---
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: 0.4654
- Der: 0.1832
- False Alarm: 0.0599
- Missed Detection: 0.0724
- Confusion: 0.0508

## 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: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4455        | 1.0   | 181  | 0.4776          | 0.1948 | 0.0699      | 0.0691           | 0.0558    |
| 0.4049        | 2.0   | 362  | 0.4746          | 0.1916 | 0.0590      | 0.0763           | 0.0562    |
| 0.3856        | 3.0   | 543  | 0.4631          | 0.1843 | 0.0565      | 0.0754           | 0.0524    |
| 0.3796        | 4.0   | 724  | 0.4634          | 0.1834 | 0.0593      | 0.0726           | 0.0515    |
| 0.3727        | 5.0   | 905  | 0.4654          | 0.1832 | 0.0599      | 0.0724           | 0.0508    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1