KasuleTrevor/whisper-small-test

Whisper Small Hi - Sanchit Gandhi

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9542
• Wer: 54.0801

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: 8
• 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: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.7988	0.2451	100	1.2849	109.6915
0.8952	0.4902	200	1.1418	95.3581
0.7785	0.7353	300	1.0263	94.3665
0.7053	0.9804	400	0.9466	85.3250
0.5318	1.2255	500	0.9058	81.4873
0.4995	1.4706	600	0.8554	66.8638
0.4802	1.7157	700	0.8245	64.1645
0.4654	1.9608	800	0.7959	77.4477
0.2461	2.2059	900	0.8013	66.1440
0.2342	2.4510	1000	0.7986	52.0676
0.2239	2.6961	1100	0.7772	62.4128
0.2732	2.9412	1200	0.7743	68.8065
0.1017	3.1863	1300	0.8005	65.6739
0.1081	3.4314	1400	0.8069	68.5678
0.1045	3.6765	1500	0.8008	63.2317
0.1076	3.9216	1600	0.7981	70.0220
0.0604	4.1667	1700	0.8220	57.7194
0.0449	4.4118	1800	0.8294	68.8652
0.0488	4.6569	1900	0.8347	59.0709
0.0453	4.9020	2000	0.8300	67.5468
0.0224	5.1471	2100	0.8509	51.2523
0.0179	5.3922	2200	0.8573	59.6071
0.0224	5.6373	2300	0.8622	50.2828
0.0218	5.8824	2400	0.8650	59.3720
0.01	6.1275	2500	0.8657	74.6126
0.0109	6.3725	2600	0.8816	64.8843
0.0095	6.6176	2700	0.8817	57.7378
0.0086	6.8627	2800	0.8824	69.1223
0.0046	7.1078	2900	0.8988	58.5861
0.0037	7.3529	3000	0.9064	55.1965
0.005	7.5980	3100	0.9079	65.3654
0.0048	7.8431	3200	0.8993	52.4128
0.0023	8.0882	3300	0.9158	59.9412
0.0021	8.3333	3400	0.9183	55.7363
0.0022	8.5784	3500	0.9247	60.8336
0.002	8.8235	3600	0.9282	60.2644
0.0017	9.0686	3700	0.9243	61.2156
0.0016	9.3137	3800	0.9308	61.0797
0.0017	9.5588	3900	0.9399	52.4091
0.0014	9.8039	4000	0.9414	50.3783
0.0013	10.0490	4100	0.9440	54.8109
0.0013	10.2941	4200	0.9430	58.0353
0.0013	10.5392	4300	0.9472	55.0790
0.0012	10.7843	4400	0.9470	55.8538
0.0011	11.0294	4500	0.9488	56.8454
0.0011	11.2745	4600	0.9517	54.1315
0.0011	11.5196	4700	0.9530	53.8414
0.0011	11.7647	4800	0.9538	54.4583
0.0011	12.0098	4900	0.9539	54.0507
0.0011	12.2549	5000	0.9542	54.0801

Framework versions

• Transformers 4.44.2
• Pytorch 2.1.0+cu118
• Datasets 2.21.0
• Tokenizers 0.19.1