End of training
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- model.safetensors +1 -1
README.md
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---
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license: mit
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base_model: pyannote/segmentation-3.0
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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-eng
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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-eng
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This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4607
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- Der: 0.1815
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- False Alarm: 0.0596
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- Missed Detection: 0.0708
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- Confusion: 0.0511
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- num_epochs: 5.0
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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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| 0.426 | 1.0 | 362 | 0.4667 | 0.1875 | 0.0549 | 0.0784 | 0.0542 |
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| 0.392 | 2.0 | 724 | 0.4678 | 0.1852 | 0.0594 | 0.0721 | 0.0536 |
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| 0.3722 | 3.0 | 1086 | 0.4561 | 0.1801 | 0.0578 | 0.0714 | 0.0509 |
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| 0.351 | 4.0 | 1448 | 0.4565 | 0.1810 | 0.0597 | 0.0699 | 0.0515 |
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| 0.3493 | 5.0 | 1810 | 0.4607 | 0.1815 | 0.0596 | 0.0708 | 0.0511 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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model.safetensors
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