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

Loss: 0.3134
Model Preparation Time: 0.0048
Der: 0.0888
False Alarm: 0.0134
Missed Detection: 0.0337
Confusion: 0.0417

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: 5e-05
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
No log	1.0	24	0.3180	0.0048	0.0915	0.0119	0.0385	0.0410
0.1903	2.0	48	0.3116	0.0048	0.0903	0.0125	0.0369	0.0409
0.1839	3.0	72	0.3089	0.0048	0.0896	0.0128	0.0357	0.0411
0.1825	4.0	96	0.3176	0.0048	0.0896	0.0131	0.0352	0.0413
0.1797	5.0	120	0.3148	0.0048	0.0892	0.0132	0.0346	0.0413
0.1801	6.0	144	0.3141	0.0048	0.0890	0.0133	0.0342	0.0415
0.1735	7.0	168	0.3137	0.0048	0.0887	0.0134	0.0338	0.0416
0.1705	8.0	192	0.3133	0.0048	0.0887	0.0134	0.0337	0.0416
0.1796	9.0	216	0.3133	0.0048	0.0887	0.0134	0.0337	0.0417
0.1644	10.0	240	0.3134	0.0048	0.0888	0.0134	0.0337	0.0417

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1

ghxjiks/ppnul_ft

JSWOOK/pyannote_3_fine_tuning

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ghxjiks/
ppnul_ft