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Waynehillsdev
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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
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Doogie commited on
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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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- | 1.8987 | 0.51 | 100 | 3.9322 | 1.0368 |
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- | 1.9171 | 1.01 | 200 | 3.9322 | 1.0368 |
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- | 1.9058 | 1.52 | 300 | 3.9322 | 1.0368 |
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- | 1.9037 | 2.02 | 400 | 3.9322 | 1.0368 |
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- | 1.9079 | 2.53 | 500 | 3.9322 | 1.0368 |
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- | 1.8788 | 3.03 | 600 | 3.9322 | 1.0368 |
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- | 1.8973 | 3.54 | 700 | 3.9322 | 1.0368 |
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- | 1.9031 | 4.04 | 800 | 3.9322 | 1.0368 |
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- | 1.8966 | 4.55 | 900 | 3.9322 | 1.0368 |
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- | 1.9092 | 5.05 | 1000 | 3.9322 | 1.0368 |
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- | 1.9158 | 5.56 | 1100 | 3.9322 | 1.0368 |
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- | 1.89 | 6.06 | 1200 | 3.9322 | 1.0368 |
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- | 1.916 | 6.57 | 1300 | 3.9322 | 1.0368 |
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- | 1.8684 | 7.07 | 1400 | 3.9322 | 1.0368 |
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- | 1.8885 | 7.58 | 1500 | 3.9322 | 1.0368 |
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- | 1.9335 | 8.08 | 1600 | 3.9322 | 1.0368 |
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- | 1.9112 | 8.59 | 1700 | 3.9322 | 1.0368 |
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- | 1.8794 | 9.09 | 1800 | 3.9322 | 1.0368 |
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- | 1.9062 | 9.6 | 1900 | 3.9322 | 1.0368 |
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- | 1.9048 | 10.1 | 2000 | 3.9322 | 1.0368 |
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- | 1.917 | 10.61 | 2100 | 3.9322 | 1.0368 |
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- | 1.8809 | 11.11 | 2200 | 3.9322 | 1.0368 |
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- | 1.9101 | 11.62 | 2300 | 3.9322 | 1.0368 |
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- | 1.8867 | 12.12 | 2400 | 3.9322 | 1.0368 |
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- | 1.9188 | 12.63 | 2500 | 3.9322 | 1.0368 |
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- | 1.8933 | 13.13 | 2600 | 3.9322 | 1.0368 |
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- | 1.8846 | 13.64 | 2700 | 3.9322 | 1.0368 |
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- | 1.9327 | 14.14 | 2800 | 3.9322 | 1.0368 |
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- | 1.9041 | 14.65 | 2900 | 3.9322 | 1.0368 |
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- | 1.8733 | 15.15 | 3000 | 3.9322 | 1.0368 |
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- | 1.9246 | 15.66 | 3100 | 3.9322 | 1.0368 |
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- | 1.8925 | 16.16 | 3200 | 3.9322 | 1.0368 |
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- | 1.9066 | 16.67 | 3300 | 3.9322 | 1.0368 |
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- | 1.8991 | 17.17 | 3400 | 3.9322 | 1.0368 |
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- | 1.899 | 17.68 | 3500 | 3.9322 | 1.0368 |
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- | 1.9003 | 18.18 | 3600 | 3.9322 | 1.0368 |
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- | 1.9131 | 18.69 | 3700 | 3.9322 | 1.0368 |
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- | 1.9141 | 19.19 | 3800 | 3.9322 | 1.0368 |
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- | 1.9074 | 19.7 | 3900 | 3.9322 | 1.0368 |
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- | 1.9308 | 20.2 | 4000 | 3.9322 | 1.0368 |
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- | 1.876 | 20.71 | 4100 | 3.9322 | 1.0368 |
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- | 1.9263 | 21.21 | 4200 | 3.9322 | 1.0368 |
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- | 1.8956 | 21.72 | 4300 | 3.9322 | 1.0368 |
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- | 1.9114 | 22.22 | 4400 | 3.9322 | 1.0368 |
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- | 1.9189 | 22.73 | 4500 | 3.9322 | 1.0368 |
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- | 1.889 | 23.23 | 4600 | 3.9322 | 1.0368 |
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- | 1.9065 | 23.74 | 4700 | 3.9322 | 1.0368 |
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- | 1.9151 | 24.24 | 4800 | 3.9322 | 1.0368 |
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- | 1.9059 | 24.75 | 4900 | 3.9322 | 1.0368 |
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- | 1.8875 | 25.25 | 5000 | 3.9322 | 1.0368 |
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- | 1.9123 | 25.76 | 5100 | 3.9322 | 1.0368 |
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- | 1.9008 | 26.26 | 5200 | 3.9322 | 1.0368 |
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- | 1.9128 | 26.77 | 5300 | 3.9322 | 1.0368 |
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- | 1.9026 | 27.27 | 5400 | 3.9322 | 1.0368 |
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- | 1.8901 | 27.78 | 5500 | 3.9322 | 1.0368 |
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- | 1.9108 | 28.28 | 5600 | 3.9322 | 1.0368 |
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- | 1.9004 | 28.79 | 5700 | 3.9322 | 1.0368 |
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- | 1.9199 | 29.29 | 5800 | 3.9322 | 1.0368 |
