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mhdp-africa/speaker-segmentation-fine-tuned-callhome-MHDP-diarization-v1

sourceHugging Facemitupdated 5mo agoView on Hugging Face

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speaker-segmentation-fine-tuned-callhome-MHDP-diarization-v1

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

—Loss: 0.3927
—Der: 0.1574
—False Alarm: 0.0406
—Missed Detection: 0.0711
—Confusion: 0.0458

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
—trainbatchsize: 64
—evalbatchsize: 64
—seed: 42
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosine
—num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.4003	1.0	304	0.4970	0.1927	0.0734	0.0675	0.0518
0.3686	2.0	608	0.4730	0.1799	0.0552	0.0778	0.0469
0.3446	3.0	912	0.4606	0.1718	0.0535	0.0763	0.0419
0.3520	4.0	1216	0.4732	0.1764	0.0661	0.0666	0.0438
0.3379	5.0	1520	0.4742	0.1773	0.0688	0.0662	0.0424
0.3186	6.0	1824	0.4669	0.1719	0.0520	0.0758	0.0441
0.3164	7.0	2128	0.4656	0.1696	0.0586	0.0689	0.0420
0.3013	8.0	2432	0.4661	0.1711	0.0564	0.0713	0.0434

Framework versions

—Transformers 5.5.4
—Pytorch 2.11.0+cu130
—Datasets 4.8.4
—Tokenizers 0.22.2