---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- amitysolution/sample-voice-dataset
model-index:
- name: amity-diarization-v02
  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. -->

# amity-diarization-v02

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the amitysolution/sample-voice-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3846
- Model Preparation Time: 0.0077
- Der: 0.1686
- False Alarm: 0.0769
- Missed Detection: 0.0777
- Confusion: 0.0140

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 15.0

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.7157        | 0.6173  | 300  | 0.5912          | 0.0077                 | 0.2733 | 0.0783      | 0.1634           | 0.0315    |
| 0.5807        | 1.2346  | 600  | 0.5261          | 0.0077                 | 0.2517 | 0.0783      | 0.1451           | 0.0283    |
| 0.5114        | 1.8519  | 900  | 0.4810          | 0.0077                 | 0.2299 | 0.0841      | 0.1207           | 0.0250    |
| 0.4601        | 2.4691  | 1200 | 0.4629          | 0.0077                 | 0.2098 | 0.0910      | 0.0960           | 0.0227    |
| 0.426         | 3.0864  | 1500 | 0.4443          | 0.0077                 | 0.2016 | 0.0909      | 0.0894           | 0.0214    |
| 0.4077        | 3.7037  | 1800 | 0.4391          | 0.0077                 | 0.1946 | 0.0866      | 0.0888           | 0.0192    |
| 0.3818        | 4.3210  | 2100 | 0.4287          | 0.0077                 | 0.1891 | 0.0863      | 0.0839           | 0.0189    |
| 0.3687        | 4.9383  | 2400 | 0.4214          | 0.0077                 | 0.1848 | 0.0838      | 0.0821           | 0.0188    |
| 0.357         | 5.5556  | 2700 | 0.4135          | 0.0077                 | 0.1802 | 0.0849      | 0.0777           | 0.0175    |
| 0.3533        | 6.1728  | 3000 | 0.4106          | 0.0077                 | 0.1768 | 0.0796      | 0.0809           | 0.0163    |
| 0.3357        | 6.7901  | 3300 | 0.3981          | 0.0077                 | 0.1732 | 0.0821      | 0.0754           | 0.0157    |
| 0.3317        | 7.4074  | 3600 | 0.3957          | 0.0077                 | 0.1724 | 0.0800      | 0.0777           | 0.0146    |
| 0.3278        | 8.0247  | 3900 | 0.3884          | 0.0077                 | 0.1710 | 0.0800      | 0.0761           | 0.0148    |
| 0.3193        | 8.6420  | 4200 | 0.3859          | 0.0077                 | 0.1696 | 0.0787      | 0.0765           | 0.0144    |
| 0.3218        | 9.2593  | 4500 | 0.3842          | 0.0077                 | 0.1687 | 0.0790      | 0.0755           | 0.0142    |
| 0.3244        | 9.8765  | 4800 | 0.3795          | 0.0077                 | 0.1674 | 0.0781      | 0.0751           | 0.0142    |
| 0.3121        | 10.4938 | 5100 | 0.3827          | 0.0077                 | 0.1685 | 0.0762      | 0.0780           | 0.0144    |
| 0.31          | 11.1111 | 5400 | 0.3825          | 0.0077                 | 0.1688 | 0.0768      | 0.0779           | 0.0140    |
| 0.3131        | 11.7284 | 5700 | 0.3855          | 0.0077                 | 0.1688 | 0.0772      | 0.0775           | 0.0141    |
| 0.3108        | 12.3457 | 6000 | 0.3836          | 0.0077                 | 0.1685 | 0.0772      | 0.0773           | 0.0141    |
| 0.3093        | 12.9630 | 6300 | 0.3853          | 0.0077                 | 0.1687 | 0.0769      | 0.0779           | 0.0139    |
| 0.3131        | 13.5802 | 6600 | 0.3855          | 0.0077                 | 0.1688 | 0.0767      | 0.0782           | 0.0139    |
| 0.3012        | 14.1975 | 6900 | 0.3847          | 0.0077                 | 0.1687 | 0.0769      | 0.0777           | 0.0140    |
| 0.3108        | 14.8148 | 7200 | 0.3846          | 0.0077                 | 0.1686 | 0.0769      | 0.0777           | 0.0140    |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1