speaker-segmentation-darija2

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

• Loss: 0.3338
• Model Preparation Time: 0.0061
• Der: 0.1220
• False Alarm: 0.0235
• Missed Detection: 0.0296
• Confusion: 0.0688

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.7164	1.0	683	0.8500	0.0061	0.2406	0.0365	0.0432	0.1609
0.6075	2.0	1366	0.6868	0.0061	0.2182	0.0361	0.0408	0.1413
0.5213	3.0	2049	0.5659	0.0061	0.1947	0.0329	0.0395	0.1224
0.4664	4.0	2732	0.5040	0.0061	0.1821	0.0306	0.0372	0.1143
0.411	5.0	3415	0.4678	0.0061	0.1738	0.0297	0.0355	0.1086
0.4205	6.0	4098	0.4503	0.0061	0.1682	0.0286	0.0348	0.1048
0.4133	7.0	4781	0.4330	0.0061	0.1629	0.0285	0.0336	0.1009
0.3936	8.0	5464	0.4191	0.0061	0.1579	0.0278	0.0329	0.0972
0.3799	9.0	6147	0.4080	0.0061	0.1529	0.0276	0.0323	0.0931
0.3557	10.0	6830	0.4007	0.0061	0.1500	0.0269	0.0317	0.0914
0.3564	11.0	7513	0.3915	0.0061	0.1465	0.0258	0.0319	0.0888
0.3658	12.0	8196	0.3853	0.0061	0.1433	0.0258	0.0314	0.0861
0.3606	13.0	8879	0.3784	0.0061	0.1408	0.0255	0.0311	0.0842
0.3685	14.0	9562	0.3739	0.0061	0.1390	0.0255	0.0308	0.0827
0.3364	15.0	10245	0.3706	0.0061	0.1378	0.0253	0.0306	0.0818
0.3436	16.0	10928	0.3698	0.0061	0.1369	0.0248	0.0307	0.0814
0.3339	17.0	11611	0.3636	0.0061	0.1353	0.0249	0.0304	0.0799
0.3416	18.0	12294	0.3615	0.0061	0.1343	0.0246	0.0304	0.0792
0.3396	19.0	12977	0.3593	0.0061	0.1337	0.0243	0.0305	0.0789
0.344	20.0	13660	0.3572	0.0061	0.1330	0.0243	0.0305	0.0782
0.3372	21.0	14343	0.3541	0.0061	0.1320	0.0245	0.0302	0.0773
0.3271	22.0	15026	0.3549	0.0061	0.1313	0.0242	0.0302	0.0768
0.3206	23.0	15709	0.3516	0.0061	0.1310	0.0243	0.0301	0.0766
0.3359	24.0	16392	0.3524	0.0061	0.1308	0.0242	0.0301	0.0765
0.322	25.0	17075	0.3512	0.0061	0.1304	0.0241	0.0301	0.0762
0.3169	26.0	17758	0.3507	0.0061	0.1301	0.0243	0.0300	0.0758
0.3351	27.0	18441	0.3508	0.0061	0.1300	0.0243	0.0299	0.0758
0.3221	28.0	19124	0.3501	0.0061	0.1300	0.0243	0.0299	0.0758
0.324	29.0	19807	0.3499	0.0061	0.1300	0.0243	0.0299	0.0758
0.3271	30.0	20490	0.3499	0.0061	0.1300	0.0243	0.0299	0.0758

Framework versions

• Transformers 4.57.3
• Pytorch 2.5.1+cu121
• Datasets 4.4.2
• Tokenizers 0.22.2