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-jpn
+  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-jpn
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7545
+- Der: 0.2264
+- False Alarm: 0.0488
+- Missed Detection: 0.1331
+- Confusion: 0.0445
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.562         | 1.0   | 328  | 0.7605          | 0.2346 | 0.0492      | 0.1357           | 0.0497    |
+| 0.5564        | 2.0   | 656  | 0.7540          | 0.2294 | 0.0512      | 0.1328           | 0.0454    |
+| 0.5273        | 3.0   | 984  | 0.7724          | 0.2321 | 0.0438      | 0.1419           | 0.0464    |
+| 0.5037        | 4.0   | 1312 | 0.7599          | 0.2276 | 0.0475      | 0.1349           | 0.0452    |
+| 0.5041        | 5.0   | 1640 | 0.7545          | 0.2264 | 0.0488      | 0.1331           | 0.0445    |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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