---
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
  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

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: 0.4602
- Der: 0.1828
- False Alarm: 0.0584
- Missed Detection: 0.0717
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4123        | 1.0   | 362  | 0.4801          | 0.1930 | 0.0627      | 0.0741           | 0.0563    |
| 0.3906        | 2.0   | 724  | 0.4558          | 0.1836 | 0.0589      | 0.0727           | 0.0519    |
| 0.3753        | 3.0   | 1086 | 0.4643          | 0.1830 | 0.0557      | 0.0746           | 0.0527    |
| 0.3632        | 4.0   | 1448 | 0.4566          | 0.1821 | 0.0564      | 0.0728           | 0.0529    |
| 0.3475        | 5.0   | 1810 | 0.4602          | 0.1828 | 0.0584      | 0.0717           | 0.0528    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1