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Whisper Tiny GPU test

This model is a fine-tuned version of openai/whisper-tiny on the NbAiLab/NCC_S3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9375
  • Wer: 51.3703

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: 3e-06
  • train_batch_size: 128
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant_with_warmup
  • lr_scheduler_warmup_steps: 200
  • training_steps: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.4574 0.1 200 1.4663 71.6504
1.9587 0.2 400 1.2581 64.7381
1.816 0.3 600 1.1672 60.9318
1.7199 0.4 800 1.1006 57.6736
1.6686 0.5 1000 1.0630 56.1815
1.621 0.6 1200 1.0273 55.4811
1.5846 0.7 1400 1.0017 53.9890
1.5482 0.8 1600 0.9773 53.0146
1.521 0.9 1800 0.9575 52.1011
1.4932 1.0 2000 0.9375 51.3703

Framework versions

  • Transformers 4.28.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2
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Evaluation results