---
library_name: transformers
language:
- jpn
license: mit
base_model: pyannote/speaker-diarization-3.1
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
should probably proofread and complete it, then remove this comment. -->

# speaker-segmentation-fine-tuned-callhome-jpn

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4370
- Model Preparation Time: 0.0038
- Der: 0.1404
- False Alarm: 0.0234
- Missed Detection: 0.0272
- Confusion: 0.0898

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4567        | 1.0   | 194  | 0.4782          | 0.0038                 | 0.1572 | 0.0219      | 0.0351           | 0.1003    |
| 0.384         | 2.0   | 388  | 0.4576          | 0.0038                 | 0.1483 | 0.0221      | 0.0295           | 0.0968    |
| 0.3582        | 3.0   | 582  | 0.4375          | 0.0038                 | 0.1402 | 0.0230      | 0.0284           | 0.0888    |
| 0.357         | 4.0   | 776  | 0.4406          | 0.0038                 | 0.1413 | 0.0229      | 0.0277           | 0.0907    |
| 0.3406        | 5.0   | 970  | 0.4370          | 0.0038                 | 0.1404 | 0.0234      | 0.0272           | 0.0898    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1