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: 0.4778
Model Preparation Time: 0.0043
Der: 0.1599
False Alarm: 0.0403
Missed Detection: 0.0162
Confusion: 0.1034

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: 0.0005
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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: 0.1
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
0.4209	1.0	254	0.4600	0.0043	0.1574	0.0369	0.0185	0.1019
0.391	2.0	508	0.4628	0.0043	0.1586	0.0365	0.0215	0.1007
0.4302	3.0	762	0.4624	0.0043	0.1579	0.0388	0.0179	0.1012
0.3421	4.0	1016	0.4778	0.0043	0.1599	0.0403	0.0162	0.1034

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-conservative

speaker-segmentation-bengali-optimized-conservative

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

lucius-40/
speaker-segmentation-bengali-optimized-conservative