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Waynehills-STT-doogie-server

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
Doogie commited on
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09ea684
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update model card README.md

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@@ -46,65 +46,65 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 92.1483 | 1.01 | 100 | 42.2435 | 1.0 |
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- | 63.5438 | 2.02 | 200 | 30.3017 | 1.0 |
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- | 40.1961 | 3.03 | 300 | 16.5376 | 1.0 |
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- | 17.0478 | 4.04 | 400 | 6.9455 | 1.0 |
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- | 6.3103 | 5.05 | 500 | 4.9786 | 1.0 |
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- | 4.8201 | 6.06 | 600 | 4.8737 | 1.0 |
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- | 4.6806 | 7.07 | 700 | 4.8619 | 1.0 |
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- | 4.6275 | 8.08 | 800 | 4.8368 | 1.0 |
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- | 4.6017 | 9.09 | 900 | 4.8350 | 1.0 |
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- | 4.5535 | 10.1 | 1000 | 4.8050 | 1.0 |
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- | 4.4901 | 11.11 | 1100 | 4.6832 | 1.0 |
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- | 3.9919 | 12.12 | 1200 | 3.8253 | 1.0 |
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- | 2.7548 | 13.13 | 1300 | 2.9664 | 1.1167 |
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- | 2.0868 | 14.14 | 1400 | 2.6503 | 1.1778 |
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- | 1.6825 | 15.15 | 1500 | 2.4266 | 1.0278 |
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- | 1.3986 | 16.16 | 1600 | 2.2006 | 1.0944 |
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- | 1.1716 | 17.17 | 1700 | 2.1571 | 1.0389 |
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- | 0.9922 | 18.18 | 1800 | 2.0690 | 1.0333 |
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- | 0.8654 | 19.19 | 1900 | 1.9874 | 1.0222 |
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- | 0.7447 | 20.2 | 2000 | 1.9131 | 1.0278 |
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- | 0.6263 | 21.21 | 2100 | 1.8423 | 1.0111 |
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- | 0.5429 | 22.22 | 2200 | 1.7591 | 1.0111 |
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- | 0.4715 | 23.23 | 2300 | 1.7380 | 1.0 |
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- | 0.4245 | 24.24 | 2400 | 1.7531 | 1.0111 |
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- | 0.3971 | 25.25 | 2500 | 1.8016 | 1.0 |
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- | 0.3495 | 26.26 | 2600 | 1.6868 | 0.9944 |
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- | 0.317 | 27.27 | 2700 | 1.8013 | 1.0 |
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- | 0.2886 | 28.28 | 2800 | 1.6985 | 1.0056 |
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- | 0.2597 | 29.29 | 2900 | 1.6931 | 1.0056 |
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- | 0.2269 | 30.3 | 3000 | 1.6208 | 1.0111 |
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- | 0.2175 | 31.31 | 3100 | 1.6451 | 1.0 |
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- | 0.2101 | 32.32 | 3200 | 1.7385 | 1.0056 |
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- | 0.2033 | 33.33 | 3300 | 1.6909 | 1.0111 |
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- | 0.184 | 34.34 | 3400 | 1.7458 | 1.0 |
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- | 0.1812 | 35.35 | 3500 | 1.7152 | 1.0 |
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- | 0.1582 | 36.36 | 3600 | 1.7101 | 1.0 |
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- | 0.1556 | 37.37 | 3700 | 1.6729 | 1.0056 |
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- | 0.1398 | 38.38 | 3800 | 1.6982 | 0.9944 |
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- | 0.1357 | 39.39 | 3900 | 1.6891 | 1.0167 |
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- | 0.1261 | 40.4 | 4000 | 1.6817 | 1.0 |
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- | 0.1226 | 41.41 | 4100 | 1.7411 | 1.0 |
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- | 0.1217 | 42.42 | 4200 | 1.7909 | 1.0 |
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- | 0.115 | 43.43 | 4300 | 1.6764 | 1.0 |
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- | 0.1127 | 44.44 | 4400 | 1.6728 | 0.9944 |
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- | 0.104 | 45.45 | 4500 | 1.7181 | 1.0056 |
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- | 0.105 | 46.46 | 4600 | 1.7491 | 1.0 |
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- | 0.0925 | 47.47 | 4700 | 1.7661 | 1.0056 |
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- | 0.0942 | 48.48 | 4800 | 1.7376 | 1.0 |
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- | 0.0943 | 49.49 | 4900 | 1.6908 | 1.0111 |
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- | 0.0859 | 50.51 | 5000 | 1.7193 | 1.0111 |
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- | 0.0845 | 51.52 | 5100 | 1.7051 | 1.0 |
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- | 0.0825 | 52.53 | 5200 | 1.6917 | 1.0 |
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- | 0.08 | 53.54 | 5300 | 1.7093 | 1.0111 |
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- | 0.0749 | 54.55 | 5400 | 1.7160 | 1.0111 |
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- | 0.0731 | 55.56 | 5500 | 1.7215 | 1.0111 |
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- | 0.0725 | 56.57 | 5600 | 1.7251 | 1.0056 |
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- | 0.0704 | 57.58 | 5700 | 1.7275 | 1.0056 |
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- | 0.0734 | 58.59 | 5800 | 1.7119 | 1.0056 |
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- | 0.0729 | 59.6 | 5900 | 1.7114 | 1.0056 |
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  ### Framework versions
 
