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

Loss: 0.7545
Der: 0.2264
False Alarm: 0.0488
Missed Detection: 0.1331
Confusion: 0.0445

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: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.562	1.0	328	0.7605	0.2346	0.0492	0.1357	0.0497
0.5564	2.0	656	0.7540	0.2294	0.0512	0.1328	0.0454
0.5273	3.0	984	0.7724	0.2321	0.0438	0.1419	0.0464
0.5037	4.0	1312	0.7599	0.2276	0.0475	0.1349	0.0452
0.5041	5.0	1640	0.7545	0.2264	0.0488	0.1331	0.0445

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

evie-8/speaker-segmentation-fine-tuned-callhome-jpn

speaker-segmentation-fine-tuned-callhome-jpn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

evie-8/
speaker-segmentation-fine-tuned-callhome-jpn