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End of training
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README.md
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---
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language:
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- eng
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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 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4692
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- Der: 0.1840
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- False Alarm: 0.0616
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- Missed Detection: 0.0711
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- Confusion: 0.0513
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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
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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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| 0.3907 | 1.0 | 362 | 0.4760 | 0.1920 | 0.0622 | 0.0739 | 0.0559 |
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| 0.4104 | 2.0 | 724 | 0.4737 | 0.1912 | 0.0704 | 0.0688 | 0.0520 |
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| 0.3848 | 3.0 | 1086 | 0.4567 | 0.1809 | 0.0595 | 0.0709 | 0.0504 |
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| 0.3688 | 4.0 | 1448 | 0.4680 | 0.1831 | 0.0581 | 0.0738 | 0.0512 |
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| 0.344 | 5.0 | 1810 | 0.4692 | 0.1840 | 0.0616 | 0.0711 | 0.0513 |
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### Framework versions
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- Transformers 4.40.0
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- Pytorch 2.2.1+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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pytorch_model.bin
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runs/Apr25_14-37-11_9816076f8c0c/events.out.tfevents.1714055848.9816076f8c0c.451.0
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