---
library_name: transformers
language:
- fr
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- ESLO2
model-index:
- name: speaker-segmentation-fine-tuned_ESLO2
  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_ESLO2

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the ESLO2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0390
- Model Preparation Time: 0.004
- Der: 0.5468
- False Alarm: 0.1928
- Missed Detection: 0.2690
- Confusion: 0.0849

## 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 | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 1.0681        | 1.0   | 686  | 1.0642          | 0.004                  | 0.5632 | 0.1931      | 0.2781           | 0.0921    |
| 1.0147        | 2.0   | 1372 | 1.0523          | 0.004                  | 0.5551 | 0.1887      | 0.2772           | 0.0893    |
| 1.0044        | 3.0   | 2058 | 1.0445          | 0.004                  | 0.5537 | 0.1887      | 0.2791           | 0.0859    |
| 0.9801        | 4.0   | 2744 | 1.0352          | 0.004                  | 0.5464 | 0.1934      | 0.2677           | 0.0853    |
| 0.9866        | 5.0   | 3430 | 1.0390          | 0.004                  | 0.5468 | 0.1928      | 0.2690           | 0.0849    |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0