This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the diarizers-community/callhome dataset. It achieves the following results on the evaluation set:

Loss: 0.4600
Model Preparation Time: 0.0051
Der: 0.1818
False Alarm: 0.0578
Missed Detection: 0.0721
Confusion: 0.0518

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.392	1.0	362	0.4730	0.0051	0.1926	0.0622	0.0736	0.0568
0.4053	2.0	724	0.4586	0.0051	0.1838	0.0625	0.0704	0.0509
0.3865	3.0	1086	0.4537	0.0051	0.1811	0.0574	0.0723	0.0514
0.3571	4.0	1448	0.4570	0.0051	0.1805	0.0551	0.0740	0.0514
0.3409	5.0	1810	0.4600	0.0051	0.1818	0.0578	0.0721	0.0518

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

JSWOOK/speaker-segmentation-fine-tuned-callhome-eng

speaker-segmentation-fine-tuned-callhome-eng-forproject

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

JSWOOK/
speaker-segmentation-fine-tuned-callhome-eng