| ---
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| library_name: transformers
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| language:
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| - bn
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| license: mit
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| base_model: pyannote/speaker-diarization-3.1
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| tags:
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| - speaker-diarization
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| - speaker-segmentation
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| - bangla
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| - bengali
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| - pyannote
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| - audio
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| - generated_from_trainer
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| datasets:
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| - Sam3000/speaker-diarization-dataset-bangla
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| model-index:
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| - name: bangla-segment
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| 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. -->
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|
|
| # bangla-segment
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|
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| 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.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.4452
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| - Model Preparation Time: 0.0056
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| - Der: 0.1488
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| - False Alarm: 0.0317
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| - Missed Detection: 0.0372
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| - Confusion: 0.0799
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|
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| ## Model description
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| More information needed
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|
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| ## Intended uses & limitations
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|
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| More information needed
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|
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| ## Training and evaluation data
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| More information needed
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|
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| ## Training procedure
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|
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| ### Training hyperparameters
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| The following hyperparameters were used during training:
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| - learning_rate: 0.001
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| - train_batch_size: 32
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| - eval_batch_size: 32
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| - seed: 42
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| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: cosine
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| - num_epochs: 5
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|
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| ### Training results
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| | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
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| |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
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| | 0.4657 | 1.0 | 170 | 0.4409 | 0.0056 | 0.1506 | 0.0392 | 0.0198 | 0.0916 |
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| | 0.4403 | 2.0 | 340 | 0.4201 | 0.0056 | 0.1507 | 0.0328 | 0.0317 | 0.0861 |
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| | 0.3691 | 3.0 | 510 | 0.4362 | 0.0056 | 0.1485 | 0.0317 | 0.0350 | 0.0818 |
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| | 0.3602 | 4.0 | 680 | 0.4437 | 0.0056 | 0.1493 | 0.0319 | 0.0377 | 0.0797 |
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| | 0.3875 | 5.0 | 850 | 0.4452 | 0.0056 | 0.1488 | 0.0317 | 0.0372 | 0.0799 |
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| ### Framework versions
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|
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| - Transformers 4.46.3
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| - Pytorch 2.4.1+cu118
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| - Datasets 3.1.0
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| - Tokenizers 0.20.3
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|
