speaker-segmentation-fine-tuned-CAENNAIS-12.03.25 – AI Model by Rziane | AlphaNeural AI

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Rziane/
speaker-segmentation-fine-tuned-CAENNAIS-12.03.25

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speaker-diarization

speaker-segmentation

generated_from_trainer

pyannote/segmentation-3.0

endpoints_compatible

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speaker-segmentation-fine-tuned_CAENNAIS

This model is a fine-tuned version of pyannote/segmentation-3.0 on the CAENNAIS dataset. It achieves the following results on the evaluation set:

Loss: 0.7428
Model Preparation Time: 0.0037
Der: 0.3408
False Alarm: 0.1209
Missed Detection: 0.0852
Confusion: 0.1347

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.8734	1.0	60	1.0093	0.0037	0.4826	0.1696	0.0702	0.2427
0.7768	2.0	120	0.8283	0.0037	0.4026	0.1486	0.0660	0.1880
0.7475	3.0	180	0.8371	0.0037	0.4044	0.1570	0.0710	0.1764
0.6646	4.0	240	0.7287	0.0037	0.3528	0.1258	0.0880	0.1390
0.6458	5.0	300	0.7566	0.0037	0.3505	0.1347	0.0757	0.1401
0.6411	6.0	360	0.7764	0.0037	0.3585	0.1326	0.0784	0.1475
0.6081	7.0	420	0.7748	0.0037	0.3547	0.1332	0.0757	0.1457
0.6008	8.0	480	0.7814	0.0037	0.3533	0.1308	0.0804	0.1420
0.5997	9.0	540	0.7417	0.0037	0.3409	0.1205	0.0854	0.1349
0.5874	10.0	600	0.7428	0.0037	0.3408	0.1209	0.0852	0.1347

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0