---
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: 0.5595
- Der: 0.2894
- False Alarm: 0.2353
- Missed Detection: 0.0536
- Confusion: 0.0005

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3614        | 1.0   | 226  | 0.4962          | 0.2910 | 0.2389      | 0.0520           | 0.0001    |
| 0.3465        | 2.0   | 452  | 0.5067          | 0.2860 | 0.2179      | 0.0679           | 0.0002    |
| 0.3325        | 3.0   | 678  | 0.5343          | 0.2941 | 0.2300      | 0.0636           | 0.0005    |
| 0.3189        | 4.0   | 904  | 0.5613          | 0.2906 | 0.2380      | 0.0522           | 0.0004    |
| 0.3238        | 5.0   | 1130 | 0.5595          | 0.2894 | 0.2353      | 0.0536           | 0.0005    |


### Framework versions

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