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

Loss: 0.6027
Model Preparation Time: 0.0046
Der: 0.1598
False Alarm: 0.0393
Missed Detection: 0.0332
Confusion: 0.0873

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: 0.0015
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.4211	1.0	508	0.4827	0.0046	0.1617	0.0410	0.0173	0.1034
0.4766	2.0	1016	0.4685	0.0046	0.1602	0.0441	0.0133	0.1028
0.4205	3.0	1524	0.4872	0.0046	0.1548	0.0400	0.0185	0.0964
0.3807	4.0	2032	0.5049	0.0046	0.1558	0.0405	0.0250	0.0903
0.3615	5.0	2540	0.4887	0.0046	0.1477	0.0431	0.0177	0.0869
0.3785	6.0	3048	0.5197	0.0046	0.1570	0.0366	0.0346	0.0858
0.3255	7.0	3556	0.5446	0.0046	0.1573	0.0382	0.0307	0.0884
0.3003	8.0	4064	0.6027	0.0046	0.1598	0.0393	0.0332	0.0873

Framework versions

Transformers 4.48.3
Pytorch 2.8.0+cu126
Datasets 3.6.0
Tokenizers 0.21.4

lucius-40/speaker-segmentation-bengali-optimized-cfg-1-aggressive

speaker-segmentation-bengali-optimized-cfg-1-aggressive

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lucius-40/
speaker-segmentation-bengali-optimized-cfg-1-aggressive