---
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: 0.5957
- Der: 0.1975
- False Alarm: 0.0777
- Missed Detection: 0.0713
- Confusion: 0.0485

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5998        | 1.0   | 336  | 0.6155          | 0.2067 | 0.0726      | 0.0792           | 0.0549    |
| 0.578         | 2.0   | 672  | 0.6258          | 0.2086 | 0.0851      | 0.0691           | 0.0544    |
| 0.5431        | 3.0   | 1008 | 0.6054          | 0.2023 | 0.0830      | 0.0689           | 0.0505    |
| 0.5198        | 4.0   | 1344 | 0.5989          | 0.1984 | 0.0762      | 0.0729           | 0.0494    |
| 0.5211        | 5.0   | 1680 | 0.5957          | 0.1975 | 0.0777      | 0.0713           | 0.0485    |


### Framework versions

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