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---
library_name: transformers
language:
- en
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/voxconverse
model-index:
- name: JSWOOK/pyannote_3_fine_tuning
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. -->
# JSWOOK/pyannote_3_fine_tuning
This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/voxconverse dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3134
- Model Preparation Time: 0.0048
- Der: 0.0888
- False Alarm: 0.0134
- Missed Detection: 0.0337
- Confusion: 0.0417
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| No log | 1.0 | 24 | 0.3180 | 0.0048 | 0.0915 | 0.0119 | 0.0385 | 0.0410 |
| 0.1903 | 2.0 | 48 | 0.3116 | 0.0048 | 0.0903 | 0.0125 | 0.0369 | 0.0409 |
| 0.1839 | 3.0 | 72 | 0.3089 | 0.0048 | 0.0896 | 0.0128 | 0.0357 | 0.0411 |
| 0.1825 | 4.0 | 96 | 0.3176 | 0.0048 | 0.0896 | 0.0131 | 0.0352 | 0.0413 |
| 0.1797 | 5.0 | 120 | 0.3148 | 0.0048 | 0.0892 | 0.0132 | 0.0346 | 0.0413 |
| 0.1801 | 6.0 | 144 | 0.3141 | 0.0048 | 0.0890 | 0.0133 | 0.0342 | 0.0415 |
| 0.1735 | 7.0 | 168 | 0.3137 | 0.0048 | 0.0887 | 0.0134 | 0.0338 | 0.0416 |
| 0.1705 | 8.0 | 192 | 0.3133 | 0.0048 | 0.0887 | 0.0134 | 0.0337 | 0.0416 |
| 0.1796 | 9.0 | 216 | 0.3133 | 0.0048 | 0.0887 | 0.0134 | 0.0337 | 0.0417 |
| 0.1644 | 10.0 | 240 | 0.3134 | 0.0048 | 0.0888 | 0.0134 | 0.0337 | 0.0417 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
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