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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/simsamu |
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model-index: |
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- name: speaker-segmentation-fine-tuned-simsamu-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-simsamu-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/simsamu default dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2428 |
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- Der: 0.0861 |
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- False Alarm: 0.0245 |
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- Missed Detection: 0.0384 |
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- Confusion: 0.0232 |
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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: 16 |
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- eval_batch_size: 16 |
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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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| 0.2179 | 1.0 | 111 | 0.2259 | 0.0951 | 0.0239 | 0.0486 | 0.0227 | |
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| 0.1694 | 2.0 | 222 | 0.2379 | 0.0930 | 0.0230 | 0.0466 | 0.0234 | |
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| 0.1559 | 3.0 | 333 | 0.2305 | 0.0898 | 0.0223 | 0.0431 | 0.0244 | |
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| 0.149 | 4.0 | 444 | 0.2323 | 0.0893 | 0.0246 | 0.0398 | 0.0249 | |
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| 0.1416 | 5.0 | 555 | 0.2351 | 0.0884 | 0.0243 | 0.0399 | 0.0243 | |
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| 0.1369 | 6.0 | 666 | 0.2458 | 0.0904 | 0.0266 | 0.0370 | 0.0268 | |
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| 0.1367 | 7.0 | 777 | 0.2410 | 0.0882 | 0.0204 | 0.0434 | 0.0244 | |
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| 0.1306 | 8.0 | 888 | 0.2400 | 0.0866 | 0.0240 | 0.0393 | 0.0234 | |
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| 0.1301 | 9.0 | 999 | 0.2422 | 0.0860 | 0.0243 | 0.0387 | 0.0230 | |
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| 0.1276 | 10.0 | 1110 | 0.2428 | 0.0861 | 0.0245 | 0.0384 | 0.0232 | |
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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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