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--- |
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library_name: transformers |
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language: |
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- jpn |
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license: mit |
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base_model: pyannote/speaker-diarization-3.1 |
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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-jpn |
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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-jpn |
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This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7600 |
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- Model Preparation Time: 0.0042 |
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- Der: 0.2253 |
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- False Alarm: 0.0463 |
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- Missed Detection: 0.1355 |
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- Confusion: 0.0434 |
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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: 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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
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| 0.5792 | 1.0 | 328 | 0.7544 | 0.0042 | 0.2330 | 0.0530 | 0.1318 | 0.0482 | |
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| 0.5219 | 2.0 | 656 | 0.7515 | 0.0042 | 0.2265 | 0.0483 | 0.1352 | 0.0430 | |
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| 0.5221 | 3.0 | 984 | 0.7581 | 0.0042 | 0.2261 | 0.0489 | 0.1340 | 0.0432 | |
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| 0.5033 | 4.0 | 1312 | 0.7550 | 0.0042 | 0.2248 | 0.0489 | 0.1329 | 0.0429 | |
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| 0.5249 | 5.0 | 1640 | 0.7600 | 0.0042 | 0.2253 | 0.0463 | 0.1355 | 0.0434 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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