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--- |
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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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- diarizers-community/callfriend |
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model-index: |
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- name: speaker-segmentation-fine-tuned-callfriend-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-callfriend-jpn |
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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 diarizers-community/callfriend jpn dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6570 |
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- Der: 0.2815 |
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- False Alarm: 0.1035 |
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- Missed Detection: 0.1082 |
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- Confusion: 0.0698 |
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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: 5.0 |
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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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| 0.6836 | 1.0 | 237 | 0.6465 | 0.2865 | 0.1045 | 0.1095 | 0.0724 | |
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| 0.6265 | 2.0 | 474 | 0.6455 | 0.2772 | 0.1023 | 0.1062 | 0.0687 | |
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| 0.6199 | 3.0 | 711 | 0.6615 | 0.2879 | 0.0950 | 0.1161 | 0.0768 | |
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| 0.6007 | 4.0 | 948 | 0.6574 | 0.2823 | 0.1051 | 0.1066 | 0.0705 | |
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| 0.5979 | 5.0 | 1185 | 0.6570 | 0.2815 | 0.1035 | 0.1082 | 0.0698 | |
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### Framework versions |
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- Transformers 4.40.0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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