Toadam Segmentation Model

--- library_name: transformers license: mit base_model: pyannote/segmentation-3.0 tags: - speaker-diarization - speaker-segmentation - generated_from_trainer datasets: - Khanh17/toadam-diarization-chunked model-index: - name: toadam-segmentation-model results: [] --- # 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/toadam-diarization-chunked dataset. It achieves the following results on the evaluation set: - Loss: 0.7110 - Model Preparation Time: 0.0008 - Der: 0.1902 - False Alarm: 0.0604 - Missed Detection: 0.1213 - Confusion: 0.0086 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| | 0.6269 | 1.0 | 118 | 0.6484 | 0.0008 | 0.2334 | 0.0387 | 0.1853 | 0.0094 | | 0.4062 | 2.0 | 236 | 0.6752 | 0.0008 | 0.2026 | 0.0717 | 0.1228 | 0.0082 | | 0.4725 | 3.0 | 354 | 0.6490 | 0.0008 | 0.1892 | 0.0382 | 0.1417 | 0.0093 | | 0.3943 | 4.0 | 472 | 0.5823 | 0.0008 | 0.1798 | 0.0405 | 0.1311 | 0.0082 | | 0.2983 | 5.0 | 590 | 0.7464 | 0.0008 | 0.1936 | 0.0615 | 0.1266 | 0.0054 | | 0.2153 | 6.0 | 708 | 0.8112 | 0.0008 | 0.2258 | 0.1253 | 0.0966 | 0.0039 | | 0.2120 | 7.0 | 826 | 0.7329 | 0.0008 | 0.1789 | 0.0427 | 0.1283 | 0.0079 | | 0.2245 | 8.0 | 944 | 0.6899 | 0.0008 | 0.1823 | 0.0501 | 0.1221 | 0.0100 | | 0.2454 | 9.0 | 1062 | 0.7033 | 0.0008 | 0.1888 | 0.0596 | 0.1213 | 0.0079 | | 0.2478 | 10.0 | 1180 | 0.7110 | 0.0008 | 0.1902 | 0.0604 | 0.1213 | 0.0086 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cpu - Datasets 4.8.5 - Tokenizers 0.22.2