---
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
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/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: 0.7512
- Der: 0.2263
- False Alarm: 0.0467
- Missed Detection: 0.1359
- Confusion: 0.0437

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5796        | 1.0   | 328  | 0.7613          | 0.2340 | 0.0514      | 0.1341           | 0.0484    |
| 0.5518        | 2.0   | 656  | 0.7646          | 0.2328 | 0.0480      | 0.1391           | 0.0458    |
| 0.5304        | 3.0   | 984  | 0.7723          | 0.2321 | 0.0426      | 0.1421           | 0.0474    |
| 0.5043        | 4.0   | 1312 | 0.7504          | 0.2272 | 0.0490      | 0.1336           | 0.0446    |
| 0.5086        | 5.0   | 1640 | 0.7512          | 0.2263 | 0.0467      | 0.1359           | 0.0437    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.19.1