generated_from_keras_callback

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whisper_syl_noforce__0060

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
5.2961 0.0113 1.9043 3.9402 0.0116 0.9526 0
4.6207 0.0121 0.8740 3.7957 0.0120 0.9397 1
4.4142 0.0128 0.8473 3.6045 0.0124 0.8988 2
4.1915 0.0135 0.8361 3.4445 0.0128 0.9019 3
4.0072 0.0140 0.8260 3.3268 0.0131 0.8816 4
3.8559 0.0145 0.8084 3.2440 0.0133 0.8592 5
3.7359 0.0149 0.7986 3.1751 0.0135 0.8598 6
3.6368 0.0152 0.7891 3.1298 0.0136 0.8398 7
3.5465 0.0154 0.7775 3.0736 0.0138 0.8606 8
3.4710 0.0157 0.7681 3.0318 0.0138 0.8455 9
3.3988 0.0159 0.7603 3.0159 0.0139 0.8770 10
3.3279 0.0162 0.7504 2.9672 0.0141 0.8241 11
3.2611 0.0164 0.7397 2.9541 0.0141 0.8676 12
3.1996 0.0167 0.7284 2.8913 0.0144 0.7990 13
3.1311 0.0169 0.7162 2.8671 0.0145 0.7934 14
3.0590 0.0172 0.7044 2.8241 0.0146 0.7907 15
2.9692 0.0177 0.6843 2.7517 0.0149 0.7645 16
2.8783 0.0181 0.6630 2.6682 0.0152 0.7263 17
2.7622 0.0187 0.6417 2.5586 0.0156 0.7220 18
2.6164 0.0194 0.6138 2.4121 0.0161 0.6909 19
2.4405 0.0203 0.5838 2.2417 0.0167 0.6527 20
2.2404 0.0213 0.5486 2.1401 0.0170 0.6662 21
2.0196 0.0225 0.5086 1.8907 0.0180 0.5774 22
1.7917 0.0237 0.4665 1.7073 0.0186 0.5446 23
1.5286 0.0253 0.4182 1.5139 0.0194 0.4919 24
1.2991 0.0267 0.3736 1.3605 0.0200 0.4570 25
1.1117 0.0279 0.3336 1.2304 0.0205 0.4262 26
0.9643 0.0289 0.2986 1.1387 0.0209 0.4040 27
0.8404 0.0298 0.2663 1.0514 0.0213 0.3776 28
0.7408 0.0305 0.2408 0.9883 0.0216 0.3596 29
0.6542 0.0311 0.2155 0.9281 0.0218 0.3418 30
0.5800 0.0316 0.1936 0.8801 0.0221 0.3269 31
0.5168 0.0321 0.1737 0.8401 0.0222 0.3168 32
0.4595 0.0326 0.1552 0.8071 0.0224 0.3077 33
0.4080 0.0330 0.1375 0.7825 0.0225 0.2994 34
0.3646 0.0333 0.1225 0.7550 0.0226 0.2887 35
0.3234 0.0337 0.1095 0.7369 0.0227 0.2847 36
0.2878 0.0340 0.0950 0.7270 0.0228 0.2796 37
0.2542 0.0343 0.0823 0.7096 0.0229 0.2728 38
0.2238 0.0346 0.0718 0.6963 0.0229 0.2697 39
0.1974 0.0348 0.0609 0.6857 0.0230 0.2669 40
0.1714 0.0351 0.0500 0.6843 0.0230 0.2663 41
0.1488 0.0353 0.0411 0.6770 0.0230 0.2630 42
0.1296 0.0355 0.0339 0.6754 0.0231 0.2612 43
0.1117 0.0356 0.0270 0.6702 0.0231 0.2585 44
0.0954 0.0358 0.0211 0.6695 0.0231 0.2574 45
0.0822 0.0359 0.0163 0.6711 0.0231 0.2572 46
0.0715 0.0360 0.0137 0.6685 0.0231 0.2583 47
0.0591 0.0361 0.0093 0.6696 0.0231 0.2590 48
0.0494 0.0361 0.0068 0.6663 0.0232 0.2609 49
0.0412 0.0362 0.0051 0.6726 0.0231 0.2577 50
0.0343 0.0362 0.0042 0.6756 0.0232 0.2609 51
0.0287 0.0362 0.0031 0.6700 0.0232 0.2549 52
0.0245 0.0362 0.0035 0.6796 0.0232 0.2639 53
0.0297 0.0362 0.0054 0.6695 0.0232 0.2557 54
0.0249 0.0362 0.0039 0.6700 0.0232 0.2554 55
0.0177 0.0362 0.0026 0.6673 0.0233 0.2504 56
0.0138 0.0362 0.0023 0.6763 0.0232 0.2526 57
0.0114 0.0362 0.0020 0.6770 0.0232 0.2509 58
0.0098 0.0362 0.0020 0.6751 0.0233 0.2494 59

Framework versions