---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- ArtFair/diarizers_dataset_70-15-15
model-index:
- name: fine_tuned_segmentation-3.0_1e-3_128_pth
  results: []
---

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# fine_tuned_segmentation-3.0_1e-3_128_pth

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the ArtFair/diarizers_dataset_70-15-15 default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3620
- Der: 0.2625
- False Alarm: 0.1458
- Missed Detection: 0.0926
- Confusion: 0.0241

## 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: 128
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.426         | 1.0   | 233  | 0.3954          | 0.2915 | 0.1834      | 0.0807           | 0.0274    |
| 0.3974        | 2.0   | 466  | 0.3667          | 0.2668 | 0.1391      | 0.1032           | 0.0246    |
| 0.3772        | 3.0   | 699  | 0.3675          | 0.2672 | 0.1552      | 0.0874           | 0.0246    |
| 0.3618        | 4.0   | 932  | 0.3629          | 0.2641 | 0.1498      | 0.0899           | 0.0243    |
| 0.3622        | 5.0   | 1165 | 0.3620          | 0.2625 | 0.1458      | 0.0926           | 0.0241    |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.4.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2