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update model card README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  name: Waynehills-STT-doogie-server

Waynehills-STT-doogie-server

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7114
  • Wer: 1.0056

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:

  • learning_rate: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 60

Training results

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

Framework versions

  • Transformers 4.12.5
  • Pytorch 1.10.0+cu113
  • Datasets 1.17.0
  • Tokenizers 0.10.3