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

Loss: 0.3085
Der: 0.1123
False Alarm: 0.0384
Missed Detection: 0.0378
Confusion: 0.0361

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
0.3367	1.0	605	0.3336	0.1237	0.0481	0.0369	0.0387
0.3267	2.0	1210	0.3148	0.1155	0.0416	0.0353	0.0386
0.302	3.0	1815	0.3119	0.1124	0.0394	0.0379	0.0351
0.29	4.0	2420	0.3088	0.1125	0.0393	0.0370	0.0361
0.288	5.0	3025	0.3085	0.1123	0.0384	0.0378	0.0361

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

KMayanja/speaker-segmentation-fine-tuned-merged-backup-uganda-callhome-eng

speaker-segmentation-fine-tuned-merged-backup-uganda-callhome-eng

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

KMayanja/
speaker-segmentation-fine-tuned-merged-backup-uganda-callhome-eng