| --- |
| library_name: transformers |
| language: |
| - en |
| license: mit |
| base_model: pyannote/speaker-diarization-3.1 |
| tags: |
| - speaker-diarization |
| - speaker-segmentation |
| - generated_from_trainer |
| datasets: |
| - diarizers-community/voxconverse |
| model-index: |
| - name: JSWOOK/pyannote_3_fine_tuning |
| 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. --> |
|
|
| # JSWOOK/pyannote_3_fine_tuning |
| |
| This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/voxconverse dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3134 |
| - Model Preparation Time: 0.0048 |
| - Der: 0.0888 |
| - False Alarm: 0.0134 |
| - Missed Detection: 0.0337 |
| - Confusion: 0.0417 |
| |
| ## 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: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
| | No log | 1.0 | 24 | 0.3180 | 0.0048 | 0.0915 | 0.0119 | 0.0385 | 0.0410 | |
| | 0.1903 | 2.0 | 48 | 0.3116 | 0.0048 | 0.0903 | 0.0125 | 0.0369 | 0.0409 | |
| | 0.1839 | 3.0 | 72 | 0.3089 | 0.0048 | 0.0896 | 0.0128 | 0.0357 | 0.0411 | |
| | 0.1825 | 4.0 | 96 | 0.3176 | 0.0048 | 0.0896 | 0.0131 | 0.0352 | 0.0413 | |
| | 0.1797 | 5.0 | 120 | 0.3148 | 0.0048 | 0.0892 | 0.0132 | 0.0346 | 0.0413 | |
| | 0.1801 | 6.0 | 144 | 0.3141 | 0.0048 | 0.0890 | 0.0133 | 0.0342 | 0.0415 | |
| | 0.1735 | 7.0 | 168 | 0.3137 | 0.0048 | 0.0887 | 0.0134 | 0.0338 | 0.0416 | |
| | 0.1705 | 8.0 | 192 | 0.3133 | 0.0048 | 0.0887 | 0.0134 | 0.0337 | 0.0416 | |
| | 0.1796 | 9.0 | 216 | 0.3133 | 0.0048 | 0.0887 | 0.0134 | 0.0337 | 0.0417 | |
| | 0.1644 | 10.0 | 240 | 0.3134 | 0.0048 | 0.0888 | 0.0134 | 0.0337 | 0.0417 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.44.2 |
| - Pytorch 2.5.0+cu121 |
| - Datasets 3.1.0 |
| - Tokenizers 0.19.1 |
| |
