---
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_32
  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. -->

# fine_tuned_segmentation-3.0_1e-3_32

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: 0.3590
- Der: 0.2602
- False Alarm: 0.1497
- Missed Detection: 0.0864
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.392         | 1.0   | 929  | 0.3807          | 0.2790 | 0.1647      | 0.0865           | 0.0278    |
| 0.3543        | 2.0   | 1858 | 0.3680          | 0.2685 | 0.1530      | 0.0889           | 0.0266    |
| 0.3494        | 3.0   | 2787 | 0.3526          | 0.2567 | 0.1427      | 0.0901           | 0.0238    |
| 0.3287        | 4.0   | 3716 | 0.3592          | 0.2605 | 0.1496      | 0.0869           | 0.0240    |
| 0.3339        | 5.0   | 4645 | 0.3590          | 0.2602 | 0.1497      | 0.0864           | 0.0241    |


### Framework versions

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