---
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: 0.4597
- Der: 0.1816
- False Alarm: 0.0595
- Missed Detection: 0.0708
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3871        | 1.0   | 362  | 0.4735          | 0.1913 | 0.0608      | 0.0744           | 0.0561    |
| 0.4079        | 2.0   | 724  | 0.4605          | 0.1850 | 0.0626      | 0.0700           | 0.0524    |
| 0.3871        | 3.0   | 1086 | 0.4603          | 0.1816 | 0.0581      | 0.0726           | 0.0509    |
| 0.3642        | 4.0   | 1448 | 0.4624          | 0.1817 | 0.0575      | 0.0723           | 0.0519    |
| 0.3421        | 5.0   | 1810 | 0.4597          | 0.1816 | 0.0595      | 0.0708           | 0.0513    |


### Framework versions

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