speaker-segmentation-fine-tuned-callhome-eng
This model is a fine-tuned version of pyannote/segmentation-3.0 on the diarizers-community/callhome eng dataset. It achieves the following results on the evaluation set:
- Loss: 0.4642
- Der: 0.1829
- False Alarm: 0.0601
- Missed Detection: 0.0701
- Confusion: 0.0527
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0
Training results
Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
---|---|---|---|---|---|---|---|
0.4229 | 1.0 | 362 | 0.4808 | 0.1907 | 0.0575 | 0.0765 | 0.0568 |
0.3931 | 2.0 | 724 | 0.4658 | 0.1884 | 0.0643 | 0.0680 | 0.0561 |
0.3757 | 3.0 | 1086 | 0.4512 | 0.1829 | 0.0605 | 0.0698 | 0.0526 |
0.3663 | 4.0 | 1448 | 0.4659 | 0.1833 | 0.0596 | 0.0707 | 0.0530 |
0.3555 | 5.0 | 1810 | 0.4642 | 0.1829 | 0.0601 | 0.0701 | 0.0527 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for briangilbert/speaker-segmentation-fine-tuned-callhome-eng
Base model
pyannote/segmentation-3.0