End of training
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README.md
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metrics:
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- name: Wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the NG_word_detect dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer:
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer
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### Framework versions
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metrics:
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- name: Wer
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type: wer
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+
value: 40.960240060015
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---
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| 28 |
|
| 29 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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|
|
|
| 33 |
|
| 34 |
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the NG_word_detect dataset.
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It achieves the following results on the evaluation set:
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+
- Loss: 0.2154
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+
- Wer: 40.9602
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## Model description
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### Training hyperparameters
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| 54 |
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The following hyperparameters were used during training:
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- learning_rate: 5e-06
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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### Training results
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+
| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|
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| 0.3435 | 0.1524 | 25 | 0.3604 | 69.1673 |
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| 0.2776 | 0.3049 | 50 | 0.2718 | 63.0158 |
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| 0.2031 | 0.4573 | 75 | 0.2177 | 55.4389 |
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| 0.171 | 0.6098 | 100 | 0.1879 | 52.3631 |
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| 0.1363 | 0.7622 | 125 | 0.1721 | 49.8875 |
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| 0.1587 | 0.9146 | 150 | 0.1654 | 48.9872 |
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| 0.0824 | 1.0671 | 175 | 0.1641 | 47.2618 |
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| 0.0933 | 1.2195 | 200 | 0.1659 | 48.3121 |
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| 0.1426 | 1.3720 | 225 | 0.1572 | 46.5116 |
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| 0.1059 | 1.5244 | 250 | 0.1528 | 45.6864 |
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| 0.095 | 1.6768 | 275 | 0.1540 | 46.0615 |
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| 0.0855 | 1.8293 | 300 | 0.1528 | 44.1110 |
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| 0.1124 | 1.9817 | 325 | 0.1525 | 45.1613 |
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| 0.052 | 2.1341 | 350 | 0.1559 | 45.5364 |
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| 0.0539 | 2.2866 | 375 | 0.1575 | 45.0863 |
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| 0.0718 | 2.4390 | 400 | 0.1667 | 45.1613 |
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| 0.0451 | 2.5915 | 425 | 0.1701 | 46.0615 |
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| 0.0421 | 2.7439 | 450 | 0.1582 | 44.4861 |
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| 0.0508 | 2.8963 | 475 | 0.1604 | 44.4111 |
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| 0.0204 | 3.0488 | 500 | 0.1601 | 42.7607 |
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| 0.0257 | 3.2012 | 525 | 0.1744 | 43.9610 |
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| 0.0175 | 3.3537 | 550 | 0.1728 | 45.7614 |
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| 0.0219 | 3.5061 | 575 | 0.1766 | 45.2363 |
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| 0.0216 | 3.6585 | 600 | 0.1800 | 45.9115 |
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| 0.0173 | 3.8110 | 625 | 0.1692 | 44.5611 |
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| 0.0418 | 3.9634 | 650 | 0.1672 | 43.7359 |
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| 0.0076 | 4.1159 | 675 | 0.1777 | 43.6609 |
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| 0.0088 | 4.2683 | 700 | 0.1805 | 42.4606 |
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| 0.0097 | 4.4207 | 725 | 0.1774 | 43.0608 |
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| 0.0097 | 4.5732 | 750 | 0.1802 | 44.7112 |
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| 0.0117 | 4.7256 | 775 | 0.1783 | 43.5859 |
