---
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-deu
  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-deu

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome deu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3780
- Der: 0.1415
- False Alarm: 0.0724
- Missed Detection: 0.0490
- Confusion: 0.0201

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4622        | 1.0   | 330  | 0.3844          | 0.1439 | 0.0653      | 0.0562           | 0.0223    |
| 0.4306        | 2.0   | 660  | 0.4004          | 0.1519 | 0.0763      | 0.0515           | 0.0241    |
| 0.4069        | 3.0   | 990  | 0.3775          | 0.1407 | 0.0707      | 0.0496           | 0.0204    |
| 0.3949        | 4.0   | 1320 | 0.3771          | 0.1408 | 0.0710      | 0.0498           | 0.0200    |
| 0.3879        | 5.0   | 1650 | 0.3780          | 0.1415 | 0.0724      | 0.0490           | 0.0201    |


### Framework versions

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