---
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-eng
  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-eng

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4725
- Der: 0.1881
- False Alarm: 0.0588
- Missed Detection: 0.0751
- Confusion: 0.0541

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4468        | 1.0   | 181  | 0.4795          | 0.1939 | 0.0655      | 0.0711           | 0.0574    |
| 0.4043        | 2.0   | 362  | 0.4747          | 0.1877 | 0.0559      | 0.0758           | 0.0560    |
| 0.3907        | 3.0   | 543  | 0.4861          | 0.1943 | 0.0682      | 0.0720           | 0.0541    |
| 0.3971        | 4.0   | 724  | 0.4792          | 0.1895 | 0.0600      | 0.0748           | 0.0547    |
| 0.3886        | 5.0   | 905  | 0.4725          | 0.1881 | 0.0588      | 0.0751           | 0.0541    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1