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README.md
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---
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library_name: transformers
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language:
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- hi
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license: mit
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base_model: pyannote/speaker-diarization-3.1
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tags:
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- speaker-diarization
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- speaker-segmentation
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- generated_from_trainer
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datasets:
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- diarizers-community/callhome
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model-index:
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- name: speaker-segmentation-fine-tuned-callhome-hi
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# speaker-segmentation-fine-tuned-callhome-hi
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.4388
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- Der: 0.1470
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- False Alarm: 0.0241
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- Missed Detection: 0.0294
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- Confusion: 0.0934
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
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| 0.4572 | 1.0 | 194 | 0.4811 | 0.1598 | 0.0239 | 0.0319 | 0.1041 |
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| 0.3809 | 2.0 | 388 | 0.4470 | 0.1488 | 0.0223 | 0.0315 | 0.0950 |
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| 0.3892 | 3.0 | 582 | 0.4388 | 0.1470 | 0.0241 | 0.0294 | 0.0934 |
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### Framework versions
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- Transformers 4.48.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.2.0
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- Tokenizers 0.21.0
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