---
language:
- spa
license: apache-2.0
base_model: openai/whisper-small
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 [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5174
- Der: 0.1732
- False Alarm: 0.0744
- Missed Detection: 0.0663
- Confusion: 0.0325

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.6346        | 1.0   | 382  | 0.5316          | 0.1789 | 0.0670      | 0.0750           | 0.0369    |
| 0.6117        | 2.0   | 764  | 0.5156          | 0.1724 | 0.0648      | 0.0766           | 0.0311    |
| 0.6017        | 3.0   | 1146 | 0.5150          | 0.1747 | 0.0737      | 0.0680           | 0.0331    |
| 0.6173        | 4.0   | 1528 | 0.5162          | 0.1737 | 0.0748      | 0.0663           | 0.0326    |
| 0.5914        | 5.0   | 1910 | 0.5174          | 0.1732 | 0.0744      | 0.0663           | 0.0325    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1