End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: pyannote/segmentation-3.0
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- Venkatesh4342/pyannote-hindi-diarization
+model-index:
+- name: speaker-segmentation-fine-tuned-hi
+  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-hi
+
+This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the Venkatesh4342/pyannote-hindi-diarization dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0767
+- Model Preparation Time: 0.0076
+- Der: 0.0269
+- False Alarm: 0.0116
+- Missed Detection: 0.0113
+- Confusion: 0.0040
+
+## 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 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: 5.0
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.1348        | 1.0   | 2674  | 0.1155          | 0.0076                 | 0.0404 | 0.0167      | 0.0145           | 0.0093    |
+| 0.0936        | 2.0   | 5348  | 0.0908          | 0.0076                 | 0.0319 | 0.0111      | 0.0155           | 0.0053    |
+| 0.0838        | 3.0   | 8022  | 0.0803          | 0.0076                 | 0.0283 | 0.0111      | 0.0126           | 0.0045    |
+| 0.0751        | 4.0   | 10696 | 0.0779          | 0.0076                 | 0.0275 | 0.0122      | 0.0111           | 0.0042    |
+| 0.0871        | 5.0   | 13370 | 0.0767          | 0.0076                 | 0.0269 | 0.0116      | 0.0113           | 0.0040    |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.4.1
+- Tokenizers 0.21.1