This model is a fine-tuned version of pyannote/segmentation-3.0 on the neyugn/synthetic-diarization-vi-meeting-4-5spk dataset. It achieves the following results on the evaluation set:

Loss: 0.5450
Model Preparation Time: 0.0039
Der: 0.1912
False Alarm: 0.0537
Missed Detection: 0.0352
Confusion: 0.1023

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.6062	1.0	132	0.6128	0.0039	0.2110	0.0521	0.0469	0.1121
0.5287	2.0	264	0.5575	0.0039	0.1987	0.0541	0.0392	0.1054
0.4709	3.0	396	0.5482	0.0039	0.1911	0.0520	0.0385	0.1006
0.4252	4.0	528	0.5501	0.0039	0.1914	0.0535	0.0355	0.1024
0.4234	5.0	660	0.5450	0.0039	0.1912	0.0537	0.0352	0.1023

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

neyugn/vi-meeting-segmentation

vi-meeting-segmentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

neyugn/
vi-meeting-segmentation