metadata
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: []
bangla-segment
This model is a fine-tuned version of 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