generated_from_keras_callback

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whisper_new_split_0075

This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set:

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

Train Loss Train Accuracy Train Wermet Validation Loss Validation Accuracy Validation Wermet Epoch
5.1027 0.0113 52.5530 4.4267 0.0121 41.4796 0
4.3285 0.0126 38.6893 3.9835 0.0145 33.6050 1
3.4573 0.0168 30.7714 2.5568 0.0215 31.7559 2
2.0878 0.0226 20.5131 1.5738 0.0257 21.2159 3
1.3529 0.0258 17.4367 1.1712 0.0276 17.7695 4
0.9953 0.0275 18.7308 0.9389 0.0287 20.5259 5
0.7852 0.0286 18.5731 0.8074 0.0294 17.6576 6
0.6428 0.0293 18.2945 0.7219 0.0298 19.9850 7
0.5384 0.0299 18.9258 0.6610 0.0301 18.9327 8
0.4565 0.0304 19.0749 0.6117 0.0304 21.9796 9
0.3901 0.0308 19.2099 0.5693 0.0306 18.0965 10
0.3348 0.0312 20.4777 0.5449 0.0307 19.9518 11
0.2877 0.0315 20.3181 0.5232 0.0309 20.4017 12
0.2471 0.0318 19.2073 0.5057 0.0310 18.7612 13
0.2120 0.0320 19.0961 0.4925 0.0311 22.3187 14
0.1809 0.0323 20.7944 0.4849 0.0311 27.2314 15
0.1539 0.0325 22.0951 0.4787 0.0312 25.2171 16
0.1299 0.0327 22.7652 0.4733 0.0312 22.7492 17
0.1087 0.0329 25.2223 0.4701 0.0312 28.9044 18
0.0899 0.0330 24.8354 0.4715 0.0313 21.1618 19
0.0739 0.0332 25.4987 0.4680 0.0313 29.6304 20
0.0604 0.0333 27.6465 0.4693 0.0313 27.6937 21
0.0498 0.0333 27.7045 0.4711 0.0313 27.5013 22
0.0414 0.0334 28.0547 0.4689 0.0313 29.1776 23
0.0327 0.0334 27.5594 0.4718 0.0313 31.5623 24
0.0256 0.0335 27.3983 0.4710 0.0313 27.1071 25
0.0210 0.0335 24.7398 0.4736 0.0313 30.8282 26
0.0165 0.0335 25.1927 0.4773 0.0313 24.1750 27
0.0133 0.0335 25.6261 0.4807 0.0313 29.9520 28
0.0110 0.0335 25.8127 0.4825 0.0314 27.0813 29
0.0171 0.0335 26.0445 0.4858 0.0313 39.8503 30
0.0154 0.0335 28.6186 0.4766 0.0314 28.4465 31
0.0094 0.0335 27.8978 0.4778 0.0314 28.7775 32
0.0071 0.0335 27.8180 0.4775 0.0314 28.5229 33
0.0054 0.0335 27.4530 0.4807 0.0315 30.3598 34
0.0043 0.0335 27.2908 0.4833 0.0315 29.9185 35
0.0036 0.0335 27.9772 0.4870 0.0315 29.0761 36
0.0031 0.0335 29.0235 0.4901 0.0315 31.1068 37
0.0027 0.0335 28.2433 0.4930 0.0315 30.2512 38
0.0024 0.0335 33.0830 0.4968 0.0315 35.0547 39
0.0152 0.0334 30.0515 0.4999 0.0314 31.3169 40
0.0095 0.0335 30.0595 0.4917 0.0315 25.2631 41
0.0038 0.0335 23.3205 0.4882 0.0315 22.6513 42
0.0028 0.0335 23.9223 0.4847 0.0315 28.0730 43
0.0023 0.0335 28.1808 0.4919 0.0315 33.1382 44
0.0036 0.0335 32.2064 0.4898 0.0315 27.6573 45
0.0032 0.0335 31.5153 0.4964 0.0315 38.2573 46
0.0025 0.0335 35.6323 0.4925 0.0315 24.0359 47
0.0023 0.0335 30.3651 0.4937 0.0315 29.2069 48
0.0023 0.0335 32.4053 0.4968 0.0315 38.3220 49
0.0020 0.0335 39.4820 0.5002 0.0315 35.0649 50
0.0021 0.0335 33.6737 0.5188 0.0314 32.5078 51
0.0057 0.0335 31.5887 0.5069 0.0315 29.0739 52
0.0053 0.0335 30.3396 0.4958 0.0316 30.4228 53
0.0035 0.0335 30.4281 0.4911 0.0316 30.4131 54
0.0019 0.0335 36.0578 0.4909 0.0316 39.2006 55
0.0013 0.0335 36.1010 0.4927 0.0316 33.9386 56
0.0011 0.0335 30.4648 0.4979 0.0316 35.2314 57
0.0023 0.0335 37.5041 0.4983 0.0316 34.6155 58
0.0017 0.0335 34.4652 0.5023 0.0316 33.7523 59
0.0025 0.0335 38.1887 0.5096 0.0316 30.1588 60
0.0021 0.0335 35.3498 0.5017 0.0316 35.6420 61
0.0016 0.0335 36.5619 0.5123 0.0315 41.2840 62
0.0012 0.0335 35.5301 0.5119 0.0316 34.3617 63
0.0019 0.0335 36.1816 0.5121 0.0316 27.5481 64
0.0017 0.0335 30.6945 0.5029 0.0316 35.9216 65
0.0008 0.0335 33.8546 0.5078 0.0316 33.1414 66
0.0006 0.0335 31.3798 0.5029 0.0317 32.3190 67
0.0019 0.0335 33.7955 0.5385 0.0315 29.8659 68
0.0052 0.0335 38.4663 0.5129 0.0316 39.1985 69
0.0018 0.0335 43.0957 0.5033 0.0317 38.1586 70
0.0007 0.0335 41.8150 0.5006 0.0317 41.7822 71
0.0005 0.0335 35.9141 0.5015 0.0317 34.7363 72
0.0004 0.0335 33.7899 0.5018 0.0317 33.1280 73
0.0003 0.0335 36.5316 0.5019 0.0317 36.4560 74

Framework versions