---
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: 0.7545
- Der: 0.2264
- False Alarm: 0.0488
- Missed Detection: 0.1331
- Confusion: 0.0445

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.562         | 1.0   | 328  | 0.7605          | 0.2346 | 0.0492      | 0.1357           | 0.0497    |
| 0.5564        | 2.0   | 656  | 0.7540          | 0.2294 | 0.0512      | 0.1328           | 0.0454    |
| 0.5273        | 3.0   | 984  | 0.7724          | 0.2321 | 0.0438      | 0.1419           | 0.0464    |
| 0.5037        | 4.0   | 1312 | 0.7599          | 0.2276 | 0.0475      | 0.1349           | 0.0452    |
| 0.5041        | 5.0   | 1640 | 0.7545          | 0.2264 | 0.0488      | 0.1331           | 0.0445    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1