End of training

README.md

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+---
+library_name: transformers
+language:
+- id
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- speaker-segmentation
+model-index:
+- name: speaker-segmentation-fine-tuned-datasetID-manual-dataset
+  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-datasetID-manual-dataset
+
+This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the speaker-segmentation dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3616
+- Model Preparation Time: 0.0066
+- Der: 0.1114
+- False Alarm: 0.0622
+- Missed Detection: 0.0215
+- Confusion: 0.0277
+
+## 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: 43
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.6418        | 1.0   | 47   | 0.3768          | 0.0066                 | 0.1208 | 0.0623      | 0.0226           | 0.0359    |
+| 0.5017        | 2.0   | 94   | 0.3626          | 0.0066                 | 0.1140 | 0.0645      | 0.0199           | 0.0296    |
+| 0.4501        | 3.0   | 141  | 0.3627          | 0.0066                 | 0.1128 | 0.0622      | 0.0224           | 0.0282    |
+| 0.4782        | 4.0   | 188  | 0.3613          | 0.0066                 | 0.1113 | 0.0625      | 0.0211           | 0.0278    |
+| 0.4795        | 5.0   | 235  | 0.3616          | 0.0066                 | 0.1114 | 0.0622      | 0.0215           | 0.0277    |
+
+
+### Framework versions
+
+- Transformers 4.50.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

model.safetensors

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