---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/simsamu
model-index:
- name: speaker-segmentation-fine-tuned-simsamu
  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-simsamu

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2302
- Der: 0.0911
- False Alarm: 0.0236
- Missed Detection: 0.0413
- Confusion: 0.0262

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2179        | 1.0   | 111  | 0.2240          | 0.0964 | 0.0254      | 0.0470           | 0.0240    |
| 0.1678        | 2.0   | 222  | 0.2279          | 0.0943 | 0.0236      | 0.0447           | 0.0260    |
| 0.156         | 3.0   | 333  | 0.2327          | 0.0947 | 0.0222      | 0.0450           | 0.0274    |
| 0.1507        | 4.0   | 444  | 0.2301          | 0.0919 | 0.0237      | 0.0420           | 0.0262    |
| 0.1471        | 5.0   | 555  | 0.2302          | 0.0911 | 0.0236      | 0.0413           | 0.0262    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1