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Waynehills-STT-doogie-server

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
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update model card README.md

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@@ -13,8 +13,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.9322
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- - Wer: 1.0368
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  ## Model description
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@@ -34,77 +34,77 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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- - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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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_steps: 1000
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- - num_epochs: 30
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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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- | 1.9017 | 0.51 | 100 | 3.9322 | 1.0368 |
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- | 1.9117 | 1.01 | 200 | 3.9322 | 1.0368 |
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- | 1.9099 | 1.52 | 300 | 3.9322 | 1.0368 |
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- | 1.8933 | 2.02 | 400 | 3.9322 | 1.0368 |
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- | 1.8659 | 2.53 | 500 | 3.9322 | 1.0368 |
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- | 1.936 | 3.03 | 600 | 3.9322 | 1.0368 |
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- | 1.8939 | 3.54 | 700 | 3.9322 | 1.0368 |
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- | 1.9037 | 4.04 | 800 | 3.9322 | 1.0368 |
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- | 1.9076 | 4.55 | 900 | 3.9322 | 1.0368 |
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- | 1.9136 | 5.05 | 1000 | 3.9322 | 1.0368 |
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- | 1.8875 | 5.56 | 1100 | 3.9322 | 1.0368 |
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- | 1.9003 | 6.06 | 1200 | 3.9322 | 1.0368 |
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- | 1.9138 | 6.57 | 1300 | 3.9322 | 1.0368 |
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- | 1.8942 | 7.07 | 1400 | 3.9322 | 1.0368 |
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- | 1.9035 | 7.58 | 1500 | 3.9322 | 1.0368 |
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- | 1.9076 | 8.08 | 1600 | 3.9322 | 1.0368 |
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- | 1.8997 | 8.59 | 1700 | 3.9322 | 1.0368 |
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- | 1.8958 | 9.09 | 1800 | 3.9322 | 1.0368 |
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- | 1.891 | 9.6 | 1900 | 3.9322 | 1.0368 |
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- | 1.9245 | 10.1 | 2000 | 3.9322 | 1.0368 |
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- | 1.9042 | 10.61 | 2100 | 3.9322 | 1.0368 |
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- | 1.9153 | 11.11 | 2200 | 3.9322 | 1.0368 |
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- | 1.892 | 11.62 | 2300 | 3.9322 | 1.0368 |
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- | 1.8937 | 12.12 | 2400 | 3.9322 | 1.0368 |
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- | 1.9036 | 12.63 | 2500 | 3.9322 | 1.0368 |
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- | 1.9162 | 13.13 | 2600 | 3.9322 | 1.0368 |
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- | 1.9014 | 13.64 | 2700 | 3.9322 | 1.0368 |
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- | 1.9083 | 14.14 | 2800 | 3.9322 | 1.0368 |
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- | 1.9003 | 14.65 | 2900 | 3.9322 | 1.0368 |
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- | 1.9015 | 15.15 | 3000 | 3.9322 | 1.0368 |
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- | 1.8851 | 15.66 | 3100 | 3.9322 | 1.0368 |
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- | 1.9062 | 16.16 | 3200 | 3.9322 | 1.0368 |
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- | 1.9279 | 16.67 | 3300 | 3.9322 | 1.0368 |
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- | 1.8795 | 17.17 | 3400 | 3.9322 | 1.0368 |
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- | 1.9126 | 17.68 | 3500 | 3.9322 | 1.0368 |
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- | 1.8688 | 18.18 | 3600 | 3.9322 | 1.0368 |
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- | 1.9234 | 18.69 | 3700 | 3.9322 | 1.0368 |
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- | 1.8872 | 19.19 | 3800 | 3.9322 | 1.0368 |
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- | 1.9096 | 19.7 | 3900 | 3.9322 | 1.0368 |
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- | 1.8854 | 20.2 | 4000 | 3.9322 | 1.0368 |
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- | 1.9168 | 20.71 | 4100 | 3.9322 | 1.0368 |
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- | 1.9145 | 21.21 | 4200 | 3.9322 | 1.0368 |
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- | 1.904 | 21.72 | 4300 | 3.9322 | 1.0368 |
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- | 1.8982 | 22.22 | 4400 | 3.9322 | 1.0368 |
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- | 1.8978 | 22.73 | 4500 | 3.9322 | 1.0368 |
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- | 1.9023 | 23.23 | 4600 | 3.9322 | 1.0368 |
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- | 1.8901 | 23.74 | 4700 | 3.9322 | 1.0368 |
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- | 1.9079 | 24.24 | 4800 | 3.9322 | 1.0368 |
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- | 1.8923 | 24.75 | 4900 | 3.9322 | 1.0368 |
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- | 1.9095 | 25.25 | 5000 | 3.9322 | 1.0368 |
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- | 1.909 | 25.76 | 5100 | 3.9322 | 1.0368 |
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- | 1.8871 | 26.26 | 5200 | 3.9322 | 1.0368 |
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- | 1.9046 | 26.77 | 5300 | 3.9322 | 1.0368 |
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- | 1.8877 | 27.27 | 5400 | 3.9322 | 1.0368 |
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- | 1.901 | 27.78 | 5500 | 3.9322 | 1.0368 |
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- | 1.9045 | 28.28 | 5600 | 3.9322 | 1.0368 |
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- | 1.907 | 28.79 | 5700 | 3.9322 | 1.0368 |
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- | 1.9075 | 29.29 | 5800 | 3.9322 | 1.0368 |
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- | 1.895 | 29.8 | 5900 | 3.9322 | 1.0368 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7114
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+ - Wer: 1.0056
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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+ - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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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_steps: 1000
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+ - num_epochs: 60
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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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+ | 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