Teamim-Large-v3-Turbo_WeightDecay-0.005_Augmented_WithSRT_date-18-04-2025

cantillation/Teamim-Large-v3-Turbo_WeightDecay-0.005_Augmented_WithSRT_date-18-04-2025

Teamim-Large-v3-Turbo_WeightDecay-0.005_Augmented_WithSRT_date-18-04-2025 is a automatic speech recognition model from cantillation. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: mit
Model type: whisper
Access: Public
Created: Apr 18, 2025
Updated: Apr 21, 2025
SHA: 90964491

Base models

openai/whisper-Large-v3-Turbo

Model card

From the Hugging Face model README

he-cantillation

This model is a fine-tuned version of openai/whisper-Large-v3-Turbo on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4731
Wer: 37.9958
Avg Precision Exact: 0.4726
Avg Recall Exact: 0.4821
Avg F1 Exact: 0.4762
Avg Precision Letter Shift: 0.4946
Avg Recall Letter Shift: 0.5062
Avg F1 Letter Shift: 0.4989
Avg Precision Word Level: 0.5073
Avg Recall Word Level: 0.5201
Avg F1 Word Level: 0.5116
Avg Precision Word Shift: 0.6840
Avg Recall Word Shift: 0.7091
Avg F1 Word Shift: 0.6925
Precision Median Exact: 0.35
Recall Median Exact: 0.3709
F1 Median Exact: 0.3577
Precision Max Exact: 1.0
Recall Max Exact: 1.0
F1 Max Exact: 1.0
Precision Min Exact: 0.0
Recall Min Exact: 0.0
F1 Min Exact: 0.0
Precision Min Letter Shift: 0.0
Recall Min Letter Shift: 0.0
F1 Min Letter Shift: 0.0
Precision Min Word Level: 0.0
Recall Min Word Level: 0.0
F1 Min Word Level: 0.0
Precision Min Word Shift: 0.0
Recall Min Word Shift: 0.0
F1 Min Word Shift: 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: 1e-05
train_batch_size: 16
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
training_steps: 80000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Avg Precision Exact	Avg Recall Exact	Avg F1 Exact	Avg Precision Letter Shift	Avg Recall Letter Shift	Avg F1 Letter Shift	Avg Precision Word Level	Avg Recall Word Level	Avg F1 Word Level	Avg Precision Word Shift	Avg Recall Word Shift	Avg F1 Word Shift	Precision Median Exact	Recall Median Exact	F1 Median Exact	Precision Max Exact	Recall Max Exact	F1 Max Exact	Precision Min Exact	Recall Min Exact	F1 Min Exact	Precision Min Letter Shift	Recall Min Letter Shift	F1 Min Letter Shift	Precision Min Word Level	Recall Min Word Level	F1 Min Word Level	Precision Min Word Shift	Recall Min Word Shift	F1 Min Word Shift
No log	0.0001	1	6.2394	110.8685	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
0.1211	0.2962	2500	0.7739	60.3892	0.2896	0.3023	0.2948	0.3230	0.3381	0.3289	0.3400	0.3560	0.3460	0.5354	0.5766	0.5518	0.1538	0.1732	0.1611	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0734	0.5925	5000	0.6116	52.4895	0.3503	0.3636	0.3556	0.3787	0.3954	0.3854	0.3946	0.4131	0.4020	0.5867	0.6260	0.6026	0.2	0.2188	0.2064	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0701	0.8887	7500	0.5577	49.6667	0.3772	0.3910	0.3826	0.4071	0.4245	0.4137	0.4220	0.4407	0.4287	0.6095	0.6498	0.6242	0.2294	0.25	0.2371	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0455	1.1850	10000	0.5711	49.5251	0.3459	0.3571	0.3502	0.3737	0.3881	0.3792	0.3910	0.4083	0.3973	0.5849	0.6229	0.5993	0.1786	0.2	0.1885	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0426	1.4812	12500	0.5363	47.2362	0.3891	0.3960	0.3916	0.4174	0.4267	0.4208	0.4314	0.4450	0.4364	0.6261	0.6527	0.6360	0.2343	0.25	0.2407	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0639	1.7775	15000	0.5074	45.9014	0.4086	0.4159	0.4110	0.4382	0.4475	0.4411	0.4531	0.4635	0.4563	0.6393	0.6673	0.6493	0.25	0.2710	0.2568	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0197	2.0737	17500	0.5560	47.4725	0.3848	0.3992	0.3897	0.4105	0.4288	0.4169	0.4245	0.4459	0.4320	0.6001	0.6449	0.6157	0.2045	0.2353	0.2171	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0185	2.3699	20000	0.5010	45.9830	0.4033	0.4160	0.4083	0.4301	0.4455	0.4360	0.4452	0.4636	0.4517	0.6296	0.6678	0.6443	0.2347	0.2632	0.2455	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.035	2.6662	22500	0.4793	44.1070	0.4046	0.4156	0.4085	0.4318	0.4452	0.4367	0.4469	0.4620	0.4518	0.6434	0.6753	0.6545	0.2630	0.2873	0.2692	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0125	2.9624	25000	0.4838	43.4315	0.4177	0.4315	0.4231	0.4443	0.4613	0.4509	0.4589	0.4782	0.4661	0.6398	0.6802	0.6556	0.2650	0.2948	0.2748	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0141	3.2587	27500	0.4849	42.0296	