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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speaker-segmentation-fine-tuned-callhome-eng-5
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/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
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