---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- syvai/synthetic-diarization-mixed-speakers
model-index:
- name: speaker-segmentation-fine-tuned
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speaker-segmentation-fine-tuned

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the syvai/synthetic-diarization-mixed-speakers dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2088
- Model Preparation Time: 0.0013
- Der: 0.0754
- False Alarm: 0.0218
- Missed Detection: 0.0180
- Confusion: 0.0356

## 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: 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2579        | 1.0   | 1232 | 0.2571          | 0.0013                 | 0.0941 | 0.0253      | 0.0190           | 0.0498    |
| 0.2437        | 2.0   | 2464 | 0.2483          | 0.0013                 | 0.0898 | 0.0230      | 0.0196           | 0.0471    |
| 0.2169        | 3.0   | 3696 | 0.2200          | 0.0013                 | 0.0787 | 0.0232      | 0.0179           | 0.0376    |
| 0.206         | 4.0   | 4928 | 0.2104          | 0.0013                 | 0.0760 | 0.0218      | 0.0181           | 0.0361    |
| 0.1928        | 5.0   | 6160 | 0.2088          | 0.0013                 | 0.0754 | 0.0218      | 0.0180           | 0.0356    |


### Framework versions

- Transformers 4.53.0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2