This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the diarizers-community/callhome dataset. It achieves the following results on the evaluation set:

Loss: 0.5013
Der: 0.1688
False Alarm: 0.0283
Missed Detection: 0.0343
Confusion: 0.1061

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.475	1.0	194	0.5013	0.1688	0.0283	0.0343	0.1061

Framework versions

Transformers 4.48.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

abhimehra8194/speaker-segmentation-fine-tuned-callhome-hindi-3

speaker-segmentation-fine-tuned-callhome-hi

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

abhimehra8194/
speaker-segmentation-fine-tuned-callhome-hindi-3