---
library_name: transformers
language:
- bn
license: mit
base_model: pyannote/segmentation-3.0
tags:
- generated_from_trainer
datasets:
- bengali-speaker-diarization
model-index:
- name: speaker-segmentation-fine-tuned-bn-v2
  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. -->

# speaker-segmentation-fine-tuned-bn-v2

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the bengali-speaker-diarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4496
- Model Preparation Time: 0.0034
- Der: 0.1487
- False Alarm: 0.0385
- Missed Detection: 0.0281
- Confusion: 0.0821

## 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: 32
- eval_batch_size: 32
- seed: 42
- 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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4078        | 1.0   | 153  | 0.4535          | 0.0034                 | 0.1507 | 0.0424      | 0.0168           | 0.0915    |
| 0.3573        | 2.0   | 306  | 0.4361          | 0.0034                 | 0.1501 | 0.0376      | 0.0302           | 0.0822    |
| 0.3413        | 3.0   | 459  | 0.4562          | 0.0034                 | 0.1495 | 0.0405      | 0.0233           | 0.0857    |
| 0.3364        | 4.0   | 612  | 0.4496          | 0.0034                 | 0.1487 | 0.0385      | 0.0281           | 0.0821    |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4