---
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-fine-tuned-voxconverse-en
  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-voxconverse-en

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: 1.1250
- Der: 0.8257
- False Alarm: 0.3733
- Missed Detection: 0.3995
- Confusion: 0.0528

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.9302        | 1.0   | 791  | 0.9903          | 0.6790 | 0.5013      | 0.0965           | 0.0812    |
| 0.8848        | 2.0   | 1582 | 1.0536          | 0.7965 | 0.3991      | 0.3409           | 0.0565    |
| 0.8513        | 3.0   | 2373 | 1.0884          | 0.8114 | 0.4017      | 0.3528           | 0.0569    |
| 0.7926        | 4.0   | 3164 | 1.1292          | 0.8378 | 0.3660      | 0.4219           | 0.0500    |
| 0.8147        | 5.0   | 3955 | 1.1250          | 0.8257 | 0.3733      | 0.3995           | 0.0528    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1