 | 2.5851 | 0.2988 |
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+ | 0.5125 | 91.0 | 819 | 2.5525 | 0.3001 |
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+ | 0.4922 | 92.0 | 828 | 2.5313 | 0.3263 |
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+ | 0.5109 | 93.0 | 837 | 2.5660 | 0.3028 |
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+ | 0.4847 | 94.0 | 846 | 2.5406 | 0.3132 |
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+ | 0.4702 | 95.0 | 855 | 2.5681 | 0.3028 |
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+ | 0.4929 | 96.0 | 864 | 2.5796 | 0.2988 |
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+ | 0.4653 | 97.0 | 873 | 2.5813 | 0.2962 |
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+ | 0.4866 | 98.0 | 882 | 2.5720 | 0.3028 |
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+ | 0.4591 | 99.0 | 891 | 2.5717 | 0.3054 |
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+ | 0.4891 | 100.0 | 900 | 2.5738 | 0.3041 |
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  ### Framework versions
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