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--- |
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library_name: transformers |
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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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- objects76/rsup-eval-ja-522-250513 |
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model-index: |
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- name: full-eval-ja-522-pivot |
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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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# full-eval-ja-522-pivot |
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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 objects76/rsup-eval-ja-522-250513 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7260 |
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- Der: 0.2451 |
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- False Alarm: 0.0501 |
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- Missed Detection: 0.1370 |
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- Confusion: 0.0579 |
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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: 2048 |
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- eval_batch_size: 2048 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- num_epochs: 200 |
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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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| No log | 1.0 | 1 | 1.3787 | 0.4863 | 0.0407 | 0.2177 | 0.2279 | |
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| No log | 2.0 | 2 | 1.3678 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 3.0 | 3 | 1.3538 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 4.0 | 4 | 1.3302 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 5.0 | 5 | 1.2866 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 6.0 | 6 | 1.2250 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 7.0 | 7 | 1.1777 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 8.0 | 8 | 1.1856 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 9.0 | 9 | 1.2031 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 10.0 | 10 | 1.1904 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 11.0 | 11 | 1.1680 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 12.0 | 12 | 1.1535 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 13.0 | 13 | 1.1492 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 14.0 | 14 | 1.1495 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 15.0 | 15 | 1.1498 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 16.0 | 16 | 1.1489 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 17.0 | 17 | 1.1472 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 18.0 | 18 | 1.1465 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 19.0 | 19 | 1.1478 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 20.0 | 20 | 1.1504 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 21.0 | 21 | 1.1519 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 22.0 | 22 | 1.1523 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 23.0 | 23 | 1.1512 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| No log | 24.0 | 24 | 1.1492 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 25.0 | 25 | 1.1470 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 26.0 | 26 | 1.1445 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 27.0 | 27 | 1.1416 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 28.0 | 28 | 1.1394 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 29.0 | 29 | 1.1369 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 30.0 | 30 | 1.1354 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 31.0 | 31 | 1.1340 | 0.4526 | 0.0501 | 0.1370 | 0.2655 | |
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| 1.1857 | 32.0 | 32 | 1.1328 | 0.4503 | 0.0501 | 0.1370 | 0.2631 | |
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| 1.1857 | 33.0 | 33 | 1.0884 | 0.4307 | 0.0501 | 0.1370 | 0.2435 | |
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| 1.1857 | 34.0 | 34 | 1.0495 | 0.3814 | 0.0501 | 0.1370 | 0.1942 | |
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| 1.1857 | 35.0 | 35 | 1.0340 | 0.2475 | 0.0501 | 0.1370 | 0.0603 | |
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| 1.1857 | 36.0 | 36 | 1.0111 | 0.3258 | 0.0501 | 0.1370 | 0.1386 | |
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| 1.1857 | 37.0 | 37 | 0.9765 | 0.2803 | 0.0501 | 0.1370 | 0.0932 | |
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| 1.1857 | 38.0 | 38 | 0.9547 | 0.2443 | 0.0501 | 0.1370 | 0.0572 | |
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| 1.1857 | 39.0 | 39 | 0.9093 | 0.2428 | 0.0501 | 0.1370 | 0.0556 | |
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| 1.1857 | 40.0 | 40 | 0.8913 | 0.2475 | 0.0501 | 0.1370 | 0.0603 | |
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| 1.1857 | 41.0 | 41 | 0.8402 | 0.2412 | 0.0501 | 0.1370 | 0.0540 | |
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| 1.1857 | 42.0 | 42 | 0.8096 | 0.2373 | 0.0501 | 0.1370 | 0.0501 | |
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| 1.1857 | 43.0 | 43 | 0.7950 | 0.2404 | 0.0501 | 0.1370 | 0.0532 | |
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| 1.1857 | 44.0 | 44 | 0.7625 | 0.2428 | 0.0501 | 0.1370 | 0.0556 | |
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| 1.1857 | 45.0 | 45 | 0.7550 | 0.2420 | 0.0501 | 0.1370 | 0.0548 | |
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| 1.1857 | 46.0 | 46 | 0.7273 | 0.2381 | 0.0501 | 0.1370 | 0.0509 | |
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| 1.1857 | 47.0 | 47 | 0.7126 | 0.2396 | 0.0501 | 0.1370 | 0.0525 | |
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| 1.1857 | 48.0 | 48 | 0.7289 | 0.2459 | 0.0501 | 0.1370 | 0.0587 | |
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| 1.1857 | 49.0 | 49 | 0.6965 | 0.2381 | 0.0501 | 0.1370 | 0.0509 | |
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| 0.9285 | 50.0 | 50 | 0.6970 | 0.2420 | 0.0619 | 0.1323 | 0.0478 | |
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| 0.9285 | 51.0 | 51 | 0.7081 | 0.2388 | 0.0603 | 0.1308 | 0.0478 | |
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| 0.9285 | 52.0 | 52 | 0.6896 | 0.2373 | 0.0720 | 0.1261 | 0.0392 | |
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| 0.9285 | 53.0 | 53 | 0.6952 | 0.2412 | 0.0752 | 0.1261 | 0.0399 | |
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| 0.9285 | 54.0 | 54 | 0.7025 | 0.2365 | 0.0642 | 0.1284 | 0.0439 | |
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| 0.9285 | 55.0 | 55 | 0.6864 | 0.2341 | 0.0713 | 0.1253 | 0.0376 | |
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| 0.9285 | 56.0 | 56 | 0.6920 | 0.2357 | 0.0713 | 0.1261 | 0.0384 | |
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| 0.9285 | 57.0 | 57 | 0.7058 | 0.2396 | 0.0658 | 0.1284 | 0.0454 | |
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| 0.9285 | 58.0 | 58 | 0.6878 | 0.2334 | 0.0681 | 0.1284 | 0.0368 | |
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| 0.9285 | 59.0 | 59 | 0.7039 | 0.2388 | 0.0634 | 0.1308 | 0.0446 | |
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| 0.9285 | 60.0 | 60 | 0.7155 | 0.2365 | 0.0532 | 0.1339 | 0.0493 | |
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| 0.9285 | 61.0 | 61 | 0.7017 | 0.2373 | 0.0540 | 0.1355 | 0.0478 | |
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| 0.9285 | 62.0 | 62 | 0.7291 | 0.2475 | 0.0501 | 0.1370 | 0.0603 | |
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| 0.9285 | 63.0 | 63 | 0.7072 | 0.2381 | 0.0501 | 0.1370 | 0.0509 | |
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| 0.9285 | 64.0 | 64 | 0.7189 | 0.2357 | 0.0501 | 0.1370 | 0.0486 | |
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| 0.9285 | 65.0 | 65 | 0.7260 | 0.2451 | 0.0501 | 0.1370 | 0.0579 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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