whisper-small-si-bank-v4

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8527
  • Wer Ortho: 81.5217
  • Wer: 68.4337

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 50
  • training_steps: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer
No log 0.8 2 2.3673 183.6957 250.3614
No log 1.4 4 2.3668 183.6957 250.3614
No log 2.0 6 2.3480 173.3696 254.6988
No log 2.8 8 2.2898 184.2391 254.2169
No log 3.4 10 2.1772 174.4565 255.6627
No log 4.0 12 2.0396 202.7174 240.0
No log 4.8 14 1.9599 225.0 236.6265
No log 5.4 16 1.8790 227.1739 220.9639
No log 6.0 18 1.7862 264.1304 243.3735
No log 6.8 20 1.7070 217.3913 232.5301
No log 7.4 22 1.6313 167.3913 227.7108
No log 8.0 24 1.5601 157.6087 230.1205
1.679 8.8 26 1.5093 162.5 246.7470
1.679 9.4 28 1.4718 178.8043 224.8193
1.679 10.0 30 1.4363 154.8913 204.8193
1.679 10.8 32 1.4004 148.3696 172.2892
1.679 11.4 34 1.3628 125.0 153.9759
1.679 12.0 36 1.3214 104.8913 114.4578
1.679 12.8 38 1.2853 117.3913 126.2651
1.679 13.4 40 1.2414 103.8043 128.1928
1.679 14.0 42 1.2084 93.4783 114.4578
1.679 14.8 44 1.1600 93.4783 107.2289
1.679 15.4 46 1.1256 92.3913 92.0482
1.679 16.0 48 1.0850 91.3043 90.8434
0.8861 16.8 50 1.0483 84.7826 85.0602
0.8861 17.4 52 1.0226 84.2391 84.3373
0.8861 18.0 54 0.9942 90.2174 80.0
0.8861 18.8 56 0.9647 82.6087 74.4578
0.8861 19.4 58 0.9485 92.9348 78.7952
0.8861 20.0 60 0.9217 80.4348 79.2771
0.8861 20.8 62 0.9001 78.2609 66.5060
0.8861 21.4 64 0.9018 78.8043 75.6627
0.8861 22.0 66 0.8823 86.9565 68.6747
0.8861 22.8 68 0.8551 76.0870 66.0241
0.8861 23.4 70 0.8548 77.7174 65.0602
0.8861 24.0 72 0.8346 77.1739 64.3373
0.8861 24.8 74 0.8190 80.4348 67.2289
0.294 25.4 76 0.8248 78.2609 62.4096
0.294 26.0 78 0.8349 77.1739 64.8193
0.294 26.8 80 0.8284 76.0870 64.3373
0.294 27.4 82 0.8440 77.7174 61.6867
0.294 28.0 84 0.8471 77.1739 62.4096
0.294 28.8 86 0.8378 76.0870 62.6506
0.294 29.4 88 0.8577 76.6304 60.2410
0.294 30.0 90 0.8401 74.4565 57.8313
0.294 30.8 92 0.8487 73.3696 60.0
0.294 31.4 94 0.8454 73.9130 55.6627
0.294 32.0 96 0.8615 73.9130 60.4819
0.294 32.8 98 0.8593 79.3478 64.3373
0.1107 33.4 100 0.8527 81.5217 68.4337

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.5.1+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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