cantillation/whisper-medium-he-teamim-aviv-base

he

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

• Loss: 0.1138
• Wer: 9.9943
• Precision: 0.8917
• Recall: 0.8913
• F1: 0.8914
• Precision Median: 1.0
• Recall Median: 1.0
• F1 Median: 1.0
• Precision Max: 1.0
• Recall Max: 1.0
• F1 Max: 1.0
• Precision Min: 0.0
• Recall Min: 0.0
• F1 Min: 0.0

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: 4e-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
• training_steps: 10000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Precision	Recall	F1	Precision Median	Recall Median	F1 Median	Precision Max	Recall Max	F1 Max	Precision Min	Recall Min	F1 Min
0.2168	0.04	500	0.2124	27.7691	0.6808	0.7027	0.6909	0.8125	0.8462	0.8276	1.0	1.0	1.0	0.0	0.0	0.0
0.1421	0.08	1000	0.1752	21.5191	0.7794	0.7820	0.7803	0.8889	0.8947	0.8947	1.0	1.0	1.0	0.0	0.0	0.0
0.086	0.12	1500	0.1510	17.9741	0.8044	0.8044	0.8040	0.9231	0.9231	0.9167	1.0	1.0	1.0	0.0	0.0	0.0
0.0822	0.16	2000	0.1357	17.1839	0.8070	0.8091	0.8078	0.9231	0.9231	0.9231	1.0	1.0	1.0	0.0	0.0	0.0
0.0675	0.2	2500	0.1227	14.9416	0.8324	0.8320	0.8319	0.9333	0.9333	0.9333	1.0	1.0	1.0	0.0	0.0	0.0
0.0583	0.24	3000	0.1224	14.0376	0.8528	0.8498	0.8510	0.9333	0.9333	0.9375	1.0	1.0	1.0	0.0	0.0	0.0
0.0528	0.28	3500	0.1167	13.8667	0.8393	0.8410	0.8399	0.9333	0.9333	0.9333	1.0	1.0	1.0	0.0	0.0	0.0
0.0431	0.32	4000	0.1173	13.3827	0.8546	0.8579	0.8560	0.9375	0.9412	0.9412	1.0	1.0	1.0	0.0	0.0	0.0
0.0402	0.36	4500	0.1154	12.1654	0.8695	0.8703	0.8697	0.9412	0.9412	0.9444	1.0	1.0	1.0	0.0	0.0	0.0
0.0385	0.4	5000	0.1173	11.9448	0.8593	0.8578	0.8584	0.9444	0.9444	0.9474	1.0	1.0	1.0	0.0	0.0	0.0
0.0266	0.44	5500	0.1144	12.1014	0.8706	0.8732	0.8717	0.9474	0.95	0.9583	1.0	1.0	1.0	0.0	0.0	0.0
0.021	0.48	6000	0.1161	11.7099	0.8737	0.8744	0.8739	1.0	1.0	0.9706	1.0	1.0	1.0	0.0	0.0	0.0
0.0228	0.52	6500	0.1109	10.9909	0.8685	0.8692	0.8687	1.0	1.0	0.9697	1.0	1.0	1.0	0.0	0.0	0.0
0.0172	0.56	7000	0.1075	10.7702	0.8780	0.8793	0.8784	1.0	0.9545	0.9697	1.0	1.0	1.0	0.0	0.0	0.0
0.0117	0.6	7500	0.1107	10.4356	0.8834	0.8825	0.8828	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0
0.0151	0.64	8000	0.1101	10.3146	0.8886	0.8899	0.8891	1.0	1.0	0.9744	1.0	1.0	1.0	0.0	0.0	0.0
0.0136	0.68	8500	0.1079	10.0370	0.8895	0.8903	0.8897	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0
0.0135	0.72	9000	0.1112	9.9445	0.8892	0.8892	0.8891	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0
0.0084	0.76	9500	0.1136	9.8875	0.8967	0.8964	0.8964	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0
0.0098	0.8	10000	0.1138	9.9943	0.8917	0.8913	0.8914	1.0	1.0	1.0	1.0	1.0	1.0	0.0	0.0	0.0

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0