---
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
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# 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