whisper-tiny-finetuned-hu

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

Tests on another databases and compare another models (tiny, base, small, mediun, large)

model_name	WER	CER	Norm WER	Norm CER	dataset	batch_size	language	runtime
openai/whisper-large-v3	19.77	4.81	14.62	3.73	g_fleurs_test_hu	16	hu	617.91
openai/whisper-large-v3	21.81	5.81	18.07	4.95	CV_17_0_hu_test	16	hu	5676.63
openai/whisper-large-v2	24.04	6.24	19.26	5.15	g_fleurs_test_hu	16	hu	627.70
openai/whisper-large-v2	25.97	6.57	21.82	5.47	CV_17_0_hu_test	16	hu	9275.54
sarpba/whisper-base-hungarian_v1	27.65	6.77	23.53	5.77	CV_17_0_hu_test	32	hu	460.27
openai/whisper-large	30.13	8.93	26.20	8.04	CV_17_0_hu_test	16	hu	5909.03
---> sarpba/whisper-hu-tiny-finetuned	30.81	7.67	26.63	6.60	CV_17_0_hu_test	32	hu	328.25
openai/whisper-large	31.74	10.69	26.67	9.57	g_fleurs_test_hu	16	hu	711.97
openai/whisper-medium	33.04	9.93	27.97	8.34	g_fleurs_test_hu	32	hu	450.89
sarpba/whisper-base-hungarian_v1	37.16	11.96	30.60	10.43	g_fleurs_test_hu	32	hu	67.86
openai/whisper-medium	34.46	9.12	30.63	8.05	CV_17_0_hu_test	32	hu	3317.29
---> sarpba/whisper-hu-tiny-finetuned	40.32	12.85	33.99	11.33	g_fleurs_test_hu	32	hu	51.74
openai/whisper-small	50.07	15.69	45.54	14.40	g_fleurs_test_hu	32	hu	185.89
openai/whisper-small	55.67	16.77	52.20	15.62	CV_17_0_hu_test	32	hu	1398.06
openai/whisper-base	89.82	40.00	86.61	37.75	g_fleurs_test_hu	32	hu	118.69
openai/whisper-base	95.66	39.98	93.67	38.51	CV_17_0_hu_test	32	hu	779.32
openai/whisper-tiny	108.61	58.69	106.29	55.98	g_fleurs_test_hu	32	hu	90.65
openai/whisper-tiny	120.86	55.10	119.12	53.19	CV_17_0_hu_test	32	hu	597.92

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More information needed

The following hyperparameters were used during training:

learning_rate: 7e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Use 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: 500
num_epochs: 2
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer
0.1078	0.0902	2000	0.1127	0.3073
0.0889	0.1804	4000	0.0899	0.2509
0.0766	0.2707	6000	0.0797	0.2238
0.0743	0.3609	8000	0.0733	0.2094
0.0691	0.4511	10000	0.0685	0.1963
0.0646	0.5413	12000	0.0650	0.1858
0.0602	0.6316	14000	0.0618	0.1759
0.0586	0.7218	16000	0.0594	0.1737
0.0553	0.8120	18000	0.0568	0.1665
0.055	0.9022	20000	0.0552	0.1635
0.0522	0.9925	22000	0.0531	0.1558
0.0415	1.0827	24000	0.0523	0.1555
0.0419	1.1729	26000	0.0512	0.1497
0.0406	1.2631	28000	0.0496	0.1483
0.042	1.3534	30000	0.0490	0.1464
0.0393	1.4436	32000	0.0473	0.1397
0.0395	1.5338	34000	0.0458	0.1373
0.0375	1.6240	36000	0.0448	0.1343
0.0372	1.7143	38000	0.0442	0.1328
0.036	1.8045	40000	0.0432	0.1286
0.0358	1.8947	42000	0.0424	0.1273
0.035	1.9849	44000	0.0418	0.1249