---
license: mit
base_model: pyannote/segmentation-3.0
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/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7433
- Der: 0.2234
- False Alarm: 0.0478
- Missed Detection: 0.1328
- Confusion: 0.0428

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5771        | 1.0   | 328  | 0.7534          | 0.2321 | 0.0564      | 0.1261           | 0.0496    |
| 0.5388        | 2.0   | 656  | 0.7503          | 0.2261 | 0.0485      | 0.1347           | 0.0429    |
| 0.5061        | 3.0   | 984  | 0.7486          | 0.2248 | 0.0475      | 0.1350           | 0.0423    |
| 0.4883        | 4.0   | 1312 | 0.7374          | 0.2227 | 0.0492      | 0.1315           | 0.0421    |
| 0.493         | 5.0   | 1640 | 0.7433          | 0.2234 | 0.0478      | 0.1328           | 0.0428    |


### Framework versions

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