whisper-small-sw-ndizi-158_2

This model is a fine-tuned version of pplantinga/whisper-small-sw on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.7989

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	18	2.8008
2.5386	2.0	36	2.7557
2.4516	3.0	54	2.6801
2.4516	4.0	72	2.5842
2.363	5.0	90	2.5062
2.2145	6.0	108	2.4334
2.1358	7.0	126	2.3611
2.1358	8.0	144	2.2867
2.059	9.0	162	2.2158
1.8708	10.0	180	2.1452
1.8708	11.0	198	2.0739
1.8568	12.0	216	2.0307
1.8349	13.0	234	2.0092
1.7117	14.0	252	1.9914
1.7117	15.0	270	1.9788
1.6683	16.0	288	1.9669
1.6639	17.0	306	1.9548
1.6639	18.0	324	1.9432
1.6765	19.0	342	1.9338
1.6148	20.0	360	1.9265
1.5875	21.0	378	1.9149
1.5875	22.0	396	1.9058
1.5664	23.0	414	1.9013
1.6485	24.0	432	1.8953
1.535	25.0	450	1.8917
1.535	26.0	468	1.8849
1.5579	27.0	486	1.8783
1.5141	28.0	504	1.8746
1.5141	29.0	522	1.8707
1.5943	30.0	540	1.8663
1.4296	31.0	558	1.8630
1.4895	32.0	576	1.8595
1.4895	33.0	594	1.8536
1.5366	34.0	612	1.8525
1.4573	35.0	630	1.8488
1.4573	36.0	648	1.8489
1.4729	37.0	666	1.8441
1.4758	38.0	684	1.8384
1.4386	39.0	702	1.8391
1.4386	40.0	720	1.8356
1.3773	41.0	738	1.8343
1.4994	42.0	756	1.8328
1.4994	43.0	774	1.8331
1.4342	44.0	792	1.8287
1.4047	45.0	810	1.8283
1.3758	46.0	828	1.8261
1.3758	47.0	846	1.8234
1.3856	48.0	864	1.8201
1.3815	49.0	882	1.8210
1.4364	50.0	900	1.8197
1.4364	51.0	918	1.8188
1.4035	52.0	936	1.8183
1.3368	53.0	954	1.8176
1.3368	54.0	972	1.8155
1.424	55.0	990	1.8158
1.3782	56.0	1008	1.8159
1.3057	57.0	1026	1.8122
1.3057	58.0	1044	1.8136
1.3615	59.0	1062	1.8142
1.4013	60.0	1080	1.8091
1.4013	61.0	1098	1.8099
1.2894	62.0	1116	1.8102
1.3972	63.0	1134	1.8089
1.3564	64.0	1152	1.8096
1.3564	65.0	1170	1.8075
1.2808	66.0	1188	1.8078
1.3871	67.0	1206	1.8061
1.3871	68.0	1224	1.8060
1.267	69.0	1242	1.8070
1.2978	70.0	1260	1.8046
1.3657	71.0	1278	1.8062
1.3657	72.0	1296	1.8061
1.342	73.0	1314	1.8066
1.2504	74.0	1332	1.8063
1.3003	75.0	1350	1.8031
1.3003	76.0	1368	1.8053
1.2927	77.0	1386	1.8057
1.2653	78.0	1404	1.8032
1.2653	79.0	1422	1.8031
1.3574	80.0	1440	1.8036
1.2253	81.0	1458	1.8061
1.3348	82.0	1476	1.8036
1.3348	83.0	1494	1.8034
1.2846	84.0	1512	1.8033
1.2671	85.0	1530	1.8032
1.2671	86.0	1548	1.8038
1.3102	87.0	1566	1.8031
1.2603	88.0	1584	1.8011
1.286	89.0	1602	1.8029
1.286	90.0	1620	1.8026
1.2761	91.0	1638	1.8029
1.2416	92.0	1656	1.8014
1.2416	93.0	1674	1.8035
1.2798	94.0	1692	1.8008
1.3043	95.0	1710	1.8009
1.2969	96.0	1728	1.8004
1.2969	97.0	1746	1.8014
1.3087	98.0	1764	1.7992
1.2364	99.0	1782	1.7997
1.2748	100.0	1800	1.7989

Framework versions

• PEFT 0.7.2.dev0
• Transformers 4.37.0.dev0
• Pytorch 2.0.0
• Datasets 2.16.1
• Tokenizers 0.15.0