---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- talkbank/callhome
model-index:
- name: callhome_finetuned_v1
  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. -->

# callhome_finetuned_v1

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the talkbank/callhome eng dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4727
- Model Preparation Time: 0.004
- Der: 0.1798
- False Alarm: 0.0603
- Missed Detection: 0.0699
- Confusion: 0.0496

## 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 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: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4176        | 1.0   | 362  | 0.4821          | 0.004                  | 0.1938 | 0.0608      | 0.0745           | 0.0584    |
| 0.3931        | 2.0   | 724  | 0.4564          | 0.004                  | 0.1853 | 0.0552      | 0.0760           | 0.0541    |
| 0.3662        | 3.0   | 1086 | 0.4535          | 0.004                  | 0.1812 | 0.0521      | 0.0765           | 0.0526    |
| 0.3575        | 4.0   | 1448 | 0.4660          | 0.004                  | 0.1815 | 0.0590      | 0.0699           | 0.0526    |
| 0.3449        | 5.0   | 1810 | 0.4605          | 0.004                  | 0.1802 | 0.0629      | 0.0670           | 0.0503    |
| 0.3288        | 6.0   | 2172 | 0.4659          | 0.004                  | 0.1794 | 0.0573      | 0.0717           | 0.0504    |
| 0.3215        | 7.0   | 2534 | 0.4676          | 0.004                  | 0.1803 | 0.0593      | 0.0709           | 0.0501    |
| 0.3261        | 8.0   | 2896 | 0.4667          | 0.004                  | 0.1786 | 0.0597      | 0.0702           | 0.0488    |
| 0.3193        | 9.0   | 3258 | 0.4696          | 0.004                  | 0.1796 | 0.0597      | 0.0705           | 0.0494    |
| 0.3203        | 10.0  | 3620 | 0.4727          | 0.004                  | 0.1798 | 0.0603      | 0.0699           | 0.0496    |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.0