whisper_med_alex.en

This model is a fine-tuned version of crossdelenna/whisper_med_alex.en on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0002
• Wer: 5.3233

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-06
• train_batch_size: 24
• eval_batch_size: 20
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10
• training_steps: 7501
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3202	2.6087	300	0.1248	10.6277
0.1006	5.2174	600	0.0287	6.0217
0.0297	7.8261	900	0.0127	5.0118
0.0135	10.4348	1200	0.0041	6.0500
0.0048	13.0435	1500	0.0025	5.5970
0.0034	15.6522	1800	0.0022	5.5687
0.0027	18.2609	2100	0.0015	5.3610
0.0018	20.8696	2400	0.0012	5.2950
0.0016	23.4783	2700	0.0011	5.3044
0.0013	26.0870	3000	0.0009	5.2761
0.0011	28.6957	3300	0.0008	5.2478
0.0009	31.3043	3600	0.0006	5.3044
0.0008	33.9130	3900	0.0005	5.3044
0.0007	36.5217	4200	0.0005	5.2855
0.0006	39.1304	4500	0.0004	5.3327
0.0005	41.7391	4800	0.0004	5.3705
0.0005	44.3478	5100	0.0004	5.3516
0.0005	46.9565	5400	0.0003	5.2855
0.0004	49.5652	5700	0.0003	5.3327
0.0004	52.1739	6000	0.0003	5.2950
0.0003	54.7826	6300	0.0002	5.2950
0.0003	57.3913	6600	0.0002	5.3327
0.0003	60.0	6900	0.0002	5.3138
0.0002	62.6087	7200	0.0002	5.3233
0.0002	65.2174	7500	0.0002	5.3233

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0