---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/voxconverse
model-index:
- name: speaker-segmentation-0.2-vox
  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-0.2-vox

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/voxconverse dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2745
- Model Preparation Time: 0.0069
- Der: 0.0853
- False Alarm: 0.0344
- Missed Detection: 0.0236
- Confusion: 0.0273

## 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 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: 15.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2419        | 1.0   | 791   | 0.2628          | 0.0069                 | 0.0909 | 0.0363      | 0.0244           | 0.0302    |
| 0.2356        | 2.0   | 1582  | 0.2557          | 0.0069                 | 0.0904 | 0.0361      | 0.0237           | 0.0306    |
| 0.2129        | 3.0   | 2373  | 0.2555          | 0.0069                 | 0.0864 | 0.0334      | 0.0249           | 0.0280    |
| 0.2085        | 4.0   | 3164  | 0.2436          | 0.0069                 | 0.0849 | 0.0306      | 0.0275           | 0.0269    |
| 0.1946        | 5.0   | 3955  | 0.2571          | 0.0069                 | 0.0857 | 0.0330      | 0.0248           | 0.0278    |
| 0.1863        | 6.0   | 4746  | 0.2503          | 0.0069                 | 0.0860 | 0.0356      | 0.0231           | 0.0273    |
| 0.1763        | 7.0   | 5537  | 0.2526          | 0.0069                 | 0.0858 | 0.0351      | 0.0234           | 0.0273    |
| 0.1783        | 8.0   | 6328  | 0.2571          | 0.0069                 | 0.0857 | 0.0319      | 0.0253           | 0.0285    |
| 0.1704        | 9.0   | 7119  | 0.2582          | 0.0069                 | 0.0861 | 0.0326      | 0.0253           | 0.0281    |
| 0.1719        | 10.0  | 7910  | 0.2626          | 0.0069                 | 0.0862 | 0.0323      | 0.0259           | 0.0281    |
| 0.1625        | 11.0  | 8701  | 0.2713          | 0.0069                 | 0.0860 | 0.0330      | 0.0245           | 0.0284    |
| 0.1668        | 12.0  | 9492  | 0.2753          | 0.0069                 | 0.0857 | 0.0345      | 0.0235           | 0.0277    |
| 0.1579        | 13.0  | 10283 | 0.2741          | 0.0069                 | 0.0850 | 0.0345      | 0.0233           | 0.0271    |
| 0.1609        | 14.0  | 11074 | 0.2748          | 0.0069                 | 0.0851 | 0.0344      | 0.0235           | 0.0272    |
| 0.1637        | 15.0  | 11865 | 0.2745          | 0.0069                 | 0.0853 | 0.0344      | 0.0236           | 0.0273    |


### Framework versions

- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1