---
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-jpn
  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-jpn

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4828
- Der: 0.1446
- False Alarm: 0.0404
- Missed Detection: 0.0606
- Confusion: 0.0435

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5955        | 1.0   | 394  | 0.5366          | 0.1609 | 0.0435      | 0.0706           | 0.0468    |
| 0.5648        | 2.0   | 788  | 0.4979          | 0.1509 | 0.0400      | 0.0646           | 0.0462    |
| 0.5392        | 3.0   | 1182 | 0.4852          | 0.1489 | 0.0447      | 0.0588           | 0.0453    |
| 0.5283        | 4.0   | 1576 | 0.4756          | 0.1442 | 0.0412      | 0.0607           | 0.0422    |
| 0.5109        | 5.0   | 1970 | 0.4828          | 0.1446 | 0.0404      | 0.0606           | 0.0435    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1