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---
library_name: transformers
language:
- jpn
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/synthetic-speaker-diarization-dataset
model-index:
- name: synthetic-speaker-jpn
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. -->
# synthetic-speaker-jpn
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/synthetic-speaker-diarization-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3546
- Model Preparation Time: 0.0018
- Der: 0.1098
- False Alarm: 0.0178
- Missed Detection: 0.0198
- Confusion: 0.0722
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4222 | 1.0 | 198 | 0.4012 | 0.0018 | 0.1316 | 0.0195 | 0.0240 | 0.0880 |
| 0.3767 | 2.0 | 396 | 0.3893 | 0.0018 | 0.1267 | 0.0176 | 0.0237 | 0.0854 |
| 0.3784 | 3.0 | 594 | 0.3935 | 0.0018 | 0.1233 | 0.0172 | 0.0232 | 0.0829 |
| 0.3596 | 4.0 | 792 | 0.3747 | 0.0018 | 0.1216 | 0.0192 | 0.0204 | 0.0820 |
| 0.352 | 5.0 | 990 | 0.3807 | 0.0018 | 0.1231 | 0.0184 | 0.0207 | 0.0840 |
| 0.3111 | 6.0 | 1188 | 0.3585 | 0.0018 | 0.1134 | 0.0183 | 0.0203 | 0.0748 |
| 0.3139 | 7.0 | 1386 | 0.3460 | 0.0018 | 0.1123 | 0.0181 | 0.0202 | 0.0740 |
| 0.3176 | 8.0 | 1584 | 0.3610 | 0.0018 | 0.1134 | 0.0184 | 0.0198 | 0.0752 |
| 0.3142 | 9.0 | 1782 | 0.3542 | 0.0018 | 0.1127 | 0.0172 | 0.0211 | 0.0745 |
| 0.2834 | 10.0 | 1980 | 0.3485 | 0.0018 | 0.1116 | 0.0178 | 0.0201 | 0.0737 |
| 0.2875 | 11.0 | 2178 | 0.3537 | 0.0018 | 0.1095 | 0.0174 | 0.0204 | 0.0717 |
| 0.2704 | 12.0 | 2376 | 0.3582 | 0.0018 | 0.1111 | 0.0177 | 0.0201 | 0.0733 |
| 0.2802 | 13.0 | 2574 | 0.3589 | 0.0018 | 0.1106 | 0.0177 | 0.0200 | 0.0728 |
| 0.2577 | 14.0 | 2772 | 0.3547 | 0.0018 | 0.1102 | 0.0180 | 0.0198 | 0.0725 |
| 0.261 | 15.0 | 2970 | 0.3511 | 0.0018 | 0.1086 | 0.0181 | 0.0196 | 0.0709 |
| 0.2647 | 16.0 | 3168 | 0.3544 | 0.0018 | 0.1096 | 0.0182 | 0.0194 | 0.0719 |
| 0.2554 | 17.0 | 3366 | 0.3537 | 0.0018 | 0.1093 | 0.0174 | 0.0202 | 0.0717 |
| 0.2624 | 18.0 | 3564 | 0.3547 | 0.0018 | 0.1095 | 0.0178 | 0.0199 | 0.0718 |
| 0.2667 | 19.0 | 3762 | 0.3542 | 0.0018 | 0.1098 | 0.0178 | 0.0198 | 0.0722 |
| 0.2613 | 20.0 | 3960 | 0.3546 | 0.0018 | 0.1098 | 0.0178 | 0.0198 | 0.0722 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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