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---
library_name: transformers
language:
- spa
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- pyannote/segmentation
model-index:
- name: segmentation-3.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segmentation-3.0
This model is a fine-tuned version of [](https://huggingface.co/) on the pyannote/segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6638
- Der: 0.2932
- False Alarm: 0.2540
- Missed Detection: 0.0387
- Confusion: 0.0004
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3145 | 1.0 | 282 | 0.5751 | 0.2944 | 0.2487 | 0.0454 | 0.0003 |
| 0.3087 | 2.0 | 564 | 0.5957 | 0.2912 | 0.2462 | 0.0440 | 0.0010 |
| 0.2905 | 3.0 | 846 | 0.6614 | 0.2970 | 0.2627 | 0.0333 | 0.0010 |
| 0.2733 | 4.0 | 1128 | 0.6626 | 0.2940 | 0.2558 | 0.0378 | 0.0004 |
| 0.2672 | 5.0 | 1410 | 0.6638 | 0.2932 | 0.2540 | 0.0387 | 0.0004 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.0
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