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---
library_name: transformers
language:
- id
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- speaker-segmentation
model-index:
- name: speaker-segmentation-fine-tuned-datasetID-hugging_2_4_updated_02
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. -->
# speaker-segmentation-fine-tuned-datasetID-hugging_2_4_updated_02
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.
It achieves the following results on the evaluation set:
- Loss: 0.4274
- Model Preparation Time: 0.0065
- Der: 0.1421
- False Alarm: 0.0214
- Missed Detection: 0.0105
- Confusion: 0.1102
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 100
- 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
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.5722 | 1.0 | 285 | 0.5763 | 0.0065 | 0.1903 | 0.0275 | 0.0158 | 0.1470 |
| 0.5001 | 2.0 | 570 | 0.5077 | 0.0065 | 0.1685 | 0.0240 | 0.0125 | 0.1319 |
| 0.4846 | 3.0 | 855 | 0.4810 | 0.0065 | 0.1604 | 0.0221 | 0.0121 | 0.1261 |
| 0.4441 | 4.0 | 1140 | 0.4686 | 0.0065 | 0.1577 | 0.0220 | 0.0111 | 0.1245 |
| 0.447 | 5.0 | 1425 | 0.4477 | 0.0065 | 0.1494 | 0.0216 | 0.0110 | 0.1168 |
| 0.4265 | 6.0 | 1710 | 0.4408 | 0.0065 | 0.1458 | 0.0217 | 0.0106 | 0.1135 |
| 0.428 | 7.0 | 1995 | 0.4311 | 0.0065 | 0.1433 | 0.0215 | 0.0105 | 0.1114 |
| 0.4047 | 8.0 | 2280 | 0.4291 | 0.0065 | 0.1429 | 0.0214 | 0.0105 | 0.1109 |
| 0.4069 | 9.0 | 2565 | 0.4280 | 0.0065 | 0.1422 | 0.0214 | 0.0105 | 0.1103 |
| 0.4133 | 10.0 | 2850 | 0.4274 | 0.0065 | 0.1421 | 0.0214 | 0.0105 | 0.1102 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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