leenag/Malasar_Medium

This model is a fine-tuned version of openai/whisper-medium on the Spoken Bible Corpus: Malasar dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5907
  • Wer: 50.2570

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: 32
  • eval_batch_size: 16
  • 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: 2000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0646 11.3636 250 0.3369 55.6254
0.0104 22.7273 500 0.4445 52.3130
0.0012 34.0909 750 0.4890 50.1428
0.0002 45.4545 1000 0.5240 50.3712
0.0002 56.8182 1250 0.5488 50.1999
0.0001 68.1818 1500 0.5695 50.3712
0.0001 79.5455 1750 0.5844 50.1999
0.0001 90.9091 2000 0.5907 50.2570

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.16.0
  • Tokenizers 0.19.1
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