---
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: 0.7498
- Der: 0.2258
- False Alarm: 0.0470
- Missed Detection: 0.1339
- Confusion: 0.0449

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5797        | 1.0   | 328  | 0.7543          | 0.2334 | 0.0582      | 0.1277           | 0.0476    |
| 0.5578        | 2.0   | 656  | 0.7684          | 0.2306 | 0.0446      | 0.1401           | 0.0458    |
| 0.5213        | 3.0   | 984  | 0.7569          | 0.2289 | 0.0463      | 0.1368           | 0.0459    |
| 0.4971        | 4.0   | 1312 | 0.7448          | 0.2273 | 0.0489      | 0.1329           | 0.0456    |
| 0.5026        | 5.0   | 1640 | 0.7498          | 0.2258 | 0.0470      | 0.1339           | 0.0449    |


### Framework versions

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