---
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: []
---

<!-- 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-6

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: 0.5147
- 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