Whisper Small Fine-tuned with THUYG20 Uyghur Dataset

This model is a fine-tuned version of openai/whisper-small on the THUGY20: A free Uyghur speech database dataset. It achieves the following results on the test set of THUGY20:

  • Loss: 0.7473
  • Wer Ortho: 18.0908
  • Wer: 17.9401
  • Cer: 4.9274

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 50
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Ortho Wer Cer
0.3815 0.8058 500 0.7944 34.8819 34.7960 10.4265
0.1343 1.6116 1000 0.7441 28.3393 28.3550 8.3051
0.0646 2.4174 1500 0.7396 27.7378 27.5653 8.5366
0.0311 3.2232 2000 0.6984 25.1910 24.9445 7.5643
0.0176 4.0290 2500 0.6934 21.3709 21.2523 5.8316
0.0075 4.8348 3000 0.7654 20.5541 20.3603 5.7519
0.0023 5.6406 3500 0.7686 18.7582 18.5846 5.1923
0.0004 6.4464 4000 0.7473 18.0908 17.9401 4.9274

Framework versions

  • Transformers 4.46.2
  • Pytorch 2.5.1+cu121
  • Datasets 3.1.0
  • Tokenizers 0.20.3
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