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--- |
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library_name: transformers |
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language: |
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- spa |
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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-spa |
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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-spa |
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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: 0.5724 |
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- Der: 0.3391 |
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- False Alarm: 0.2612 |
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- Missed Detection: 0.0776 |
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- Confusion: 0.0003 |
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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: 64 |
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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: 5 |
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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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| 0.4184 | 1.0 | 230 | 0.4700 | 0.2893 | 0.2341 | 0.0546 | 0.0006 | |
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| 0.4075 | 2.0 | 460 | 0.5348 | 0.3197 | 0.2567 | 0.0625 | 0.0005 | |
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| 0.3941 | 3.0 | 690 | 0.5296 | 0.3134 | 0.2608 | 0.0525 | 0.0001 | |
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| 0.3902 | 4.0 | 920 | 0.5936 | 0.3624 | 0.2612 | 0.1009 | 0.0003 | |
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| 0.389 | 5.0 | 1150 | 0.5724 | 0.3391 | 0.2612 | 0.0776 | 0.0003 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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