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/voxconverse
+model-index:
+- name: speaker-segmentation-fine-tuned-voxconverse-en
+  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-voxconverse-en
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/voxconverse dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1250
+- Der: 0.8257
+- False Alarm: 0.3733
+- Missed Detection: 0.3995
+- Confusion: 0.0528
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.9302        | 1.0   | 791  | 0.9903          | 0.6790 | 0.5013      | 0.0965           | 0.0812    |
+| 0.8848        | 2.0   | 1582 | 1.0536          | 0.7965 | 0.3991      | 0.3409           | 0.0565    |
+| 0.8513        | 3.0   | 2373 | 1.0884          | 0.8114 | 0.4017      | 0.3528           | 0.0569    |
+| 0.7926        | 4.0   | 3164 | 1.1292          | 0.8378 | 0.3660      | 0.4219           | 0.0500    |
+| 0.8147        | 5.0   | 3955 | 1.1250          | 0.8257 | 0.3733      | 0.3995           | 0.0528    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.19.1

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