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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/ami |
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model-index: |
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- name: speaker-segmentation-fine-tuned-ami-2 |
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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-ami-2 |
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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/ami ihm dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3764 |
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- Der: 0.1401 |
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- False Alarm: 0.0503 |
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- Missed Detection: 0.0575 |
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- Confusion: 0.0323 |
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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: 10.0 |
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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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| 0.4149 | 1.0 | 1427 | 0.3607 | 0.1407 | 0.0492 | 0.0593 | 0.0323 | |
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| 0.3915 | 2.0 | 2854 | 0.3684 | 0.1422 | 0.0460 | 0.0621 | 0.0340 | |
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| 0.3748 | 3.0 | 4281 | 0.3730 | 0.1419 | 0.0530 | 0.0570 | 0.0318 | |
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| 0.3778 | 4.0 | 5708 | 0.3649 | 0.1409 | 0.0472 | 0.0611 | 0.0326 | |
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| 0.3565 | 5.0 | 7135 | 0.3723 | 0.1415 | 0.0501 | 0.0591 | 0.0324 | |
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| 0.3566 | 6.0 | 8562 | 0.3740 | 0.1406 | 0.0499 | 0.0584 | 0.0323 | |
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| 0.3534 | 7.0 | 9989 | 0.3736 | 0.1399 | 0.0493 | 0.0581 | 0.0325 | |
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| 0.3418 | 8.0 | 11416 | 0.3744 | 0.1397 | 0.0500 | 0.0577 | 0.0321 | |
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| 0.3388 | 9.0 | 12843 | 0.3777 | 0.1403 | 0.0505 | 0.0574 | 0.0324 | |
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| 0.346 | 10.0 | 14270 | 0.3764 | 0.1401 | 0.0503 | 0.0575 | 0.0323 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.19.1 |
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