---
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: 0.4585
- Der: 0.1815
- False Alarm: 0.0615
- Missed Detection: 0.0694
- Confusion: 0.0506

## 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 | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3855        | 1.0   | 362  | 0.4769          | 0.1895 | 0.0554      | 0.0764           | 0.0577    |
| 0.3977        | 2.0   | 724  | 0.4610          | 0.1879 | 0.0668      | 0.0693           | 0.0518    |
| 0.3778        | 3.0   | 1086 | 0.4577          | 0.1805 | 0.0597      | 0.0703           | 0.0505    |
| 0.3558        | 4.0   | 1448 | 0.4600          | 0.1812 | 0.0606      | 0.0703           | 0.0503    |
| 0.3335        | 5.0   | 1810 | 0.4585          | 0.1815 | 0.0615      | 0.0694           | 0.0506    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1