---
library_name: transformers
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-simsamu-fra
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speaker-segmentation-simsamu-fra

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2243
- Model Preparation Time: 0.0033
- Der: 0.0906
- False Alarm: 0.0239
- Missed Detection: 0.0427
- Confusion: 0.0240

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| No log        | 1.0   | 56   | 0.2303          | 0.0033                 | 0.0985 | 0.0298      | 0.0430           | 0.0257    |
| 0.2116        | 2.0   | 112  | 0.2301          | 0.0033                 | 0.0968 | 0.0218      | 0.0524           | 0.0227    |
| 0.2116        | 3.0   | 168  | 0.2247          | 0.0033                 | 0.0923 | 0.0230      | 0.0462           | 0.0231    |
| 0.1681        | 4.0   | 224  | 0.2244          | 0.0033                 | 0.0909 | 0.0246      | 0.0424           | 0.0240    |
| 0.1681        | 5.0   | 280  | 0.2243          | 0.0033                 | 0.0906 | 0.0239      | 0.0427           | 0.0240    |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.6.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1