speaker-segmentation-fine-tuned-datasetID-hugging_2_4_updated_01

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

  • Loss: 0.3877
  • Model Preparation Time: 0.0041
  • Der: 0.1275
  • False Alarm: 0.0211
  • Missed Detection: 0.0097
  • Confusion: 0.0968

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: 64
  • eval_batch_size: 64
  • seed: 42
  • 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: 15

Training results

Training Loss Epoch Step Validation Loss Model Preparation Time Der False Alarm Missed Detection Confusion
0.5397 1.0 285 0.5425 0.0041 0.1807 0.0266 0.0139 0.1402
0.4678 2.0 570 0.4776 0.0041 0.1626 0.0221 0.0123 0.1283
0.4275 3.0 855 0.4479 0.0041 0.1488 0.0218 0.0103 0.1168
0.4065 4.0 1140 0.4242 0.0041 0.1416 0.0213 0.0103 0.1100
0.4065 5.0 1425 0.4137 0.0041 0.1374 0.0215 0.0101 0.1058
0.3939 6.0 1710 0.4140 0.0041 0.1373 0.0211 0.0102 0.1060
0.3581 7.0 1995 0.3972 0.0041 0.1328 0.0219 0.0094 0.1015
0.3589 8.0 2280 0.3983 0.0041 0.1327 0.0214 0.0099 0.1015
0.3575 9.0 2565 0.3969 0.0041 0.1318 0.0213 0.0097 0.1008
0.3639 10.0 2850 0.3899 0.0041 0.1273 0.0211 0.0097 0.0965
0.3417 11.0 3135 0.3891 0.0041 0.1284 0.0210 0.0098 0.0976
0.3475 12.0 3420 0.3898 0.0041 0.1278 0.0211 0.0097 0.0970
0.3212 13.0 3705 0.3899 0.0041 0.1280 0.0211 0.0097 0.0973
0.3384 14.0 3990 0.3885 0.0041 0.1277 0.0211 0.0097 0.0969
0.3258 15.0 4275 0.3877 0.0041 0.1275 0.0211 0.0097 0.0968

Framework versions

  • Transformers 4.50.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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