End of training

README.md

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+---
+license: mit
+base_model: pyannote/segmentation-3.0
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/simsamu
+model-index:
+- name: speaker-segmentation-fine-tuned-simsamu
+  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. -->
+
+# speaker-segmentation-fine-tuned-simsamu
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu default dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2302
+- Der: 0.0911
+- False Alarm: 0.0236
+- Missed Detection: 0.0413
+- Confusion: 0.0262
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.2179        | 1.0   | 111  | 0.2240          | 0.0964 | 0.0254      | 0.0470           | 0.0240    |
+| 0.1678        | 2.0   | 222  | 0.2279          | 0.0943 | 0.0236      | 0.0447           | 0.0260    |
+| 0.156         | 3.0   | 333  | 0.2327          | 0.0947 | 0.0222      | 0.0450           | 0.0274    |
+| 0.1507        | 4.0   | 444  | 0.2301          | 0.0919 | 0.0237      | 0.0420           | 0.0262    |
+| 0.1471        | 5.0   | 555  | 0.2302          | 0.0911 | 0.0236      | 0.0413           | 0.0262    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.19.1

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