---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-eng-2
  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-callhome-eng-2

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4666
- Der: 0.1814
- False Alarm: 0.0552
- Missed Detection: 0.0739
- Confusion: 0.0523

## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4548        | 1.0   | 181  | 0.4943          | 0.1966 | 0.0564      | 0.0811           | 0.0590    |
| 0.4171        | 2.0   | 362  | 0.4845          | 0.1951 | 0.0644      | 0.0754           | 0.0552    |
| 0.396         | 3.0   | 543  | 0.4633          | 0.1856 | 0.0502      | 0.0825           | 0.0529    |
| 0.3856        | 4.0   | 724  | 0.4609          | 0.1843 | 0.0571      | 0.0739           | 0.0534    |
| 0.3693        | 5.0   | 905  | 0.4639          | 0.1821 | 0.0531      | 0.0761           | 0.0528    |
| 0.3634        | 6.0   | 1086 | 0.4610          | 0.1821 | 0.0588      | 0.0716           | 0.0517    |
| 0.3655        | 7.0   | 1267 | 0.4638          | 0.1827 | 0.0566      | 0.0740           | 0.0521    |
| 0.3608        | 8.0   | 1448 | 0.4603          | 0.1814 | 0.0567      | 0.0732           | 0.0515    |
| 0.3545        | 9.0   | 1629 | 0.4645          | 0.1805 | 0.0530      | 0.0761           | 0.0514    |
| 0.3508        | 10.0  | 1810 | 0.4666          | 0.1814 | 0.0552      | 0.0739           | 0.0523    |


### Framework versions

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