whisper-tiny-south-indic-audio-abuse-feature

This model is a fine-tuned version of parambharat/whisper-tiny-south-indic on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8754
• Accuracy: 0.8091
• Macro F1-score: 0.7326

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
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1-score
7.6265	0.29	10	7.5999	0.0	0.0
7.5514	0.58	20	7.4739	0.0	0.0
7.3762	0.86	30	7.2108	0.7073	0.2762
7.0242	1.15	40	6.6877	0.7114	0.4157
6.421	1.44	50	6.0112	0.7114	0.4157
5.7786	1.73	60	5.4468	0.7114	0.4157
5.2429	2.01	70	4.9849	0.7114	0.4157
4.8449	2.3	80	4.5921	0.7114	0.4157
4.5013	2.59	90	4.2484	0.7114	0.4157
4.1317	2.88	100	3.9375	0.7114	0.4157
3.8904	3.17	110	3.6543	0.7114	0.4157
3.5933	3.45	120	3.3909	0.7114	0.4157
3.3129	3.74	130	3.1460	0.7114	0.4157
3.0954	4.03	140	2.9201	0.7114	0.4157
2.8817	4.32	150	2.7098	0.7114	0.4157
2.7003	4.6	160	2.5173	0.7114	0.4157
2.5074	4.89	170	2.3380	0.7114	0.4157
2.3684	5.18	180	2.1744	0.7114	0.4157
2.1876	5.47	190	2.0227	0.7114	0.4157
2.0526	5.76	200	1.8856	0.7114	0.4157
1.8551	6.04	210	1.7647	0.7114	0.4157
1.7855	6.33	220	1.6472	0.7114	0.4157
1.7239	6.62	230	1.5505	0.7358	0.4996
1.5197	6.91	240	1.4623	0.7114	0.4157
1.485	7.19	250	1.3723	0.7439	0.5250
1.4318	7.48	260	1.2978	0.7642	0.5914
1.3828	7.77	270	1.2308	0.7805	0.6390
1.1559	8.06	280	1.1848	0.7398	0.5124
1.1692	8.35	290	1.1189	0.7886	0.6581
1.2058	8.63	300	1.0709	0.7846	0.6486
1.1676	8.92	310	1.0313	0.7886	0.6635
1.0257	9.21	320	0.9966	0.7886	0.6581
0.9943	9.5	330	0.9652	0.7886	0.6635
0.9894	9.78	340	0.9398	0.7927	0.6726
1.0581	10.07	350	0.9206	0.8171	0.7313
0.9395	10.36	360	0.9003	0.8008	0.6992
0.9766	10.65	370	0.8873	0.7886	0.6687
0.9291	10.94	380	0.8759	0.8089	0.7155
0.8967	11.22	390	0.8704	0.7927	0.6726
0.9463	11.51	400	0.8656	0.7967	0.6862

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3