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--- |
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library_name: transformers |
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language: |
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- id |
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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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- modality:audio |
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- modality:text |
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- format:parquet |
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- generated_from_trainer |
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datasets: |
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- speaker-segmentation |
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model-index: |
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- name: speaker-segmentation-fine-tuned-id |
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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-id |
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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 speaker-segmentation dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3788 |
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- Model Preparation Time: 0.0136 |
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- Der: 0.1188 |
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- False Alarm: 0.0628 |
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- Missed Detection: 0.0216 |
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- Confusion: 0.0345 |
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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: 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: 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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| 0.6461 | 1.0 | 47 | 0.4021 | 0.0136 | 0.1318 | 0.0649 | 0.0218 | 0.0450 | |
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| 0.5224 | 2.0 | 94 | 0.3720 | 0.0136 | 0.1179 | 0.0632 | 0.0217 | 0.0330 | |
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| 0.4869 | 3.0 | 141 | 0.3716 | 0.0136 | 0.1180 | 0.0636 | 0.0208 | 0.0337 | |
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| 0.4707 | 4.0 | 188 | 0.3707 | 0.0136 | 0.1175 | 0.0622 | 0.0224 | 0.0329 | |
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| 0.4697 | 5.0 | 235 | 0.3788 | 0.0136 | 0.1188 | 0.0628 | 0.0216 | 0.0345 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
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