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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: 0.4632 |
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- Der: 0.1810 |
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- False Alarm: 0.0575 |
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- Missed Detection: 0.0707 |
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- Confusion: 0.0528 |
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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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| 0.4223 | 1.0 | 362 | 0.4718 | 0.1890 | 0.0617 | 0.0724 | 0.0549 | |
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| 0.3832 | 2.0 | 724 | 0.4581 | 0.1836 | 0.0561 | 0.0749 | 0.0526 | |
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| 0.3812 | 3.0 | 1086 | 0.4676 | 0.1846 | 0.0598 | 0.0702 | 0.0546 | |
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| 0.3607 | 4.0 | 1448 | 0.4610 | 0.1819 | 0.0585 | 0.0701 | 0.0533 | |
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| 0.3581 | 5.0 | 1810 | 0.4632 | 0.1810 | 0.0575 | 0.0707 | 0.0528 | |
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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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