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