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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-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-callhome-eng-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/callhome eng dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4666 |
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- Der: 0.1814 |
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- False Alarm: 0.0552 |
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- Missed Detection: 0.0739 |
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- Confusion: 0.0523 |
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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: 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: linear |
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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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| 0.4548 | 1.0 | 181 | 0.4943 | 0.1966 | 0.0564 | 0.0811 | 0.0590 | |
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| 0.4171 | 2.0 | 362 | 0.4845 | 0.1951 | 0.0644 | 0.0754 | 0.0552 | |
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| 0.396 | 3.0 | 543 | 0.4633 | 0.1856 | 0.0502 | 0.0825 | 0.0529 | |
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| 0.3856 | 4.0 | 724 | 0.4609 | 0.1843 | 0.0571 | 0.0739 | 0.0534 | |
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| 0.3693 | 5.0 | 905 | 0.4639 | 0.1821 | 0.0531 | 0.0761 | 0.0528 | |
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| 0.3634 | 6.0 | 1086 | 0.4610 | 0.1821 | 0.0588 | 0.0716 | 0.0517 | |
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| 0.3655 | 7.0 | 1267 | 0.4638 | 0.1827 | 0.0566 | 0.0740 | 0.0521 | |
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| 0.3608 | 8.0 | 1448 | 0.4603 | 0.1814 | 0.0567 | 0.0732 | 0.0515 | |
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| 0.3545 | 9.0 | 1629 | 0.4645 | 0.1805 | 0.0530 | 0.0761 | 0.0514 | |
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| 0.3508 | 10.0 | 1810 | 0.4666 | 0.1814 | 0.0552 | 0.0739 | 0.0523 | |
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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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