---
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_3_fine_tuning
  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_3_fine_tuning

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: 0.3134
- Model Preparation Time: 0.0048
- Der: 0.0888
- False Alarm: 0.0134
- Missed Detection: 0.0337
- Confusion: 0.0417

## 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: 5e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| No log        | 1.0   | 24   | 0.3180          | 0.0048                 | 0.0915 | 0.0119      | 0.0385           | 0.0410    |
| 0.1903        | 2.0   | 48   | 0.3116          | 0.0048                 | 0.0903 | 0.0125      | 0.0369           | 0.0409    |
| 0.1839        | 3.0   | 72   | 0.3089          | 0.0048                 | 0.0896 | 0.0128      | 0.0357           | 0.0411    |
| 0.1825        | 4.0   | 96   | 0.3176          | 0.0048                 | 0.0896 | 0.0131      | 0.0352           | 0.0413    |
| 0.1797        | 5.0   | 120  | 0.3148          | 0.0048                 | 0.0892 | 0.0132      | 0.0346           | 0.0413    |
| 0.1801        | 6.0   | 144  | 0.3141          | 0.0048                 | 0.0890 | 0.0133      | 0.0342           | 0.0415    |
| 0.1735        | 7.0   | 168  | 0.3137          | 0.0048                 | 0.0887 | 0.0134      | 0.0338           | 0.0416    |
| 0.1705        | 8.0   | 192  | 0.3133          | 0.0048                 | 0.0887 | 0.0134      | 0.0337           | 0.0416    |
| 0.1796        | 9.0   | 216  | 0.3133          | 0.0048                 | 0.0887 | 0.0134      | 0.0337           | 0.0417    |
| 0.1644        | 10.0  | 240  | 0.3134          | 0.0048                 | 0.0888 | 0.0134      | 0.0337           | 0.0417    |


### Framework versions

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