End of training

README.md

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+---
+library_name: transformers
+language:
+- spa
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/callhome
+model-index:
+- name: speaker-segmentation-fine-tuned-callhome-spa
+  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-spa
+
+This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5724
+- Der: 0.3391
+- False Alarm: 0.2612
+- Missed Detection: 0.0776
+- Confusion: 0.0003
+
+## 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: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.4184        | 1.0   | 230  | 0.4700          | 0.2893 | 0.2341      | 0.0546           | 0.0006    |
+| 0.4075        | 2.0   | 460  | 0.5348          | 0.3197 | 0.2567      | 0.0625           | 0.0005    |
+| 0.3941        | 3.0   | 690  | 0.5296          | 0.3134 | 0.2608      | 0.0525           | 0.0001    |
+| 0.3902        | 4.0   | 920  | 0.5936          | 0.3624 | 0.2612      | 0.1009           | 0.0003    |
+| 0.389         | 5.0   | 1150 | 0.5724          | 0.3391 | 0.2612      | 0.0776           | 0.0003    |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.1
+- Datasets 3.0.1
+- Tokenizers 0.20.0

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