End of training

README.md

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+---
+language:
+- hi
+license: mit
+base_model: pyannote/speaker-diarization-3.1
+tags:
+- speaker-diarization
+- speaker-segmentation
+- generated_from_trainer
+datasets:
+- diarizers-community/synthetic
+model-index:
+- name: speaker-segmentation-fine-tuned-hindi
+  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-hindi
+
+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/synthetic dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4442
+- Der: 0.1448
+- False Alarm: 0.0243
+- Missed Detection: 0.0280
+- Confusion: 0.0925
+
+## 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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
+| 0.477         | 1.0   | 194  | 0.4877          | 0.1651 | 0.0259      | 0.0320           | 0.1072    |
+| 0.3908        | 2.0   | 388  | 0.4562          | 0.1526 | 0.0231      | 0.0315           | 0.0980    |
+| 0.3708        | 3.0   | 582  | 0.4356          | 0.1451 | 0.0242      | 0.0284           | 0.0924    |
+| 0.3567        | 4.0   | 776  | 0.4461          | 0.1441 | 0.0244      | 0.0280           | 0.0917    |
+| 0.3447        | 5.0   | 970  | 0.4442          | 0.1448 | 0.0243      | 0.0280           | 0.0925    |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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