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--- |
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library_name: transformers |
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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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- objects76/synthetic-ja4-speaker-overlap-6400 |
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model-index: |
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- name: full-ja4-2.25sec-big-rf |
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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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# full-ja4-2.25sec-big-rf |
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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 objects76/synthetic-ja4-speaker-overlap-6400 dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 1.3497 |
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- eval_der: 0.3641 |
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- eval_false_alarm: 0.0785 |
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- eval_missed_detection: 0.2430 |
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- eval_confusion: 0.0426 |
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- eval_runtime: 2.1638 |
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- eval_samples_per_second: 295.775 |
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- eval_steps_per_second: 0.462 |
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- step: 0 |
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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: 2048 |
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- eval_batch_size: 2048 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- num_epochs: 200 |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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