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Whisper Small - Singlish v2

This model is a fine-tuned version of openai/whisper-small on the rngzhi/cs3264-project dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1850
  • Wer: 4.9236

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
  • 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: 25
  • training_steps: 800
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.5404 0.0625 50 0.1970 5.6075
0.075 1.0144 100 0.1557 4.8780
0.042 1.0769 150 0.1610 4.9692
0.0185 2.0288 200 0.1628 4.9122
0.0117 2.0913 250 0.1651 5.0262
0.0096 3.0431 300 0.1716 5.0490
0.007 3.1056 350 0.1747 5.0034
0.0045 4.0575 400 0.1783 5.1402
0.0046 5.0094 450 0.1749 5.1288
0.004 5.0719 500 0.1782 5.0148
0.0021 6.0237 550 0.1814 5.0034
0.004 6.0862 600 0.1813 4.9920
0.0024 7.0381 650 0.1844 4.9350
0.0022 7.1006 700 0.1834 4.9008
0.0032 8.0525 750 0.1850 4.9236
0.0016 9.0044 800 0.1850 4.9236

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.1.dev0
  • Tokenizers 0.19.1
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Finetuned from

Dataset used to train rngzhi/cs3264-project-v2

Space using rngzhi/cs3264-project-v2 1

Evaluation results