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

Loss: 0.3660
Der: 0.1396
False Alarm: 0.0503
Missed Detection: 0.0578
Confusion: 0.0314

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
0.4133	1.0	1427	0.3629	0.1388	0.0424	0.0646	0.0318
0.3907	2.0	2854	0.3638	0.1400	0.0492	0.0583	0.0324
0.3651	3.0	4281	0.3631	0.1403	0.0506	0.0581	0.0316
0.3692	4.0	5708	0.3643	0.1394	0.0489	0.0591	0.0314
0.3484	5.0	7135	0.3660	0.1396	0.0503	0.0578	0.0314

Framework versions

Transformers 4.40.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.19.1

tgrhn/speaker-segmentation-fine-tuned-ami

speaker-segmentation-fine-tuned-ami

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

tgrhn/
speaker-segmentation-fine-tuned-ami