speaker-segmentation-fine-tuned-id
This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the speaker-segmentation dataset. It achieves the following results on the evaluation set:
- Loss: 0.3788
- Model Preparation Time: 0.0136
- Der: 0.1188
- False Alarm: 0.0628
- Missed Detection: 0.0216
- Confusion: 0.0345
Model description
More information needed
Intended uses & limitations
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Training and evaluation data
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Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
---|---|---|---|---|---|---|---|---|
0.6461 | 1.0 | 47 | 0.4021 | 0.0136 | 0.1318 | 0.0649 | 0.0218 | 0.0450 |
0.5224 | 2.0 | 94 | 0.3720 | 0.0136 | 0.1179 | 0.0632 | 0.0217 | 0.0330 |
0.4869 | 3.0 | 141 | 0.3716 | 0.0136 | 0.1180 | 0.0636 | 0.0208 | 0.0337 |
0.4707 | 4.0 | 188 | 0.3707 | 0.0136 | 0.1175 | 0.0622 | 0.0224 | 0.0329 |
0.4697 | 5.0 | 235 | 0.3788 | 0.0136 | 0.1188 | 0.0628 | 0.0216 | 0.0345 |
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Base model
pyannote/speaker-diarization-3.1