---
library_name: transformers
language:
- eng
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/callhome
model-index:
- name: speaker-segmentation-fine-tuned-callhome-eng-forproject
  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-callhome-eng-forproject

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the diarizers-community/callhome dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4600
- Model Preparation Time: 0.0051
- Der: 0.1818
- False Alarm: 0.0578
- Missed Detection: 0.0721
- Confusion: 0.0518

## 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 | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.392         | 1.0   | 362  | 0.4730          | 0.0051                 | 0.1926 | 0.0622      | 0.0736           | 0.0568    |
| 0.4053        | 2.0   | 724  | 0.4586          | 0.0051                 | 0.1838 | 0.0625      | 0.0704           | 0.0509    |
| 0.3865        | 3.0   | 1086 | 0.4537          | 0.0051                 | 0.1811 | 0.0574      | 0.0723           | 0.0514    |
| 0.3571        | 4.0   | 1448 | 0.4570          | 0.0051                 | 0.1805 | 0.0551      | 0.0740           | 0.0514    |
| 0.3409        | 5.0   | 1810 | 0.4600          | 0.0051                 | 0.1818 | 0.0578      | 0.0721           | 0.0518    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1