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---
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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: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bangla-segment
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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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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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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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### 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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- 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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