---
library_name: transformers
language:
- spa
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-spa
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speaker-segmentation-fine-tuned-callhome-spa

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5724
- Der: 0.3391
- False Alarm: 0.2612
- Missed Detection: 0.0776
- Confusion: 0.0003

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4184        | 1.0   | 230  | 0.4700          | 0.2893 | 0.2341      | 0.0546           | 0.0006    |
| 0.4075        | 2.0   | 460  | 0.5348          | 0.3197 | 0.2567      | 0.0625           | 0.0005    |
| 0.3941        | 3.0   | 690  | 0.5296          | 0.3134 | 0.2608      | 0.0525           | 0.0001    |
| 0.3902        | 4.0   | 920  | 0.5936          | 0.3624 | 0.2612      | 0.1009           | 0.0003    |
| 0.389         | 5.0   | 1150 | 0.5724          | 0.3391 | 0.2612      | 0.0776           | 0.0003    |


### Framework versions

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