---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Khanh17/training-diarization
model-index:
- name: toadam-segmentation-model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# toadam-segmentation-model

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the Khanh17/training-diarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1729
- Model Preparation Time: 0.0037
- Der: 0.0376
- False Alarm: 0.0096
- Missed Detection: 0.0238
- Confusion: 0.0042

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.1961        | 1.0   | 693  | 0.1592          | 0.0037                 | 0.0432 | 0.0075      | 0.0313           | 0.0044    |
| 0.2766        | 2.0   | 1386 | 0.1731          | 0.0037                 | 0.0517 | 0.0065      | 0.0338           | 0.0113    |
| 0.1205        | 3.0   | 2079 | 0.1508          | 0.0037                 | 0.0374 | 0.0058      | 0.0269           | 0.0046    |
| 0.0546        | 4.0   | 2772 | 0.1668          | 0.0037                 | 0.0374 | 0.0075      | 0.0252           | 0.0047    |
| 0.0668        | 5.0   | 3465 | 0.1950          | 0.0037                 | 0.0421 | 0.0086      | 0.0290           | 0.0046    |
| 0.0713        | 6.0   | 4158 | 0.1704          | 0.0037                 | 0.0363 | 0.0088      | 0.0230           | 0.0045    |
| 0.1388        | 7.0   | 4851 | 0.1631          | 0.0037                 | 0.0367 | 0.0087      | 0.0225           | 0.0055    |
| 0.0898        | 8.0   | 5544 | 0.1682          | 0.0037                 | 0.0374 | 0.0098      | 0.0233           | 0.0043    |
| 0.0720        | 9.0   | 6237 | 0.1719          | 0.0037                 | 0.0376 | 0.0094      | 0.0239           | 0.0043    |
| 0.0395        | 10.0  | 6930 | 0.1729          | 0.0037                 | 0.0376 | 0.0096      | 0.0238           | 0.0042    |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2