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whisper-small-libirClean-vs-commonNative-en

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

  • Loss: 2.3358
  • Wer: 85.5379

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: 8
  • 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: 10
  • training_steps: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.2481 0.08 10 3.5688 21.1895
0.7793 0.16 20 2.8307 38.9990
0.5443 0.24 30 2.4196 67.0458
0.4484 0.32 40 2.2903 71.1732
0.4086 0.4 50 2.3358 85.5379

Framework versions

  • Transformers 4.25.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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Dataset used to train bgstud/whisper-small-libirClean-vs-commonNative-en

Evaluation results