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- | 1.8783 | 29.8 | 5900 | 3.9322 | 1.0368 |
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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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+ | 1.8925 | 0.51 | 100 | 3.9322 | 1.0368 |
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+ | 1.9361 | 1.01 | 200 | 3.9322 | 1.0368 |
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+ | 1.9349 | 1.52 | 300 | 3.9322 | 1.0368 |
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+ | 1.9047 | 2.02 | 400 | 3.9322 | 1.0368 |
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+ | 1.8903 | 2.53 | 500 | 3.9322 | 1.0368 |
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+ | 1.9246 | 3.03 | 600 | 3.9322 | 1.0368 |
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+ | 1.9243 | 3.54 | 700 | 3.9322 | 1.0368 |
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+ | 1.9048 | 4.04 | 800 | 3.9322 | 1.0368 |
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+ | 1.9095 | 4.55 | 900 | 3.9322 | 1.0368 |
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+ | 1.8892 | 5.05 | 1000 | 3.9322 | 1.0368 |
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+ | 1.8724 | 5.56 | 1100 | 3.9322 | 1.0368 |
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+ | 1.9132 | 6.06 | 1200 | 3.9322 | 1.0368 |
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+ | 1.901 | 6.57 | 1300 | 3.9322 | 1.0368 |
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+ | 1.9005 | 7.07 | 1400 | 3.9322 | 1.0368 |
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+ | 1.8921 | 7.58 | 1500 | 3.9322 | 1.0368 |
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+ | 1.9159 | 8.08 | 1600 | 3.9322 | 1.0368 |
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+ | 1.8817 | 8.59 | 1700 | 3.9322 | 1.0368 |
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+ | 1.9161 | 9.09 | 1800 | 3.9322 | 1.0368 |
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+ | 1.9013 | 9.6 | 1900 | 3.9322 | 1.0368 |
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+ | 1.8953 | 10.1 | 2000 | 3.9322 | 1.0368 |
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+ | 1.9131 | 10.61 | 2100 | 3.9322 | 1.0368 |
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+ | 1.8756 | 11.11 | 2200 | 3.9322 | 1.0368 |
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+ | 1.91 | 11.62 | 2300 | 3.9322 | 1.0368 |
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+ | 1.9115 | 12.12 | 2400 | 3.9322 | 1.0368 |
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+ | 1.9179 | 12.63 | 2500 | 3.9322 | 1.0368 |
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+ | 1.8858 | 13.13 | 2600 | 3.9322 | 1.0368 |
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+ | 1.9098 | 13.64 | 2700 | 3.9322 | 1.0368 |
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+ | 1.8918 | 14.14 | 2800 | 3.9322 | 1.0368 |
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+ | 1.9174 | 14.65 | 2900 | 3.9322 | 1.0368 |
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+ | 1.8735 | 15.15 | 3000 | 3.9322 | 1.0368 |
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+ | 1.898 | 15.66 | 3100 | 3.9322 | 1.0368 |
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+ | 1.8958 | 16.16 | 3200 | 3.9322 | 1.0368 |
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+ | 1.8824 | 16.67 | 3300 | 3.9322 | 1.0368 |
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+ | 1.9061 | 17.17 | 3400 | 3.9322 | 1.0368 |
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+ | 1.8963 | 17.68 | 3500 | 3.9322 | 1.0368 |
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+ | 1.9476 | 18.18 | 3600 | 3.9322 | 1.0368 |
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+ | 1.9137 | 18.69 | 3700 | 3.9322 | 1.0368 |
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+ | 1.8795 | 19.19 | 3800 | 3.9322 | 1.0368 |
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+ | 1.8922 | 19.7 | 3900 | 3.9322 | 1.0368 |
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+ | 1.9307 | 20.2 | 4000 | 3.9322 | 1.0368 |
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+ | 1.9179 | 20.71 | 4100 | 3.9322 | 1.0368 |
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+ | 1.8951 | 21.21 | 4200 | 3.9322 | 1.0368 |
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+ | 1.9096 | 21.72 | 4300 | 3.9322 | 1.0368 |
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+ | 1.9104 | 22.22 | 4400 | 3.9322 | 1.0368 |
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+ | 1.8902 | 22.73 | 4500 | 3.9322 | 1.0368 |
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+ | 1.9276 | 23.23 | 4600 | 3.9322 | 1.0368 |
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+ | 1.9154 | 23.74 | 4700 | 3.9322 | 1.0368 |
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+ | 1.9003 | 24.24 | 4800 | 3.9322 | 1.0368 |
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+ | 1.9029 | 24.75 | 4900 | 3.9322 | 1.0368 |
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+ | 1.8991 | 25.25 | 5000 | 3.9322 | 1.0368 |
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+ | 1.9173 | 25.76 | 5100 | 3.9322 | 1.0368 |
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+ | 1.8857 | 26.26 | 5200 | 3.9322 | 1.0368 |
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+ | 1.9122 | 26.77 | 5300 | 3.9322 | 1.0368 |
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+ | 1.9032 | 27.27 | 5400 | 3.9322 | 1.0368 |
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+ | 1.8865 | 27.78 | 5500 | 3.9322 | 1.0368 |
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+ | 1.8944 | 28.28 | 5600 | 3.9322 | 1.0368 |
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+ | 1.9086 | 28.79 | 5700 | 3.9322 | 1.0368 |
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+ | 1.9048 | 29.29 | 5800 | 3.9322 | 1.0368 |
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+ | 1.9152 | 29.8 | 5900 | 3.9322 | 1.0368 |
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  ### Framework versions