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| 0.0101 | 4.8780 | 800 | 0.1851 | 42.9107 |
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| 0.0069 | 5.0305 | 825 | 0.1807 | 41.9355 |
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| 0.006 | 5.1829 | 850 | 0.1865 | 42.2356 |
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| 0.0029 | 5.3354 | 875 | 0.1878 | 42.6107 |
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| 0.0079 | 5.4878 | 900 | 0.1994 | 44.1110 |
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| 0.0118 | 5.6402 | 925 | 0.1889 | 43.9610 |
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| 0.0125 | 5.7927 | 950 | 0.1905 | 44.6362 |
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| 0.0115 | 5.9451 | 975 | 0.1846 | 44.0360 |
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| 0.0054 | 6.0976 | 1000 | 0.1845 | 43.8110 |
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| 0.0036 | 6.25 | 1025 | 0.1922 | 42.7607 |
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| 0.0088 | 6.4024 | 1050 | 0.1937 | 42.8357 |
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| 0.0043 | 6.5549 | 1075 | 0.1914 | 42.9107 |
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| 0.0016 | 6.7073 | 1100 | 0.1958 | 42.6107 |
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| 0.0103 | 6.8598 | 1125 | 0.1877 | 41.6354 |
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| 0.0027 | 7.0122 | 1150 | 0.1873 | 41.7104 |
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| 0.0018 | 7.1646 | 1175 | 0.1890 | 41.7854 |
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| 0.0012 | 7.3171 | 1200 | 0.1918 | 41.7104 |
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| 0.0054 | 7.4695 | 1225 | 0.1949 | 41.0353 |
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| 0.0014 | 7.6220 | 1250 | 0.1965 | 41.6354 |
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| 0.0009 | 7.7744 | 1275 | 0.2024 | 41.7104 |
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| 0.0011 | 7.9268 | 1300 | 0.1970 | 41.1853 |
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| 0.0007 | 8.0793 | 1325 | 0.1995 | 41.1103 |
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| 0.0006 | 8.2317 | 1350 | 0.2012 | 41.4854 |
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| 0.0006 | 8.3841 | 1375 | 0.2075 | 41.7854 |
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| 0.0006 | 8.5366 | 1400 | 0.2077 | 41.5604 |
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| 0.0034 | 8.6890 | 1425 | 0.2092 | 41.7854 |
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| 0.0006 | 8.8415 | 1450 | 0.2079 | 41.2603 |
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| 0.0023 | 8.9939 | 1475 | 0.2080 | 41.0353 |
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| 0.0004 | 9.1463 | 1500 | 0.2095 | 41.0353 |
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| 0.0021 | 9.2988 | 1525 | 0.2096 | 41.4854 |
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| 0.0004 | 9.4512 | 1550 | 0.2095 | 41.3353 |
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| 0.0015 | 9.6037 | 1575 | 0.2102 | 41.0353 |
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| 0.0012 | 9.7561 | 1600 | 0.2106 | 41.1853 |
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| 0.0006 | 9.9085 | 1625 | 0.2110 | 41.2603 |
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| 0.0004 | 10.0610 | 1650 | 0.2111 | 41.1103 |
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| 0.0003 | 10.2134 | 1675 | 0.2122 | 41.1853 |
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| 0.0003 | 10.3659 | 1700 | 0.2122 | 40.9602 |
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| 0.0006 | 10.5183 | 1725 | 0.2125 | 40.8102 |
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| 0.0004 | 10.6707 | 1750 | 0.2131 | 40.8852 |
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| 0.0004 | 10.8232 | 1775 | 0.2137 | 41.0353 |
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| 0.0003 | 10.9756 | 1800 | 0.2141 | 40.9602 |
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| 0.0003 | 11.1280 | 1825 | 0.2144 | 40.9602 |
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| 0.0003 | 11.2805 | 1850 | 0.2147 | 40.9602 |
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| 0.0021 | 11.4329 | 1875 | 0.2149 | 40.9602 |
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| 0.0011 | 11.5854 | 1900 | 0.2152 | 40.9602 |
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| 0.0003 | 11.7378 | 1925 | 0.2153 | 40.9602 |
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| 0.0003 | 11.8902 | 1950 | 0.2153 | 40.9602 |
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| 0.0024 | 12.0427 | 1975 | 0.2153 | 40.9602 |
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| 0.0003 | 12.1951 | 2000 | 0.2154 | 40.9602 |
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### Framework versions
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