---
library_name: transformers
language:
- bn
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- bangla
- bengali
- pyannote
- audio
- generated_from_trainer
datasets:
- Sam3000/speaker-diarization-dataset-bangla
model-index:
- name: bangla-segment
  results: []
---

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

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the Sam3000/speaker-diarization-dataset-bangla dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4452
- Model Preparation Time: 0.0056
- Der: 0.1488
- False Alarm: 0.0317
- Missed Detection: 0.0372
- Confusion: 0.0799

## 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 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4657        | 1.0   | 170  | 0.4409          | 0.0056                 | 0.1506 | 0.0392      | 0.0198           | 0.0916    |
| 0.4403        | 2.0   | 340  | 0.4201          | 0.0056                 | 0.1507 | 0.0328      | 0.0317           | 0.0861    |
| 0.3691        | 3.0   | 510  | 0.4362          | 0.0056                 | 0.1485 | 0.0317      | 0.0350           | 0.0818    |
| 0.3602        | 4.0   | 680  | 0.4437          | 0.0056                 | 0.1493 | 0.0319      | 0.0377           | 0.0797    |
| 0.3875        | 5.0   | 850  | 0.4452          | 0.0056                 | 0.1488 | 0.0317      | 0.0372           | 0.0799    |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3