---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- KMayanja/backup_and_callhome
model-index:
- name: speaker-segmentation-fine-tuned-merged-backup-uganda-callhome-eng
  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-merged-backup-uganda-callhome-eng

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the KMayanja/backup_and_callhome default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3085
- Der: 0.1123
- False Alarm: 0.0384
- Missed Detection: 0.0378
- Confusion: 0.0361

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3367        | 1.0   | 605  | 0.3336          | 0.1237 | 0.0481      | 0.0369           | 0.0387    |
| 0.3267        | 2.0   | 1210 | 0.3148          | 0.1155 | 0.0416      | 0.0353           | 0.0386    |
| 0.302         | 3.0   | 1815 | 0.3119          | 0.1124 | 0.0394      | 0.0379           | 0.0351    |
| 0.29          | 4.0   | 2420 | 0.3088          | 0.1125 | 0.0393      | 0.0370           | 0.0361    |
| 0.288         | 5.0   | 3025 | 0.3085          | 0.1123 | 0.0384      | 0.0378           | 0.0361    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1