---
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
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# 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