he-cantillation

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

Loss: 0.4208
Wer: 18.5421
Avg Precision Exact: 0.8482
Avg Recall Exact: 0.8623
Avg F1 Exact: 0.8545
Avg Precision Letter Shift: 0.8778
Avg Recall Letter Shift: 0.8926
Avg F1 Letter Shift: 0.8844
Avg Precision Word Level: 0.8803
Avg Recall Word Level: 0.8946
Avg F1 Word Level: 0.8866
Avg Precision Word Shift: 0.9416
Avg Recall Word Shift: 0.9549
Avg F1 Word Shift: 0.9475
Precision Median Exact: 0.9167
Recall Median Exact: 0.9167
F1 Median Exact: 0.9167
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: 0.6154
Recall Min Word Shift: 0.6667
F1 Min Word Shift: 0.64

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: 32
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: 200000
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 Word Shift	Recall Min Word Shift	F1 Min Word Shift
No log	8e-05	1	7.8969	120.8943	0.0002	0.0000	0.0000	0.0030	0.0028	0.0027	0.0024	0.0137	0.0027	0.0141	0.0233	0.0133	0.0	0.0	0.0	0.0625	0.0074	0.0132	0.0	0.0	0.0
0.0777	0.8	10000	0.2194	22.7167	0.7902	0.7985	0.7935	0.8205	0.8298	0.8243	0.8264	0.8358	0.8303	0.9175	0.9318	0.9237	0.9091	0.9091	0.8889	1.0	1.0	1.0	0.0	0.0	0.0
0.0363	1.6	20000	0.2639	19.9619	0.8266	0.8330	0.8289	0.8554	0.8623	0.8580	0.8620	0.8676	0.8640	0.9419	0.9519	0.9459	0.9091	0.9167	0.9091	1.0	1.0	1.0	0.6154	0.6429	0.64
0.0189	2.4	30000	0.2936	20.0678	0.8200	0.8284	0.8233	0.8485	0.8574	0.8521	0.8538	0.8621	0.8571	0.9365	0.9491	0.9419	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.4167	0.4545	0.4348
0.0089	3.2	40000	0.3115	19.8347	0.8154	0.8264	0.8200	0.8456	0.8570	0.8504	0.8488	0.8601	0.8536	0.9396	0.9526	0.9452	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.5	0.5556	0.5263
0.0095	4.0	50000	0.3330	20.0042	0.8325	0.8425	0.8366	0.8628	0.8739	0.8674	0.8668	0.8780	0.8715	0.9376	0.9481	0.9418	0.9091	0.9167	0.9091	1.0	1.0	1.0	0.5385	0.5833	0.5600
0.0062	4.8	60000	0.3605	19.6228	0.8291	0.8399	0.8336	0.8619	0.8733	0.8667	0.8651	0.8752	0.8693	0.9376	0.9466	0.9413	0.9091	0.9167	0.9167	1.0	1.0	1.0	0.1111	0.0833	0.0952
0.0052	5.6	70000	0.3855	20.4492	0.8137	0.8244	0.8182	0.8398	0.8513	0.8448	0.8446	0.8568	0.8499	0.9330	0.9448	0.9380	0.9091	0.9091	0.9	1.0	1.0	1.0	0.4545	0.5556	0.5
0.0018	6.4	80000	0.3755	20.1950	0.8275	0.8378	0.8318	0.8579	0.8695	0.8628	0.8617	0.8732	0.8666	0.9402	0.9511	0.9447	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6667	0.6	0.6316
0.0038	7.2	90000	0.3829	20.4281	0.8308	0.8429	0.8359	0.8586	0.8715	0.8640	0.8619	0.8738	0.8669	0.9328	0.9467	0.9387	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.0833	0.0769	0.08
0.0029	8.0	100000	0.3823	19.4533	0.8423	0.8504	0.8455	0.8686	0.8773	0.8720	0.8716	0.8795	0.8747	0.9435	0.9498	0.9457	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6154	0.6667	0.64
0.0013	8.8	110000	0.3992	19.7499	0.8323	0.8449	0.8378	0.8609	0.8745	0.8668	0.8639	0.8767	0.8694	0.9404	0.9532	0.9459	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6154	0.625	0.625
0.0015	9.6	120000	0.3963	19.8135	0.8317	0.8426	0.8364	0.8629	0.8745	0.8679	0.8660	0.8775	0.8710	0.9355	0.9460	0.9399	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.0	0.0	0.0
0.0005	10.4	130000	0.4010	19.3261	0.8398	0.8500	0.8441	0.8705	0.8816	0.8751	0.8737	0.8845	0.8782	0.9379	0.9497	0.9430	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.2727	0.2727	0.2727
0.0003	11.2	140000	0.4015	19.3685	0.8386	0.8518	0.8444	0.8676	0.8817	0.8738	0.8714	0.8844	0.8771	0.9319	0.9461	0.9381	0.9091	0.9167	0.9091	1.0	1.0	1.0	0.2727	0.2727	0.2727
0.0007	12.0	150000	0.4071	19.2837	0.8341	0.8455	0.8389	0.8642	0.8765	0.8695	0.8684	0.8802	0.8735	0.9380	0.9493	0.9428	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.5833	0.5385	0.5600
0.0008	12.8	160000	0.4111	19.3685	0.8391	0.8491	0.8433	0.8683	0.8791	0.8729	0.8722	0.8824	0.8765	0.9373	0.9494	0.9425	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.5833	0.5833	0.5833
0.0002	13.6	170000	0.4107	19.4745	0.8295	0.8400	0.8339	0.8607	0.8721	0.8655	0.8645	0.8751	0.8689	0.9379	0.9493	0.9427	0.9091	0.9167	0.9091	1.0	1.0	1.0	0.5833	0.5833	0.5833
0.0001	14.4	180000	0.4214	18.7752	0.8473	0.8609	0.8533	0.8780	0.8925	0.8844	0.8810	0.8940	0.8867	0.9407	0.9539	0.9464	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6364	0.7273	0.6957
0.0001	15.2	190000	0.4146	18.7116	0.8474	0.8609	0.8533	0.8768	0.8910	0.8831	0.8804	0.8944	0.8866	0.9413	0.9550	0.9473	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6667	0.7273	0.6957
0.0005	16.0	200000	0.4208	18.5421	0.8482	0.8623	0.8545	0.8778	0.8926	0.8844	0.8803	0.8946	0.8866	0.9416	0.9549	0.9475	0.9167	0.9167	0.9167	1.0	1.0	1.0	0.6154	0.6667	0.64

Framework versions

Transformers 4.41.2
Pytorch 2.2.1
Datasets 2.20.0
Tokenizers 0.19.1

cantillation
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Teamim-small_Random_WeightDecay-0.05_Augmented_Old-Data_date-21-07-2024_14-33

he-cantillation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for cantillation/Teamim-small_Random_WeightDecay-0.05_Augmented_Old-Data_date-21-07-2024_14-33

Evaluation results