---
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-3
  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-3

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: 0.4652
- Der: 0.1821
- False Alarm: 0.0597
- Missed Detection: 0.0715
- Confusion: 0.0509

## 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4563        | 1.0   | 181  | 0.4971          | 0.1973 | 0.0553      | 0.0802           | 0.0617    |
| 0.4053        | 2.0   | 362  | 0.4740          | 0.1899 | 0.0604      | 0.0749           | 0.0546    |
| 0.3833        | 3.0   | 543  | 0.4636          | 0.1854 | 0.0556      | 0.0766           | 0.0531    |
| 0.3738        | 4.0   | 724  | 0.4664          | 0.1830 | 0.0579      | 0.0733           | 0.0518    |
| 0.3596        | 5.0   | 905  | 0.4571          | 0.1800 | 0.0558      | 0.0748           | 0.0494    |
| 0.3533        | 6.0   | 1086 | 0.4671          | 0.1844 | 0.0629      | 0.0685           | 0.0529    |
| 0.3571        | 7.0   | 1267 | 0.4641          | 0.1820 | 0.0594      | 0.0711           | 0.0515    |
| 0.3496        | 8.0   | 1448 | 0.4641          | 0.1824 | 0.0596      | 0.0717           | 0.0511    |
| 0.3449        | 9.0   | 1629 | 0.4636          | 0.1819 | 0.0591      | 0.0718           | 0.0510    |
| 0.3415        | 10.0  | 1810 | 0.4652          | 0.1821 | 0.0597      | 0.0715           | 0.0509    |


### Framework versions

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