---
library_name: transformers
language:
- spa
license: mit
base_model: diarizers-community/speaker-segmentation-fine-tuned-callhome-spa
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
should probably proofread and complete it, then remove this comment. -->

# speaker-segmentation-fine-tuned-callhome-spa

This model is a fine-tuned version of [diarizers-community/speaker-segmentation-fine-tuned-callhome-spa](https://huggingface.co/diarizers-community/speaker-segmentation-fine-tuned-callhome-spa) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3513
- Der: 0.2029
- False Alarm: 0.1480
- Missed Detection: 0.0549
- Confusion: 0.0000

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3117        | 1.0   | 281  | 0.3448          | 0.2096 | 0.1526      | 0.0545           | 0.0024    |
| 0.2973        | 2.0   | 562  | 0.3260          | 0.1961 | 0.1359      | 0.0601           | 0.0001    |
| 0.2937        | 3.0   | 843  | 0.3413          | 0.2027 | 0.1468      | 0.0555           | 0.0004    |
| 0.2953        | 4.0   | 1124 | 0.3466          | 0.2023 | 0.1467      | 0.0555           | 0.0000    |
| 0.2725        | 5.0   | 1405 | 0.3513          | 0.2029 | 0.1480      | 0.0549           | 0.0000    |


### Framework versions

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