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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
should probably proofread and complete it, then remove this comment. -->
# 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
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