hubert-japanese-large-noise-0427

This model is a fine-tuned version of rinna/japanese-hubert-large on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3383
  • Cer: 0.0896
  • Wer: 0.998

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: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 12500.0
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Cer Wer
9.4222 1.0 2500 8.3462 0.9998 1.0
3.8795 2.0 5000 3.7791 0.7299 1.0
3.6295 3.0 7500 3.5969 0.7280 1.0
1.451 4.0 10000 1.0974 0.1931 1.0
0.7754 5.0 12500 0.5525 0.1595 1.0
0.636 6.0 15000 0.4586 0.1605 1.0
0.5528 7.0 17500 0.4240 0.1377 1.0
0.5064 8.0 20000 0.3931 0.1412 1.0
0.4767 9.0 22500 0.3593 0.1403 1.0
0.449 10.0 25000 0.3519 0.1112 1.0
0.4261 11.0 27500 0.3578 0.1048 1.0
0.4131 12.0 30000 0.3459 0.1142 1.0
0.3807 13.0 32500 0.3355 0.1072 1.0
0.3759 14.0 35000 0.3380 0.0967 1.0
0.3532 15.0 37500 0.3310 0.1198 1.0
0.3469 16.0 40000 0.3383 0.0927 1.0
0.3297 17.0 42500 0.3363 0.0911 1.0
0.3347 18.0 45000 0.3333 0.0895 0.998
0.3225 19.0 47500 0.3393 0.0944 0.998
0.3199 20.0 50000 0.3341 0.0873 0.998
0.3141 21.0 52500 0.3363 0.0863 0.998
0.2927 22.0 55000 0.3384 0.0889 0.998
0.3051 23.0 57500 0.3389 0.0902 0.998
0.3072 24.0 60000 0.3387 0.0895 0.998
0.3109 25.0 62500 0.3383 0.0896 0.998

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2
  • Datasets 2.18.0
  • Tokenizers 0.15.1
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Evaluation results