---
library_name: transformers
language:
- jpn
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
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# 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: 0.3412
- Der: 0.1116
- False Alarm: 0.0194
- Missed Detection: 0.0267
- Confusion: 0.0655

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3963        | 1.0   | 755  | 0.4401          | 0.1468 | 0.0203      | 0.0353           | 0.0912    |
| 0.3814        | 2.0   | 1510 | 0.3740          | 0.1222 | 0.0185      | 0.0310           | 0.0727    |
| 0.3112        | 3.0   | 2265 | 0.3522          | 0.1192 | 0.0209      | 0.0263           | 0.0720    |
| 0.3186        | 4.0   | 3020 | 0.3417          | 0.1127 | 0.0188      | 0.0275           | 0.0664    |
| 0.3093        | 5.0   | 3775 | 0.3412          | 0.1116 | 0.0194      | 0.0267           | 0.0655    |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0