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-deu
+  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-deu
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome deu dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3780
+- Der: 0.1415
+- False Alarm: 0.0724
+- Missed Detection: 0.0490
+- Confusion: 0.0201
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4622        | 1.0   | 330  | 0.3844          | 0.1439 | 0.0653      | 0.0562           | 0.0223    |
+| 0.4306        | 2.0   | 660  | 0.4004          | 0.1519 | 0.0763      | 0.0515           | 0.0241    |
+| 0.4069        | 3.0   | 990  | 0.3775          | 0.1407 | 0.0707      | 0.0496           | 0.0204    |
+| 0.3949        | 4.0   | 1320 | 0.3771          | 0.1408 | 0.0710      | 0.0498           | 0.0200    |
+| 0.3879        | 5.0   | 1650 | 0.3780          | 0.1415 | 0.0724      | 0.0490           | 0.0201    |
+
+
+### 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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