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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: 0.5595 |
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- Der: 0.2894 |
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- False Alarm: 0.2353 |
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- Missed Detection: 0.0536 |
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- Confusion: 0.0005 |
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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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| 0.3614 | 1.0 | 226 | 0.4962 | 0.2910 | 0.2389 | 0.0520 | 0.0001 | |
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| 0.3465 | 2.0 | 452 | 0.5067 | 0.2860 | 0.2179 | 0.0679 | 0.0002 | |
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| 0.3325 | 3.0 | 678 | 0.5343 | 0.2941 | 0.2300 | 0.0636 | 0.0005 | |
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| 0.3189 | 4.0 | 904 | 0.5613 | 0.2906 | 0.2380 | 0.0522 | 0.0004 | |
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| 0.3238 | 5.0 | 1130 | 0.5595 | 0.2894 | 0.2353 | 0.0536 | 0.0005 | |
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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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