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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-3 |
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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-3 |
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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: 0.4652 |
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- Der: 0.1821 |
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- False Alarm: 0.0597 |
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- Missed Detection: 0.0715 |
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- Confusion: 0.0509 |
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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: 64 |
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- eval_batch_size: 64 |
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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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| 0.4563 | 1.0 | 181 | 0.4971 | 0.1973 | 0.0553 | 0.0802 | 0.0617 | |
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| 0.4053 | 2.0 | 362 | 0.4740 | 0.1899 | 0.0604 | 0.0749 | 0.0546 | |
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| 0.3833 | 3.0 | 543 | 0.4636 | 0.1854 | 0.0556 | 0.0766 | 0.0531 | |
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| 0.3738 | 4.0 | 724 | 0.4664 | 0.1830 | 0.0579 | 0.0733 | 0.0518 | |
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| 0.3596 | 5.0 | 905 | 0.4571 | 0.1800 | 0.0558 | 0.0748 | 0.0494 | |
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| 0.3533 | 6.0 | 1086 | 0.4671 | 0.1844 | 0.0629 | 0.0685 | 0.0529 | |
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| 0.3571 | 7.0 | 1267 | 0.4641 | 0.1820 | 0.0594 | 0.0711 | 0.0515 | |
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| 0.3496 | 8.0 | 1448 | 0.4641 | 0.1824 | 0.0596 | 0.0717 | 0.0511 | |
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| 0.3449 | 9.0 | 1629 | 0.4636 | 0.1819 | 0.0591 | 0.0718 | 0.0510 | |
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| 0.3415 | 10.0 | 1810 | 0.4652 | 0.1821 | 0.0597 | 0.0715 | 0.0509 | |
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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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