whisper-small-emergency

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

• Loss: 0.2212
• Wer: 21.7895
• Cer: 10.3463

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: 1000
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
2.2586	0.02	100	2.0061	38.3870	19.3958
0.9821	0.05	200	0.8927	37.3486	16.9619
0.7244	0.07	300	0.6577	32.9353	15.2939
0.505	0.1	400	0.4395	33.8006	16.8397
0.4397	0.12	500	0.3908	29.2489	13.3393
0.3602	0.15	600	0.3710	27.7259	12.8741
0.4321	0.17	700	0.3558	27.7778	12.8224
0.3979	0.19	800	0.3504	27.5528	12.6063
0.2614	0.22	900	0.3434	28.2451	13.5601
0.3725	0.24	1000	0.3362	26.8086	12.8177
0.4098	0.27	1100	0.3329	26.8086	13.1466
0.3083	0.29	1200	0.3240	25.6663	12.0566
0.324	0.32	1300	0.3169	24.7490	11.3659
0.3437	0.34	1400	0.3090	24.2471	10.9383
0.3719	0.36	1500	0.3064	24.4548	11.2155
0.3563	0.39	1600	0.3013	24.0222	11.0887
0.3493	0.41	1700	0.3036	24.1606	11.1779
0.3132	0.44	1800	0.3011	24.0741	11.1685
0.3024	0.46	1900	0.2920	24.4202	11.2014
0.2982	0.49	2000	0.2873	22.9664	10.4262
0.3309	0.51	2100	0.2880	23.3991	10.8208
0.3209	0.53	2200	0.2811	21.9280	10.2288
0.2778	0.56	2300	0.2883	22.6895	10.5060
0.3391	0.58	2400	0.2796	21.9280	10.1818
0.3261	0.61	2500	0.2757	22.3607	10.1865
0.2711	0.63	2600	0.2746	22.9491	10.4356
0.2723	0.66	2700	0.2708	22.3088	10.5624
0.3152	0.68	2800	0.2681	21.8934	10.0127
0.248	0.7	2900	0.2679	22.2568	10.0644
0.2354	0.73	3000	0.2665	21.7203	9.8576
0.2828	0.75	3100	0.2628	21.4261	9.9422
0.2759	0.78	3200	0.2652	21.2703	9.8623
0.2904	0.8	3300	0.2606	21.2876	9.8388
0.2844	0.83	3400	0.2600	21.8761	10.0362
0.2815	0.85	3500	0.2554	20.9069	9.5992
0.2713	0.87	3600	0.2573	20.8550	9.5334
0.2748	0.9	3700	0.2566	21.5126	9.8811
0.2447	0.92	3800	0.2526	20.5088	9.3455
0.3255	0.95	3900	0.2517	20.3358	11.3048
0.2786	0.97	4000	0.2489	20.8030	9.5898
0.245	1.0	4100	0.2523	21.4607	9.7167
0.1655	1.02	4200	0.2470	20.4396	9.5287
0.1898	1.04	4300	0.2422	19.9550	9.0871
0.1394	1.07	4400	0.2429	20.0242	9.2750
0.1592	1.09	4500	0.2433	19.9896	9.0824
0.1542	1.12	4600	0.2428	20.2492	9.3126
0.1296	1.14	4700	0.2437	19.4531	8.9038
0.1477	1.17	4800	0.2432	19.7300	11.0605
0.1551	1.19	4900	0.2436	20.0762	11.3236
0.1581	1.21	5000	0.2435	19.7992	10.9994
0.2033	1.24	5100	0.2434	19.8339	9.1763
0.1444	1.26	5200	0.2399	19.8165	10.9806
0.1543	1.29	5300	0.2371	19.1762	10.8913
0.1735	1.31	5400	0.2350	19.4185	9.0166
0.1552	1.34	5500	0.2363	19.0897	8.8098
0.1495	1.36	5600	0.2332	19.1070	8.8145
0.1636	1.38	5700	0.2350	18.6051	10.5718
0.1827	1.41	5800	0.2333	18.4493	8.5091
0.1464	1.43	5900	0.2344	19.2454	8.8850
0.1999	1.46	6000	0.2325	23.1222	10.9900
0.1547	1.48	6100	0.2318	19.3839	8.8709
0.1296	1.51	6200	0.2339	19.3146	8.9085
0.1535	1.53	6300	0.2317	22.5684	10.8302
0.1467	1.55	6400	0.2310	19.1070	8.7958
0.1709	1.58	6500	0.2338	18.9685	8.7441
0.1359	1.6	6600	0.2295	19.0550	8.6548
0.1611	1.63	6700	0.2293	18.5877	8.5608
0.1232	1.65	6800	0.2309	19.4012	8.9273
0.1692	1.68	6900	0.2288	18.6224	8.8756
0.1544	1.7	7000	0.2265	18.3454	8.5467
0.1282	1.72	7100	0.2256	18.6570	8.6642
0.1414	1.75	7200	0.2258	22.1011	10.2993
0.157	1.77	7300	0.2259	18.8474	8.6501
0.1592	1.8	7400	0.2249	18.6570	8.5702
0.0998	1.82	7500	0.2246	18.8127	8.6125
0.1486	1.85	7600	0.2225	18.3281	8.3024
0.1336	1.87	7700	0.2221	18.5704	8.4387
0.1388	1.9	7800	0.2222	18.5531	8.5044
0.1341	1.92	7900	0.2212	22.0665	10.4215
0.1548	1.94	8000	0.2215	21.8588	10.3275
0.1276	1.97	8100	0.2182	21.8069	10.3040
0.1567	1.99	8200	0.2200	18.1031	8.3541
0.1054	2.02	8300	0.2201	21.5646	10.2335
0.0793	2.04	8400	0.2219	21.1838	10.1161
0.0944	2.07	8500	0.2225	21.5819	10.3510
0.0824	2.09	8600	0.2230	21.7203	10.2476
0.0863	2.11	8700	0.2222	21.6684	10.2241
0.1102	2.14	8800	0.2233	21.5819	10.3228
0.0852	2.16	8900	0.2226	21.8588	10.2946
0.0796	2.19	9000	0.2227	21.9626	10.3651
0.1023	2.21	9100	0.2223	21.7722	10.4309
0.08	2.24	9200	0.2216	21.4780	10.2664
0.0703	2.26	9300	0.2218	21.5992	10.2429
0.0923	2.28	9400	0.2212	21.4434	10.2006
0.0694	2.31	9500	0.2217	21.4780	10.2194
0.1033	2.33	9600	0.2216	21.5126	10.2382
0.0913	2.36	9700	0.2214	21.5299	10.2194
0.0882	2.38	9800	0.2212	21.7376	10.2758
0.0852	2.41	9900	0.2212	21.7203	10.3087
0.0862	2.43	10000	0.2212	21.7895	10.3463

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.13.3