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.0734 | 1.01 | 100 | 1.7114 | 1.0056 |
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+ | 0.074 | 2.02 | 200 | 1.7114 | 1.0056 |
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+ | 0.0707 | 3.03 | 300 | 1.7114 | 1.0056 |
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+ | 0.0727 | 4.04 | 400 | 1.7114 | 1.0056 |
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+ | 0.076 | 5.05 | 500 | 1.7114 | 1.0056 |
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+ | 0.0713 | 6.06 | 600 | 1.7114 | 1.0056 |
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+ | 0.0709 | 7.07 | 700 | 1.7114 | 1.0056 |
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+ | 0.0721 | 8.08 | 800 | 1.7114 | 1.0056 |
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+ | 0.0761 | 9.09 | 900 | 1.7114 | 1.0056 |
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+ | 0.0724 | 10.1 | 1000 | 1.7114 | 1.0056 |
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+ | 0.0749 | 11.11 | 1100 | 1.7114 | 1.0056 |
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+ | 0.0746 | 12.12 | 1200 | 1.7114 | 1.0056 |
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+ | 0.0756 | 13.13 | 1300 | 1.7114 | 1.0056 |
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+ | 0.0712 | 14.14 | 1400 | 1.7114 | 1.0056 |
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+ | 0.0704 | 15.15 | 1500 | 1.7114 | 1.0056 |
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+ | 0.0698 | 16.16 | 1600 | 1.7114 | 1.0056 |
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+ | 0.0715 | 17.17 | 1700 | 1.7114 | 1.0056 |
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+ | 0.0743 | 18.18 | 1800 | 1.7114 | 1.0056 |
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+ | 0.0743 | 19.19 | 1900 | 1.7114 | 1.0056 |
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+ | 0.0745 | 20.2 | 2000 | 1.7114 | 1.0056 |
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+ | 0.0702 | 21.21 | 2100 | 1.7114 | 1.0056 |
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+ | 0.0717 | 22.22 | 2200 | 1.7114 | 1.0056 |
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+ | 0.0673 | 23.23 | 2300 | 1.7114 | 1.0056 |
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+ | 0.0701 | 24.24 | 2400 | 1.7114 | 1.0056 |
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+ | 0.0754 | 25.25 | 2500 | 1.7114 | 1.0056 |
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+ | 0.0677 | 26.26 | 2600 | 1.7114 | 1.0056 |
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+ | 0.0751 | 27.27 | 2700 | 1.7114 | 1.0056 |
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+ | 0.0828 | 28.28 | 2800 | 1.7114 | 1.0056 |
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+ | 0.0714 | 29.29 | 2900 | 1.7114 | 1.0056 |
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+ | 0.0735 | 30.3 | 3000 | 1.7114 | 1.0056 |
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+ | 0.0724 | 31.31 | 3100 | 1.7114 | 1.0056 |
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+ | 0.0777 | 32.32 | 3200 | 1.7114 | 1.0056 |
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+ | 0.0747 | 33.33 | 3300 | 1.7114 | 1.0056 |
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+ | 0.0724 | 34.34 | 3400 | 1.7114 | 1.0056 |
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+ | 0.0717 | 35.35 | 3500 | 1.7114 | 1.0056 |
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+ | 0.0723 | 36.36 | 3600 | 1.7114 | 1.0056 |
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+ | 0.0797 | 37.37 | 3700 | 1.7114 | 1.0056 |
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+ | 0.0693 | 38.38 | 3800 | 1.7114 | 1.0056 |
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+ | 0.0748 | 39.39 | 3900 | 1.7114 | 1.0056 |
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+ | 0.0739 | 40.4 | 4000 | 1.7114 | 1.0056 |
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+ | 0.0701 | 41.41 | 4100 | 1.7114 | 1.0056 |
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+ | 0.079 | 42.42 | 4200 | 1.7114 | 1.0056 |
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+ | 0.0753 | 43.43 | 4300 | 1.7114 | 1.0056 |
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+ | 0.0707 | 44.44 | 4400 | 1.7114 | 1.0056 |
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+ | 0.0724 | 45.45 | 4500 | 1.7114 | 1.0056 |
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+ | 0.0667 | 46.46 | 4600 | 1.7114 | 1.0056 |
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+ | 0.077 | 47.47 | 4700 | 1.7114 | 1.0056 |
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+ | 0.0716 | 48.48 | 4800 | 1.7114 | 1.0056 |
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+ | 0.0731 | 49.49 | 4900 | 1.7114 | 1.0056 |
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+ | 0.0741 | 50.51 | 5000 | 1.7114 | 1.0056 |
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+ | 0.0705 | 51.52 | 5100 | 1.7114 | 1.0056 |
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+ | 0.0736 | 52.53 | 5200 | 1.7114 | 1.0056 |
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+ | 0.0741 | 53.54 | 5300 | 1.7114 | 1.0056 |
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+ | 0.0721 | 54.55 | 5400 | 1.7114 | 1.0056 |
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+ | 0.074 | 55.56 | 5500 | 1.7114 | 1.0056 |
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+ | 0.071 | 56.57 | 5600 | 1.7114 | 1.0056 |
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+ | 0.0723 | 57.58 | 5700 | 1.7114 | 1.0056 |
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+ | 0.0725 | 58.59 | 5800 | 1.7114 | 1.0056 |
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+ | 0.0746 | 59.6 | 5900 | 1.7114 | 1.0056 |
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  ### Framework versions