0.4273	0.4390	0.4319	0.4507	0.4649	0.4562	0.4636	0.4790	0.4694	0.6480	0.6827	0.6611	0.2857	0.3150	0.3014	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0458	3.5549	30000	0.4973	45.4608	0.4103	0.4230	0.4150	0.4362	0.4511	0.4413	0.4489	0.4653	0.4545	0.6252	0.6608	0.6377	0.2411	0.2620	0.2483	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0229	3.8512	32500	0.4870	43.0668	0.4337	0.4459	0.4384	0.4585	0.4739	0.4644	0.4721	0.4896	0.4786	0.6519	0.6883	0.6656	0.2903	0.3158	0.2987	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0151	4.1474	35000	0.4685	41.9114	0.4322	0.4410	0.4351	0.4558	0.4669	0.4596	0.4700	0.4830	0.4743	0.6506	0.6798	0.6608	0.2667	0.2910	0.2798	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0175	4.4437	37500	0.5112	42.8421	0.4149	0.4269	0.4190	0.4405	0.4558	0.4459	0.4547	0.4715	0.4601	0.6293	0.6670	0.6422	0.2411	0.2667	0.2516	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0097	4.7399	40000	0.4808	42.9647	0.4254	0.4353	0.4290	0.4481	0.4613	0.4531	0.4618	0.4777	0.4676	0.6326	0.6680	0.6452	0.2407	0.2639	0.25	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0072	5.0361	42500	0.4667	40.1506	0.4493	0.4608	0.4538	0.4747	0.4878	0.4793	0.4891	0.5041	0.4940	0.6741	0.7059	0.6854	0.3	0.3333	0.3171	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0092	5.3324	45000	0.4639	40.5386	0.4385	0.4534	0.4446	0.4630	0.4804	0.4697	0.4766	0.4966	0.4840	0.6494	0.6895	0.6645	0.2903	0.3247	0.3041	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0098	5.6286	47500	0.5158	43.3221	0.4220	0.4344	0.4268	0.4456	0.4628	0.4519	0.4580	0.4782	0.4651	0.6228	0.6661	0.6383	0.2474	0.2784	0.2584	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0043	5.9249	50000	0.4820	41.5204	0.4424	0.4514	0.4455	0.4657	0.4765	0.4695	0.4774	0.4899	0.4819	0.6544	0.6795	0.6632	0.2941	0.3207	0.3000	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0043	6.2211	52500	0.4708	41.5802	0.4544	0.4684	0.4588	0.4779	0.4944	0.4832	0.4905	0.5092	0.4964	0.6580	0.6914	0.6685	0.3243	0.3571	0.3333	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0105	6.5174	55000	0.5031	42.6977	0.4399	0.4539	0.4452	0.4629	0.4799	0.4693	0.4762	0.4950	0.4830	0.6501	0.6888	0.6642	0.3037	0.3422	0.32	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0161	6.8136	57500	0.4820	39.5101	0.4516	0.4628	0.4556	0.4728	0.4866	0.4776	0.4855	0.5023	0.4910	0.6528	0.6893	0.6656	0.3118	0.3448	0.3231	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0069	7.1098	60000	0.4743	39.6487	0.4535	0.4651	0.4577	0.4759	0.4897	0.4807	0.4889	0.5049	0.4944	0.6699	0.7018	0.6815	0.3333	0.3507	0.3333	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0098	7.4061	62500	0.4884	40.9938	0.4399	0.4524	0.4447	0.4623	0.4774	0.4677	0.4744	0.4932	0.4806	0.6489	0.6895	0.6630	0.2896	0.32	0.3038	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0012	7.7023	65000	0.4967	40.0236	0.4449	0.4579	0.4498	0.4664	0.4817	0.4720	0.4785	0.4957	0.4844	0.6567	0.6922	0.6690	0.3086	0.3409	0.3205	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0025	7.9986	67500	0.4861	39.8325	0.4618	0.4724	0.4657	0.4854	0.4981	0.4902	0.4981	0.5122	0.5031	0.6722	0.7016	0.6828	0.3459	0.375	0.3529	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0018	8.2948	70000	0.4827	39.1016	0.4563	0.4680	0.4607	0.4785	0.4923	0.4836	0.4907	0.5061	0.4961	0.6707	0.7028	0.6821	0.3333	0.3624	0.3485	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0027	8.5911	72500	0.4808	38.9368	0.4607	0.4718	0.4647	0.4825	0.4965	0.4875	0.4955	0.5120	0.5010	0.6703	0.7022	0.6813	0.3333	0.3709	0.3521	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0018	8.8873	75000	0.4793	38.9076	0.4630	0.4733	0.4669	0.4852	0.4984	0.4901	0.4975	0.5123	0.5025	0.6681	0.6997	0.6789	0.3438	0.3718	0.3519	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0015	9.1836	77500	0.4782	38.2453	0.4716	0.4820	0.4755	0.4931	0.5057	0.4978	0.5052	0.5191	0.5100	0.6811	0.7099	0.6913	0.3667	0.3908	0.3774	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.001	9.4798	80000	0.4731	37.9958	0.4726	0.4821	0.4762	0.4946	0.5062	0.4989	0.5073	0.5201	0.5116	0.6840	0.7091	0.6925	0.35	0.3709	0.3577	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu126
Datasets 2.12.0
Tokenizers 0.20.1

Task: Automatic Speech Recognition
Library: transformers
Type: whisper
License: mit
Languages: he
Created: Apr 18, 2025
Updated: Apr 21, 2025