segmentationCompleto – AI Model by mirari | AlphaNeural AI

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mirari/
segmentationCompleto

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speaker-diarization

speaker-segmentation

generated_from_trainer

pyannote/segmentation

endpoints_compatible

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segmentation-3.0

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

Loss: 0.6638
Der: 0.2932
False Alarm: 0.2540
Missed Detection: 0.0387
Confusion: 0.0004

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.3145	1.0	282	0.5751	0.2944	0.2487	0.0454	0.0003
0.3087	2.0	564	0.5957	0.2912	0.2462	0.0440	0.0010
0.2905	3.0	846	0.6614	0.2970	0.2627	0.0333	0.0010
0.2733	4.0	1128	0.6626	0.2940	0.2558	0.0378	0.0004
0.2672	5.0	1410	0.6638	0.2932	0.2540	0.0387	0.0004

Framework versions

Transformers 4.45.1
Pytorch 2.4.1
Datasets 3.0.1
Tokenizers 0.20.0