---
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: results
  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. -->

# results

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: 0.3941
- Der: 0.2887
- False Alarm: 0.1590
- Missed Detection: 0.1025
- Confusion: 0.0272

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4009        | 1.0   | 747  | 0.4264          | 0.3191 | 0.1921      | 0.0979           | 0.0291    |
| 0.3626        | 2.0   | 1494 | 0.4017          | 0.3027 | 0.1664      | 0.1085           | 0.0278    |
| 0.3527        | 3.0   | 2241 | 0.4077          | 0.2972 | 0.1354      | 0.1347           | 0.0271    |
| 0.3303        | 4.0   | 2988 | 0.3933          | 0.2867 | 0.1506      | 0.1083           | 0.0277    |
| 0.3312        | 5.0   | 3735 | 0.3941          | 0.2887 | 0.1590      | 0.1025           | 0.0272    |


### Framework versions

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