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---
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
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