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Model_G_ALL_Wav2Vec2

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Model_G_ALL_Wav2Vec2 / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: Model_G_ALL_Wav2Vec2
    results: []

Model_G_ALL_Wav2Vec2

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7956
  • Wer: 0.1972
  • Cer: 0.0811

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.8395 0.67 400 0.5656 0.3303 0.1236
0.3196 1.34 800 0.5007 0.2922 0.1071
0.2491 2.01 1200 0.5008 0.2830 0.1056
0.2012 2.68 1600 0.5177 0.2689 0.1003
0.1882 3.35 2000 0.5517 0.2622 0.0986
0.1811 4.02 2400 0.5225 0.2543 0.0980
0.1648 4.69 2800 0.5504 0.2477 0.0948
0.1451 5.36 3200 0.5181 0.2346 0.0908
0.149 6.04 3600 0.5204 0.2375 0.0941
0.1328 6.71 4000 0.5780 0.2375 0.0920
0.1256 7.38 4400 0.5406 0.2443 0.0960
0.1193 8.05 4800 0.5489 0.2317 0.0928
0.1099 8.72 5200 0.5864 0.2363 0.0925
0.1125 9.39 5600 0.5749 0.2267 0.0902
0.1044 10.06 6000 0.5698 0.2279 0.0905
0.0925 10.73 6400 0.6051 0.2337 0.0933
0.0951 11.4 6800 0.6785 0.2286 0.0907
0.0926 12.07 7200 0.5937 0.2337 0.0919
0.0838 12.74 7600 0.5918 0.2233 0.0893
0.0775 13.41 8000 0.5642 0.2227 0.0888
0.0742 14.08 8400 0.5927 0.2249 0.0898
0.0687 14.75 8800 0.6647 0.2265 0.0900
0.0685 15.42 9200 0.7438 0.2164 0.0885
0.0645 16.09 9600 0.6351 0.2128 0.0858
0.0582 16.76 10000 0.6164 0.2169 0.0878
0.0604 17.44 10400 0.6327 0.2146 0.0867
0.0557 18.11 10800 0.6790 0.2148 0.0879
0.0552 18.78 11200 0.6859 0.2101 0.0848
0.0474 19.45 11600 0.6648 0.2071 0.0847
0.048 20.12 12000 0.7172 0.2136 0.0873
0.0475 20.79 12400 0.6451 0.2058 0.0845
0.041 21.46 12800 0.6826 0.2074 0.0839
0.0405 22.13 13200 0.6738 0.2110 0.0842
0.0355 22.8 13600 0.7020 0.2050 0.0839
0.0325 23.47 14000 0.7085 0.2117 0.0854
0.0308 24.14 14400 0.7418 0.2077 0.0854
0.0321 24.81 14800 0.7371 0.2051 0.0840
0.0274 25.48 15200 0.7611 0.2082 0.0848
0.0287 26.15 15600 0.7208 0.2021 0.0836
0.0253 26.82 16000 0.7432 0.2025 0.0831
0.0256 27.49 16400 0.7435 0.2011 0.0824
0.0243 28.16 16800 0.7543 0.1991 0.0818
0.0241 28.83 17200 0.7676 0.1986 0.0814
0.0204 29.51 17600 0.7956 0.1972 0.0811

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 1.18.3
  • Tokenizers 0.13.3