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: 0.7512
+- Der: 0.2263
+- False Alarm: 0.0467
+- Missed Detection: 0.1359
+- Confusion: 0.0437
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.5796        | 1.0   | 328  | 0.7613          | 0.2340 | 0.0514      | 0.1341           | 0.0484    |
+| 0.5518        | 2.0   | 656  | 0.7646          | 0.2328 | 0.0480      | 0.1391           | 0.0458    |
+| 0.5304        | 3.0   | 984  | 0.7723          | 0.2321 | 0.0426      | 0.1421           | 0.0474    |
+| 0.5043        | 4.0   | 1312 | 0.7504          | 0.2272 | 0.0490      | 0.1336           | 0.0446    |
+| 0.5086        | 5.0   | 1640 | 0.7512          | 0.2263 | 0.0467      | 0.1359           | 0.0437    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.2+cu118
+- Datasets 2.18.0
+- Tokenizers 0.19.1

runs/Apr25_19-21-54_vinbdi/events.out.tfevents.1714049254.vinbdi.1747590.0

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