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---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-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. -->
# speaker-segmentation-fine-tuned-callhome-jpn
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome jpn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7545
- Der: 0.2264
- False Alarm: 0.0488
- Missed Detection: 0.1331
- Confusion: 0.0445
## 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: 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: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.562 | 1.0 | 328 | 0.7605 | 0.2346 | 0.0492 | 0.1357 | 0.0497 |
| 0.5564 | 2.0 | 656 | 0.7540 | 0.2294 | 0.0512 | 0.1328 | 0.0454 |
| 0.5273 | 3.0 | 984 | 0.7724 | 0.2321 | 0.0438 | 0.1419 | 0.0464 |
| 0.5037 | 4.0 | 1312 | 0.7599 | 0.2276 | 0.0475 | 0.1349 | 0.0452 |
| 0.5041 | 5.0 | 1640 | 0.7545 | 0.2264 | 0.0488 | 0.1331 | 0.0445 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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