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
+- modality:audio
+- modality:text
+- format:parquet
+- generated_from_trainer
+datasets:
+- speaker-segmentation
+model-index:
+- name: speaker-segmentation-fine-tuned-id
+  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-id
+
+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: 0.6384
+- Model Preparation Time: 0.0039
+- Der: 0.2151
+- False Alarm: 0.0579
+- Missed Detection: 0.0412
+- Confusion: 0.1160
+
+## 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: 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.6696        | 1.0   | 151  | 0.6690          | 0.0039                 | 0.2248 | 0.0590      | 0.0445           | 0.1214    |
+| 0.6283        | 2.0   | 302  | 0.6558          | 0.0039                 | 0.2187 | 0.0575      | 0.0417           | 0.1196    |
+| 0.6013        | 3.0   | 453  | 0.6436          | 0.0039                 | 0.2159 | 0.0584      | 0.0405           | 0.1170    |
+| 0.5765        | 4.0   | 604  | 0.6379          | 0.0039                 | 0.2135 | 0.0579      | 0.0413           | 0.1142    |
+| 0.5594        | 5.0   | 755  | 0.6384          | 0.0039                 | 0.2151 | 0.0579      | 0.0412           | 0.1160    |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.1
+- Tokenizers 0.21.1

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