---
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: 0.5146
- Der: 0.1869
- False Alarm: 0.0933
- Missed Detection: 0.0709
- Confusion: 0.0227

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.6043        | 1.0   | 340  | 0.5075          | 0.1789 | 0.0682      | 0.0789           | 0.0318    |
| 0.5766        | 2.0   | 680  | 0.5207          | 0.1951 | 0.1012      | 0.0708           | 0.0230    |
| 0.5345        | 3.0   | 1020 | 0.5011          | 0.1798 | 0.0852      | 0.0716           | 0.0231    |
| 0.518         | 4.0   | 1360 | 0.5344          | 0.1934 | 0.1009      | 0.0700           | 0.0225    |
| 0.5147        | 5.0   | 1700 | 0.5146          | 0.1869 | 0.0933      | 0.0709           | 0.0227    |


### Framework versions

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