---
library_name: transformers
language:
- fr
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- CAENNAIS
model-index:
- name: speaker-segmentation-fine-tuned_CAENNAIS
  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_CAENNAIS

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the CAENNAIS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7201
- Model Preparation Time: 0.0037
- Der: 0.3164
- False Alarm: 0.1145
- Missed Detection: 0.0684
- Confusion: 0.1335

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.8687        | 1.0   | 61   | 0.8189          | 0.0037                 | 0.3927 | 0.1128      | 0.0900           | 0.1899    |
| 0.7991        | 2.0   | 122  | 0.7751          | 0.0037                 | 0.3552 | 0.1158      | 0.0750           | 0.1644    |
| 0.7269        | 3.0   | 183  | 0.7795          | 0.0037                 | 0.3571 | 0.1269      | 0.0622           | 0.1680    |
| 0.6891        | 4.0   | 244  | 0.7384          | 0.0037                 | 0.3360 | 0.1233      | 0.0642           | 0.1485    |
| 0.6723        | 5.0   | 305  | 0.7053          | 0.0037                 | 0.3235 | 0.1017      | 0.0807           | 0.1411    |
| 0.6409        | 6.0   | 366  | 0.7068          | 0.0037                 | 0.3193 | 0.1142      | 0.0680           | 0.1372    |
| 0.6345        | 7.0   | 427  | 0.7200          | 0.0037                 | 0.3243 | 0.1150      | 0.0695           | 0.1398    |
| 0.6087        | 8.0   | 488  | 0.7483          | 0.0037                 | 0.3302 | 0.1241      | 0.0623           | 0.1438    |
| 0.5997        | 9.0   | 549  | 0.7163          | 0.0037                 | 0.3158 | 0.1139      | 0.0689           | 0.1330    |
| 0.5976        | 10.0  | 610  | 0.7201          | 0.0037                 | 0.3164 | 0.1145      | 0.0684           | 0.1335    |


### Framework versions

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