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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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- ArtFair/diarizers_dataset_70-15-15 |
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model-index: |
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- name: fine_tuned_segmentation-3.0_1e-3_128_pth |
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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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# fine_tuned_segmentation-3.0_1e-3_128_pth |
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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 ArtFair/diarizers_dataset_70-15-15 default dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3620 |
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- Der: 0.2625 |
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- False Alarm: 0.1458 |
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- Missed Detection: 0.0926 |
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- Confusion: 0.0241 |
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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: 128 |
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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 | 233 | 0.3954 | 0.2915 | 0.1834 | 0.0807 | 0.0274 | |
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| 0.3974 | 2.0 | 466 | 0.3667 | 0.2668 | 0.1391 | 0.1032 | 0.0246 | |
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| 0.3772 | 3.0 | 699 | 0.3675 | 0.2672 | 0.1552 | 0.0874 | 0.0246 | |
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| 0.3618 | 4.0 | 932 | 0.3629 | 0.2641 | 0.1498 | 0.0899 | 0.0243 | |
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| 0.3622 | 5.0 | 1165 | 0.3620 | 0.2625 | 0.1458 | 0.0926 | 0.0241 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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