---
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-2
  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-2

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: 0.2428
- Der: 0.0861
- False Alarm: 0.0245
- Missed Detection: 0.0384
- Confusion: 0.0232

## 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2179        | 1.0   | 111  | 0.2259          | 0.0951 | 0.0239      | 0.0486           | 0.0227    |
| 0.1694        | 2.0   | 222  | 0.2379          | 0.0930 | 0.0230      | 0.0466           | 0.0234    |
| 0.1559        | 3.0   | 333  | 0.2305          | 0.0898 | 0.0223      | 0.0431           | 0.0244    |
| 0.149         | 4.0   | 444  | 0.2323          | 0.0893 | 0.0246      | 0.0398           | 0.0249    |
| 0.1416        | 5.0   | 555  | 0.2351          | 0.0884 | 0.0243      | 0.0399           | 0.0243    |
| 0.1369        | 6.0   | 666  | 0.2458          | 0.0904 | 0.0266      | 0.0370           | 0.0268    |
| 0.1367        | 7.0   | 777  | 0.2410          | 0.0882 | 0.0204      | 0.0434           | 0.0244    |
| 0.1306        | 8.0   | 888  | 0.2400          | 0.0866 | 0.0240      | 0.0393           | 0.0234    |
| 0.1301        | 9.0   | 999  | 0.2422          | 0.0860 | 0.0243      | 0.0387           | 0.0230    |
| 0.1276        | 10.0  | 1110 | 0.2428          | 0.0861 | 0.0245      | 0.0384           | 0.0232    |


### Framework versions

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