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--- |
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library_name: transformers |
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language: |
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- en |
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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/voxconverse |
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model-index: |
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- name: JSWOOK/pyannote_2_finetuning |
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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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# JSWOOK/pyannote_2_finetuning |
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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/voxconverse dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1327 |
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- Model Preparation Time: 0.004 |
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- Der: 0.0499 |
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- False Alarm: 0.0304 |
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- Missed Detection: 0.0094 |
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- Confusion: 0.0101 |
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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: 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 | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:| |
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| No log | 1.0 | 21 | 0.1258 | 0.004 | 0.0486 | 0.0289 | 0.0104 | 0.0093 | |
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| 0.2277 | 2.0 | 42 | 0.1355 | 0.004 | 0.0509 | 0.0300 | 0.0097 | 0.0112 | |
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| 0.1872 | 3.0 | 63 | 0.1327 | 0.004 | 0.0494 | 0.0304 | 0.0095 | 0.0095 | |
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| 0.1649 | 4.0 | 84 | 0.1313 | 0.004 | 0.0492 | 0.0303 | 0.0094 | 0.0095 | |
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| 0.1535 | 5.0 | 105 | 0.1327 | 0.004 | 0.0499 | 0.0304 | 0.0094 | 0.0101 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.19.1 |
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