---
library_name: transformers
language:
- spa
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- pyannote/segmentation
model-index:
- name: segmentation-3.0
  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. -->

# segmentation-3.0

This model is a fine-tuned version of [](https://huggingface.co/) on the pyannote/segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6638
- Der: 0.2932
- False Alarm: 0.2540
- Missed Detection: 0.0387
- Confusion: 0.0004

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3145        | 1.0   | 282  | 0.5751          | 0.2944 | 0.2487      | 0.0454           | 0.0003    |
| 0.3087        | 2.0   | 564  | 0.5957          | 0.2912 | 0.2462      | 0.0440           | 0.0010    |
| 0.2905        | 3.0   | 846  | 0.6614          | 0.2970 | 0.2627      | 0.0333           | 0.0010    |
| 0.2733        | 4.0   | 1128 | 0.6626          | 0.2940 | 0.2558      | 0.0378           | 0.0004    |
| 0.2672        | 5.0   | 1410 | 0.6638          | 0.2932 | 0.2540      | 0.0387           | 0.0004    |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.0