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whisper-multiclass-lang-en-base

This model is a fine-tuned version of openai/whisper-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1493
  • Wer: 6.2678
  • Cer: 4.3198

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: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.0286 4.5872 500 0.1880 10.4938 6.6816
0.0011 9.1743 1000 0.1529 7.6923 5.1185
0.0003 13.7615 1500 0.1525 7.1700 4.8523
0.0002 18.3486 2000 0.1510 7.0275 4.8695
0.0001 22.9358 2500 0.1505 6.7426 4.5946
0.0001 27.5229 3000 0.1499 6.6952 4.5861
0.0001 32.1101 3500 0.1496 6.4103 4.4143
0.0001 36.6972 4000 0.1495 6.5527 4.4830
0.0001 41.2844 4500 0.1493 6.3153 4.3456
0.0001 45.8716 5000 0.1493 6.2678 4.3198

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0
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