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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-5_32 |
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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-5_32 |
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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: 0.4908 |
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- Der: 0.3585 |
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- False Alarm: 0.2041 |
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- Missed Detection: 0.1214 |
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- Confusion: 0.0330 |
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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: 1e-05 |
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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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| 0.621 | 1.0 | 929 | 0.5601 | 0.3774 | 0.2133 | 0.1279 | 0.0361 | |
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| 0.5472 | 2.0 | 1858 | 0.5149 | 0.3680 | 0.2084 | 0.1254 | 0.0342 | |
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| 0.5353 | 3.0 | 2787 | 0.4969 | 0.3615 | 0.2049 | 0.1234 | 0.0333 | |
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| 0.517 | 4.0 | 3716 | 0.4919 | 0.3590 | 0.2042 | 0.1216 | 0.0331 | |
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| 0.5217 | 5.0 | 4645 | 0.4908 | 0.3585 | 0.2041 | 0.1214 | 0.0330 | |
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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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