Taeyeun72/whisper-small-noising_4

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whisper-kor_noising_4

This model is a fine-tuned version of openai/whisper-small on the whisper-kor_noising_4 dataset. It achieves the following results on the evaluation set:

Loss: 0.4072
Wer: 23.5379
Cer: 10.7578

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: 16
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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.2602	0.05	100	0.4040	24.4742	10.9617
0.2721	0.09	200	0.4070	24.3302	11.0676
0.2355	0.14	300	0.4146	24.9640	11.3655
0.2238	0.18	400	0.4275	25.5690	11.5615
0.2185	0.23	500	0.4297	26.2460	11.9614
0.209	0.28	600	0.4323	26.5918	12.6671
0.2203	0.32	700	0.4270	28.4788	13.3963
0.2003	0.37	800	0.4344	26.4333	12.0790
0.2297	0.42	900	0.4305	28.6949	15.6584
0.1892	0.46	1000	0.4314	25.3961	12.1731
0.2137	0.51	1100	0.4292	25.4394	11.3812
0.2058	0.55	1200	0.4291	24.9784	11.3459
0.2139	0.6	1300	0.4257	25.2377	12.0477
0.2002	0.65	1400	0.4252	25.0792	11.4910
0.2151	0.69	1500	0.4218	25.2233	11.4478
0.2298	0.74	1600	0.4168	28.5941	14.9763
0.2138	0.78	1700	0.4157	25.5258	11.8752
0.2282	0.83	1800	0.4140	24.8055	12.0124
0.2476	0.88	1900	0.4121	28.5508	14.0628
0.2122	0.92	2000	0.4090	25.5114	13.0200
0.2742	0.97	2100	0.4083	24.2869	11.7497
0.1406	1.02	2200	0.4137	24.5462	11.0401
0.0976	1.06	2300	0.4184	24.6183	11.2126
0.1235	1.11	2400	0.4186	24.5462	11.1852
0.1237	1.15	2500	0.4156	24.4598	11.1342
0.108	1.2	2600	0.4164	24.2581	11.0911
0.1228	1.25	2700	0.4140	24.5751	11.2126
0.1292	1.29	2800	0.4144	24.2293	11.0558
0.1306	1.34	2900	0.4161	24.2149	11.0166
0.14	1.39	3000	0.4137	24.0709	11.0479
0.1298	1.43	3100	0.4139	24.5174	11.0519
0.1326	1.48	3200	0.4139	24.2581	10.9852
0.1342	1.52	3300	0.4132	25.8859	12.6632
0.1362	1.57	3400	0.4116	23.9268	10.8480
0.1607	1.62	3500	0.4106	23.5955	11.3577
0.1256	1.66	3600	0.4099	23.9556	10.8558
0.1276	1.71	3700	0.4084	23.7396	10.7696
0.1453	1.75	3800	0.4087	23.5811	10.7343
0.1614	1.8	3900	0.4077	23.6243	10.7892
0.1435	1.85	4000	0.4072	23.5379	10.7578

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu118
Datasets 2.15.0
Tokenizers 0.15.0