---
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
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# 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: 0.4570
- Der: 0.1803
- False Alarm: 0.0556
- Missed Detection: 0.0731
- Confusion: 0.0516

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4257        | 1.0   | 362  | 0.4789          | 0.1918 | 0.0573      | 0.0786           | 0.0559    |
| 0.3889        | 2.0   | 724  | 0.4660          | 0.1866 | 0.0556      | 0.0760           | 0.0549    |
| 0.3758        | 3.0   | 1086 | 0.4587          | 0.1807 | 0.0548      | 0.0755           | 0.0503    |
| 0.3643        | 4.0   | 1448 | 0.4564          | 0.1805 | 0.0555      | 0.0734           | 0.0515    |
| 0.3511        | 5.0   | 1810 | 0.4570          | 0.1803 | 0.0556      | 0.0731           | 0.0516    |


### Framework versions

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