---
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: 1.1719
- Der: 0.2668
- False Alarm: 0.0225
- Missed Detection: 0.0148
- Confusion: 0.2295

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4449        | 1.0   | 44   | 0.9090          | 0.2891 | 0.0225      | 0.0193           | 0.2473    |
| 0.411         | 2.0   | 88   | 0.9007          | 0.2767 | 0.0225      | 0.0088           | 0.2454    |
| 0.3691        | 3.0   | 132  | 0.8465          | 0.2570 | 0.0225      | 0.0115           | 0.2229    |
| 0.3762        | 4.0   | 176  | 0.8855          | 0.2585 | 0.0225      | 0.0088           | 0.2272    |
| 0.337         | 5.0   | 220  | 0.9608          | 0.2721 | 0.0225      | 0.0142           | 0.2354    |
| 0.3203        | 6.0   | 264  | 1.0052          | 0.2636 | 0.0225      | 0.0152           | 0.2259    |
| 0.314         | 7.0   | 308  | 1.0084          | 0.2650 | 0.0225      | 0.0145           | 0.2279    |
| 0.3066        | 8.0   | 352  | 0.9484          | 0.2614 | 0.0225      | 0.0127           | 0.2262    |
| 0.2968        | 9.0   | 396  | 1.0768          | 0.2720 | 0.0225      | 0.0163           | 0.2332    |
| 0.2847        | 10.0  | 440  | 0.9485          | 0.2528 | 0.0225      | 0.0098           | 0.2205    |
| 0.2784        | 11.0  | 484  | 1.0811          | 0.2677 | 0.0225      | 0.0146           | 0.2306    |
| 0.2674        | 12.0  | 528  | 1.0390          | 0.2670 | 0.0225      | 0.0145           | 0.2300    |
| 0.2646        | 13.0  | 572  | 1.1117          | 0.2666 | 0.0225      | 0.0148           | 0.2293    |
| 0.2425        | 14.0  | 616  | 1.1455          | 0.2682 | 0.0225      | 0.0146           | 0.2310    |
| 0.2569        | 15.0  | 660  | 1.1830          | 0.2682 | 0.0225      | 0.0148           | 0.2309    |
| 0.2497        | 16.0  | 704  | 1.1674          | 0.2673 | 0.0225      | 0.0148           | 0.2300    |
| 0.2494        | 17.0  | 748  | 1.1050          | 0.2630 | 0.0225      | 0.0148           | 0.2257    |
| 0.2334        | 18.0  | 792  | 1.1736          | 0.2674 | 0.0225      | 0.0148           | 0.2301    |
| 0.24          | 19.0  | 836  | 1.1566          | 0.2679 | 0.0225      | 0.0148           | 0.2306    |
| 0.2371        | 20.0  | 880  | 1.1571          | 0.2650 | 0.0225      | 0.0148           | 0.2277    |
| 0.2403        | 21.0  | 924  | 1.1472          | 0.2640 | 0.0225      | 0.0148           | 0.2267    |
| 0.2317        | 22.0  | 968  | 1.1751          | 0.2676 | 0.0225      | 0.0148           | 0.2303    |
| 0.2318        | 23.0  | 1012 | 1.1817          | 0.2677 | 0.0225      | 0.0148           | 0.2304    |
| 0.2322        | 24.0  | 1056 | 1.1723          | 0.2669 | 0.0225      | 0.0148           | 0.2296    |
| 0.2418        | 25.0  | 1100 | 1.1719          | 0.2668 | 0.0225      | 0.0148           | 0.2295    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1