speaker-segmentation-fine-tuned-hindi

This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the sk001/synthetic-speaker-diarization-dataset-hindi dataset. It achieves the following results on the evaluation set:

• Loss: 0.2921
• Der: 0.1014
• False Alarm: 0.0141
• Missed Detection: 0.0249
• Confusion: 0.0624

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: 64
• eval_batch_size: 64
• 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

Training results

Training Loss	Epoch	Step	Validation Loss	Der	False Alarm	Missed Detection	Confusion
0.3669	1.0	110	0.3843	0.1310	0.0169	0.0300	0.0841
0.3047	2.0	220	0.3198	0.1081	0.0152	0.0269	0.0660
0.2775	3.0	330	0.3062	0.1072	0.0134	0.0277	0.0661
0.2756	4.0	440	0.2921	0.1014	0.0141	0.0249	0.0624
0.2779	5.0	550	0.2923	0.1012	0.0137	0.0252	0.0622

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.8.3
• Tokenizers 0.22.2