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: 0.7498
+- Der: 0.2258
+- False Alarm: 0.0470
+- Missed Detection: 0.1339
+- Confusion: 0.0449
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.5797        | 1.0   | 328  | 0.7543          | 0.2334 | 0.0582      | 0.1277           | 0.0476    |
+| 0.5578        | 2.0   | 656  | 0.7684          | 0.2306 | 0.0446      | 0.1401           | 0.0458    |
+| 0.5213        | 3.0   | 984  | 0.7569          | 0.2289 | 0.0463      | 0.1368           | 0.0459    |
+| 0.4971        | 4.0   | 1312 | 0.7448          | 0.2273 | 0.0489      | 0.1329           | 0.0456    |
+| 0.5026        | 5.0   | 1640 | 0.7498          | 0.2258 | 0.0470      | 0.1339           | 0.0449    |
+
+
+### Framework versions
+
+- Transformers 4.40.0
+- Pytorch 2.2.2+cu121
+- Datasets 2.18.0
+- Tokenizers 0.19.1

model.safetensors

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