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speaker-segmentation-fine-tuned-callhome-eng-5

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metadata
license: mit
base_model: pyannote/segmentation-3.0
tags:
  - speaker-diarization
  - speaker-segmentation
  - generated_from_trainer
datasets:
  - diarizers-community/callhome
model-index:
  - name: speaker-segmentation-fine-tuned-callhome-eng-5
    results: []

speaker-segmentation-fine-tuned-callhome-eng-5

This model is a fine-tuned version of pyannote/segmentation-3.0 on the diarizers-community/callhome eng dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4674
  • Der: 0.1833
  • False Alarm: 0.0583
  • Missed Detection: 0.0725
  • Confusion: 0.0526

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: 0.0003
  • train_batch_size: 64
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Der False Alarm Missed Detection Confusion
0.4679 1.0 181 0.4997 0.2011 0.0620 0.0789 0.0602
0.4255 2.0 362 0.4820 0.1948 0.0604 0.0770 0.0574
0.4084 3.0 543 0.4808 0.1920 0.0598 0.0769 0.0553
0.4017 4.0 724 0.4787 0.1906 0.0584 0.0760 0.0562
0.3911 5.0 905 0.4716 0.1885 0.0572 0.0762 0.0552
0.3845 6.0 1086 0.4676 0.1875 0.0618 0.0718 0.0538
0.3877 7.0 1267 0.4682 0.1877 0.0584 0.0739 0.0555
0.3828 8.0 1448 0.4681 0.1849 0.0579 0.0740 0.0530
0.3768 9.0 1629 0.4645 0.1842 0.0581 0.0733 0.0528
0.3697 10.0 1810 0.4662 0.1838 0.0576 0.0734 0.0529
0.3731 11.0 1991 0.4697 0.1852 0.0607 0.0715 0.0530
0.3691 12.0 2172 0.4642 0.1829 0.0572 0.0734 0.0523
0.3663 13.0 2353 0.4701 0.1854 0.0611 0.0708 0.0535
0.3641 14.0 2534 0.4678 0.1835 0.0591 0.0714 0.0530
0.3631 15.0 2715 0.4655 0.1835 0.0583 0.0724 0.0528
0.3685 16.0 2896 0.4693 0.1838 0.0589 0.0720 0.0529
0.365 17.0 3077 0.4675 0.1836 0.0584 0.0724 0.0528
0.3618 18.0 3258 0.4675 0.1834 0.0582 0.0726 0.0526
0.3651 19.0 3439 0.4675 0.1833 0.0582 0.0725 0.0526
0.3583 20.0 3620 0.4674 0.1833 0.0583 0.0725 0.0526

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.19.1