---
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
  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

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: 0.4823
- Der: 0.1830
- False Alarm: 0.0620
- Missed Detection: 0.0697
- Confusion: 0.0513

## 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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4474        | 1.0   | 181  | 0.4819          | 0.1951 | 0.0640      | 0.0729           | 0.0582    |
| 0.403         | 2.0   | 362  | 0.4771          | 0.1875 | 0.0524      | 0.0789           | 0.0562    |
| 0.389         | 3.0   | 543  | 0.4636          | 0.1827 | 0.0506      | 0.0782           | 0.0539    |
| 0.3715        | 4.0   | 724  | 0.4553          | 0.1811 | 0.0547      | 0.0733           | 0.0531    |
| 0.3722        | 5.0   | 905  | 0.4699          | 0.1833 | 0.0516      | 0.0769           | 0.0548    |
| 0.3625        | 6.0   | 1086 | 0.4677          | 0.1831 | 0.0585      | 0.0714           | 0.0531    |
| 0.351         | 7.0   | 1267 | 0.4729          | 0.1800 | 0.0532      | 0.0750           | 0.0518    |
| 0.3405        | 8.0   | 1448 | 0.4707          | 0.1817 | 0.0550      | 0.0749           | 0.0518    |
| 0.3378        | 9.0   | 1629 | 0.4654          | 0.1820 | 0.0575      | 0.0725           | 0.0519    |
| 0.3312        | 10.0  | 1810 | 0.4745          | 0.1821 | 0.0566      | 0.0739           | 0.0516    |
| 0.3283        | 11.0  | 1991 | 0.4768          | 0.1832 | 0.0618      | 0.0695           | 0.0520    |
| 0.3225        | 12.0  | 2172 | 0.4872          | 0.1850 | 0.0647      | 0.0680           | 0.0523    |
| 0.3205        | 13.0  | 2353 | 0.4840          | 0.1857 | 0.0628      | 0.0692           | 0.0537    |
| 0.3129        | 14.0  | 2534 | 0.4782          | 0.1827 | 0.0644      | 0.0678           | 0.0505    |
| 0.3145        | 15.0  | 2715 | 0.4798          | 0.1817 | 0.0597      | 0.0712           | 0.0507    |
| 0.3109        | 16.0  | 2896 | 0.4803          | 0.1822 | 0.0631      | 0.0685           | 0.0506    |
| 0.3086        | 17.0  | 3077 | 0.4813          | 0.1827 | 0.0622      | 0.0694           | 0.0511    |
| 0.3095        | 18.0  | 3258 | 0.4813          | 0.1830 | 0.0618      | 0.0699           | 0.0513    |
| 0.3109        | 19.0  | 3439 | 0.4823          | 0.1829 | 0.0620      | 0.0697           | 0.0512    |
| 0.3115        | 20.0  | 3620 | 0.4823          | 0.1830 | 0.0620      | 0.0697           | 0.0513    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1