---
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-4
  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-4

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: 0.4660
- Der: 0.1806
- False Alarm: 0.0592
- Missed Detection: 0.0714
- Confusion: 0.0501

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4104        | 1.0   | 362  | 0.4742          | 0.1920 | 0.0615      | 0.0742           | 0.0562    |
| 0.4041        | 2.0   | 724  | 0.4738          | 0.1868 | 0.0620      | 0.0714           | 0.0534    |
| 0.3741        | 3.0   | 1086 | 0.4695          | 0.1851 | 0.0625      | 0.0705           | 0.0521    |
| 0.3612        | 4.0   | 1448 | 0.4689          | 0.1814 | 0.0588      | 0.0707           | 0.0519    |
| 0.3404        | 5.0   | 1810 | 0.4649          | 0.1792 | 0.0580      | 0.0720           | 0.0492    |
| 0.3462        | 6.0   | 2172 | 0.4620          | 0.1812 | 0.0615      | 0.0692           | 0.0505    |
| 0.3296        | 7.0   | 2534 | 0.4631          | 0.1800 | 0.0582      | 0.0713           | 0.0506    |
| 0.3261        | 8.0   | 2896 | 0.4731          | 0.1820 | 0.0586      | 0.0733           | 0.0501    |
| 0.3251        | 9.0   | 3258 | 0.4663          | 0.1811 | 0.0579      | 0.0727           | 0.0506    |
| 0.3154        | 10.0  | 3620 | 0.4660          | 0.1806 | 0.0592      | 0.0714           | 0.0501    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1