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speaker-segmentation-fine-tuned-callhome-eng-6

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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-6
    results: []

speaker-segmentation-fine-tuned-callhome-eng-6

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: 0.5147
  • Der: 0.1839
  • False Alarm: 0.0668
  • Missed Detection: 0.0694
  • Confusion: 0.0477

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: 0.002
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Der False Alarm Missed Detection Confusion
0.4083 1.0 362 0.4880 0.1967 0.0505 0.0840 0.0621
0.3919 2.0 724 0.4688 0.1852 0.0608 0.0717 0.0527
0.3708 3.0 1086 0.4637 0.1846 0.0581 0.0738 0.0527
0.3549 4.0 1448 0.4636 0.1809 0.0585 0.0689 0.0535
0.3299 5.0 1810 0.4727 0.1835 0.0587 0.0699 0.0549
0.3457 6.0 2172 0.4727 0.1861 0.0654 0.0672 0.0535
0.3241 7.0 2534 0.4921 0.1835 0.0621 0.0701 0.0513
0.3116 8.0 2896 0.4859 0.1839 0.0647 0.0677 0.0515
0.304 9.0 3258 0.4639 0.1788 0.0571 0.0718 0.0499
0.2896 10.0 3620 0.4844 0.1826 0.0659 0.0676 0.0490
0.2853 11.0 3982 0.4696 0.1787 0.0521 0.0777 0.0489
0.2831 12.0 4344 0.4858 0.1831 0.0662 0.0684 0.0484
0.2746 13.0 4706 0.4799 0.1828 0.0639 0.0703 0.0486
0.2685 14.0 5068 0.4951 0.1847 0.0658 0.0695 0.0494
0.2627 15.0 5430 0.5042 0.1829 0.0627 0.0713 0.0489
0.2551 16.0 5792 0.5066 0.1839 0.0671 0.0682 0.0486
0.2509 17.0 6154 0.5126 0.1854 0.0690 0.0695 0.0469
0.2502 18.0 6516 0.5196 0.1861 0.0676 0.0695 0.0490
0.247 19.0 6878 0.5187 0.1844 0.0670 0.0698 0.0476
0.2417 20.0 7240 0.5147 0.1839 0.0668 0.0694 0.0477

Framework versions

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