---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callfriend
model-index:
- name: speaker-segmentation-fine-tuned-callfriend-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-callfriend-jpn

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callfriend jpn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6570
- Der: 0.2815
- False Alarm: 0.1035
- Missed Detection: 0.1082
- Confusion: 0.0698

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.6836        | 1.0   | 237  | 0.6465          | 0.2865 | 0.1045      | 0.1095           | 0.0724    |
| 0.6265        | 2.0   | 474  | 0.6455          | 0.2772 | 0.1023      | 0.1062           | 0.0687    |
| 0.6199        | 3.0   | 711  | 0.6615          | 0.2879 | 0.0950      | 0.1161           | 0.0768    |
| 0.6007        | 4.0   | 948  | 0.6574          | 0.2823 | 0.1051      | 0.1066           | 0.0705    |
| 0.5979        | 5.0   | 1185 | 0.6570          | 0.2815 | 0.1035      | 0.1082           | 0.0698    |


### Framework versions

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