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speaker-segmentation-bengali-optimized-cfg-1-aggressive
lucius-40/speaker-segmentation-bengali-optimized-cfg-1-aggressive
speaker-segmentation-bengali-optimized-cfg-1-aggressive is a machine learning model from lucius-40. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- pyannet
- Access
- Public
- Created
- Feb 10, 2026
- Updated
- Feb 10, 2026
- SHA
- a672be9d
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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-bengali-optimized-cfg-1-aggressive
This model is a fine-tuned version of pyannote/segmentation-3.0 on the bengali-speaker-diarization dataset. It achieves the following results on the evaluation set:
- Loss: 0.6027
- Model Preparation Time: 0.0046
- Der: 0.1598
- False Alarm: 0.0393
- Missed Detection: 0.0332
- Confusion: 0.0873
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: 0.0015
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|---|---|---|---|---|---|---|---|---|
| 0.4211 | 1.0 | 508 | 0.4827 | 0.0046 | 0.1617 | 0.0410 | 0.0173 | 0.1034 |
| 0.4766 | 2.0 | 1016 | 0.4685 | 0.0046 | 0.1602 | 0.0441 | 0.0133 | 0.1028 |
| 0.4205 | 3.0 | 1524 | 0.4872 | 0.0046 | 0.1548 | 0.0400 | 0.0185 | 0.0964 |
| 0.3807 | 4.0 | 2032 | 0.5049 | 0.0046 | 0.1558 | 0.0405 | 0.0250 | 0.0903 |
| 0.3615 | 5.0 | 2540 | 0.4887 | 0.0046 | 0.1477 | 0.0431 | 0.0177 | 0.0869 |
| 0.3785 | 6.0 | 3048 | 0.5197 | 0.0046 | 0.1570 | 0.0366 | 0.0346 | 0.0858 |
| 0.3255 | 7.0 | 3556 | 0.5446 | 0.0046 | 0.1573 | 0.0382 | 0.0307 | 0.0884 |
| 0.3003 | 8.0 | 4064 | 0.6027 | 0.0046 | 0.1598 | 0.0393 | 0.0332 | 0.0873 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4
- Library
- transformers
- Type
- pyannet
- License
- mit
- Languages
- bn
- Created
- Feb 10, 2026
- Updated
- Feb 10, 2026