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/callhome
+model-index:
+- name: speaker-segmentation-fine-tuned-callhome-eng
+  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-callhome-eng
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4654
+- Der: 0.1832
+- False Alarm: 0.0599
+- Missed Detection: 0.0724
+- Confusion: 0.0508
+
+## 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: 64
+- eval_batch_size: 64
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4455        | 1.0   | 181  | 0.4776          | 0.1948 | 0.0699      | 0.0691           | 0.0558    |
+| 0.4049        | 2.0   | 362  | 0.4746          | 0.1916 | 0.0590      | 0.0763           | 0.0562    |
+| 0.3856        | 3.0   | 543  | 0.4631          | 0.1843 | 0.0565      | 0.0754           | 0.0524    |
+| 0.3796        | 4.0   | 724  | 0.4634          | 0.1834 | 0.0593      | 0.0726           | 0.0515    |
+| 0.3727        | 5.0   | 905  | 0.4654          | 0.1832 | 0.0599      | 0.0724           | 0.0508    |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

model.safetensors

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