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: 0.4570
+- Der: 0.1803
+- False Alarm: 0.0556
+- Missed Detection: 0.0731
+- Confusion: 0.0516
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4257        | 1.0   | 362  | 0.4789          | 0.1918 | 0.0573      | 0.0786           | 0.0559    |
+| 0.3889        | 2.0   | 724  | 0.4660          | 0.1866 | 0.0556      | 0.0760           | 0.0549    |
+| 0.3758        | 3.0   | 1086 | 0.4587          | 0.1807 | 0.0548      | 0.0755           | 0.0503    |
+| 0.3643        | 4.0   | 1448 | 0.4564          | 0.1805 | 0.0555      | 0.0734           | 0.0515    |
+| 0.3511        | 5.0   | 1810 | 0.4570          | 0.1803 | 0.0556      | 0.0731           | 0.0516    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.19.1

model.safetensors

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