This model is a fine-tuned version of pyannote/segmentation-3.0 on the Venkatesh4342/pyannote-hindi-diarization dataset. It achieves the following results on the evaluation set:

Loss: 0.0767
Model Preparation Time: 0.0076
Der: 0.0269
False Alarm: 0.0116
Missed Detection: 0.0113
Confusion: 0.0040

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 adamw_torch 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
0.1348	1.0	2674	0.1155	0.0076	0.0404	0.0167	0.0145	0.0093
0.0936	2.0	5348	0.0908	0.0076	0.0319	0.0111	0.0155	0.0053
0.0838	3.0	8022	0.0803	0.0076	0.0283	0.0111	0.0126	0.0045
0.0751	4.0	10696	0.0779	0.0076	0.0275	0.0122	0.0111	0.0042
0.0871	5.0	13370	0.0767	0.0076	0.0269	0.0116	0.0113	0.0040

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Venkatesh4342/speaker-segmentation-fine-tuned-hi

speaker-segmentation-fine-tuned-hi

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Venkatesh4342/
speaker-segmentation-fine-tuned-hi