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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/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/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5957 |
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- Der: 0.1975 |
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- False Alarm: 0.0777 |
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- Missed Detection: 0.0713 |
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- Confusion: 0.0485 |
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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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| 0.5998 | 1.0 | 336 | 0.6155 | 0.2067 | 0.0726 | 0.0792 | 0.0549 | |
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| 0.578 | 2.0 | 672 | 0.6258 | 0.2086 | 0.0851 | 0.0691 | 0.0544 | |
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| 0.5431 | 3.0 | 1008 | 0.6054 | 0.2023 | 0.0830 | 0.0689 | 0.0505 | |
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| 0.5198 | 4.0 | 1344 | 0.5989 | 0.1984 | 0.0762 | 0.0729 | 0.0494 | |
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| 0.5211 | 5.0 | 1680 | 0.5957 | 0.1975 | 0.0777 | 0.0713 | 0.0485 | |
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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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