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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: 0.4642 |
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- Der: 0.1829 |
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- False Alarm: 0.0601 |
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- Missed Detection: 0.0701 |
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- Confusion: 0.0527 |
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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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| 0.4229 | 1.0 | 362 | 0.4808 | 0.1907 | 0.0575 | 0.0765 | 0.0568 | |
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| 0.3931 | 2.0 | 724 | 0.4658 | 0.1884 | 0.0643 | 0.0680 | 0.0561 | |
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| 0.3757 | 3.0 | 1086 | 0.4512 | 0.1829 | 0.0605 | 0.0698 | 0.0526 | |
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| 0.3663 | 4.0 | 1448 | 0.4659 | 0.1833 | 0.0596 | 0.0707 | 0.0530 | |
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| 0.3555 | 5.0 | 1810 | 0.4642 | 0.1829 | 0.0601 | 0.0701 | 0.0527 | |
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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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