leenag/Norm_KLuke_Med

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

• Loss: 0.5144
• Wer: 44.9541

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
0.0275	11.3636	250	0.3935	48.5092
0.0091	22.7273	500	0.4626	49.0252
0.0012	34.0909	750	0.4798	47.1330
0.0002	45.4545	1000	0.5015	46.3876
0.0001	56.8182	1250	0.4947	45.5849
0.0	68.1818	1500	0.5088	45.0688
0.0	79.5455	1750	0.5129	44.9541
0.0	90.9091	2000	0.5144	44.9541

Framework versions

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