| --- |
| library_name: transformers |
| license: mit |
| base_model: pyannote/segmentation-3.0 |
| tags: |
| - speaker-diarization |
| - speaker-segmentation |
| - generated_from_trainer |
| datasets: |
| - Khanh17/training-diarization |
| model-index: |
| - name: toadam-segmentation-model |
| 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. --> |
|
|
| # toadam-segmentation-model |
|
|
| This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the Khanh17/training-diarization dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1729 |
| - Model Preparation Time: 0.0037 |
| - Der: 0.0376 |
| - False Alarm: 0.0096 |
| - Missed Detection: 0.0238 |
| - Confusion: 0.0042 |
|
|
| ## 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: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - num_epochs: 10.0 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
| | 0.1961 | 1.0 | 693 | 0.1592 | 0.0037 | 0.0432 | 0.0075 | 0.0313 | 0.0044 | |
| | 0.2766 | 2.0 | 1386 | 0.1731 | 0.0037 | 0.0517 | 0.0065 | 0.0338 | 0.0113 | |
| | 0.1205 | 3.0 | 2079 | 0.1508 | 0.0037 | 0.0374 | 0.0058 | 0.0269 | 0.0046 | |
| | 0.0546 | 4.0 | 2772 | 0.1668 | 0.0037 | 0.0374 | 0.0075 | 0.0252 | 0.0047 | |
| | 0.0668 | 5.0 | 3465 | 0.1950 | 0.0037 | 0.0421 | 0.0086 | 0.0290 | 0.0046 | |
| | 0.0713 | 6.0 | 4158 | 0.1704 | 0.0037 | 0.0363 | 0.0088 | 0.0230 | 0.0045 | |
| | 0.1388 | 7.0 | 4851 | 0.1631 | 0.0037 | 0.0367 | 0.0087 | 0.0225 | 0.0055 | |
| | 0.0898 | 8.0 | 5544 | 0.1682 | 0.0037 | 0.0374 | 0.0098 | 0.0233 | 0.0043 | |
| | 0.0720 | 9.0 | 6237 | 0.1719 | 0.0037 | 0.0376 | 0.0094 | 0.0239 | 0.0043 | |
| | 0.0395 | 10.0 | 6930 | 0.1729 | 0.0037 | 0.0376 | 0.0096 | 0.0238 | 0.0042 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
