---
language:
- eng
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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4692
- Der: 0.1840
- False Alarm: 0.0616
- Missed Detection: 0.0711
- Confusion: 0.0513

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3907        | 1.0   | 362  | 0.4760          | 0.1920 | 0.0622      | 0.0739           | 0.0559    |
| 0.4104        | 2.0   | 724  | 0.4737          | 0.1912 | 0.0704      | 0.0688           | 0.0520    |
| 0.3848        | 3.0   | 1086 | 0.4567          | 0.1809 | 0.0595      | 0.0709           | 0.0504    |
| 0.3688        | 4.0   | 1448 | 0.4680          | 0.1831 | 0.0581      | 0.0738           | 0.0512    |
| 0.344         | 5.0   | 1810 | 0.4692          | 0.1840 | 0.0616      | 0.0711           | 0.0513    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1