---
library_name: transformers
language:
- en
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/voxconverse
model-index:
- name: JSWOOK/pyannote_finetuning
  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. -->

# JSWOOK/pyannote_finetuning

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/voxconverse dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1283
- Model Preparation Time: 0.0036
- Der: 0.0490
- False Alarm: 0.0309
- Missed Detection: 0.0091
- Confusion: 0.0090

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| No log        | 1.0   | 21   | 0.1258          | 0.0036                 | 0.0485 | 0.0287      | 0.0105           | 0.0093    |
| 0.228         | 2.0   | 42   | 0.1327          | 0.0036                 | 0.0509 | 0.0300      | 0.0098           | 0.0112    |
| 0.1873        | 3.0   | 63   | 0.1280          | 0.0036                 | 0.0496 | 0.0307      | 0.0092           | 0.0097    |
| 0.166         | 4.0   | 84   | 0.1280          | 0.0036                 | 0.0487 | 0.0307      | 0.0091           | 0.0090    |
| 0.152         | 5.0   | 105  | 0.1283          | 0.0036                 | 0.0490 | 0.0309      | 0.0091           | 0.0090    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1