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: 0.4592
+- Der: 0.1801
+- False Alarm: 0.0585
+- Missed Detection: 0.0704
+- Confusion: 0.0512
+
+## 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: 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: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4101        | 1.0   | 362  | 0.4686          | 0.1875 | 0.0544      | 0.0788           | 0.0543    |
+| 0.3867        | 2.0   | 724  | 0.4671          | 0.1858 | 0.0605      | 0.0708           | 0.0544    |
+| 0.3777        | 3.0   | 1086 | 0.4603          | 0.1817 | 0.0554      | 0.0737           | 0.0526    |
+| 0.3557        | 4.0   | 1448 | 0.4635          | 0.1815 | 0.0594      | 0.0700           | 0.0521    |
+| 0.3517        | 5.0   | 1810 | 0.4592          | 0.1801 | 0.0585      | 0.0704           | 0.0512    |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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