Whisper Small ko

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the custom dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 64
eval_batch_size: 256
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: 50
training_steps: 500
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss
0.8865	0.0901	10	1.5498
0.8668	0.1802	20	1.4739
0.7179	0.2703	30	1.2411
0.3916	0.3604	40	0.8666
0.219	0.4505	50	0.7558
0.1545	0.5405	60	0.6752
0.1278	0.6306	70	0.5819
0.0983	0.7207	80	0.5394
0.0908	0.8108	90	0.5013
0.0718	0.9009	100	0.4740
0.0773	0.9910	110	0.4579
0.0665	1.0811	120	0.4430
0.0608	1.1712	130	0.4284
0.0612	1.2613	140	0.4136
0.0605	1.3514	150	0.4104
0.0632	1.4414	160	0.3857
0.0534	1.5315	170	0.3678
0.0527	1.6216	180	0.3584
0.0516	1.7117	190	0.3458
0.0467	1.8018	200	0.3373
0.0526	1.8919	210	0.3299
0.0363	1.9820	220	0.3280
0.0468	2.0721	230	0.3202
0.0473	2.1622	240	0.3152
0.0394	2.2523	250	0.3065
0.0356	2.3423	260	0.3009
0.042	2.4324	270	0.2934
0.042	2.5225	280	0.2911
0.0314	2.6126	290	0.2899
0.0397	2.7027	300	0.2817
0.0377	2.7928	310	0.2743
0.0412	2.8829	320	0.2695
0.0362	2.9730	330	0.2649
0.0321	3.0631	340	0.2589
0.0406	3.1532	350	0.2572
0.028	3.2432	360	0.2568
0.0345	3.3333	370	0.2568
0.0346	3.4234	380	0.2544
0.0391	3.5135	390	0.2513
0.0362	3.6036	400	0.2468
0.0304	3.6937	410	0.2446
0.032	3.7838	420	0.2428
0.0307	3.8739	430	0.2422
0.0301	3.9640	440	0.2414
0.0315	4.0541	450	0.2396
0.0336	4.1441	460	0.2396
0.024	4.2342	470	0.2396
0.0286	4.3243	480	0.2391
0.0289	4.4144	490	0.2389
0.0323	4.5045	500	0.2388