---
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
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# 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: 0.4632
- Der: 0.1810
- False Alarm: 0.0575
- Missed Detection: 0.0707
- Confusion: 0.0528

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4223        | 1.0   | 362  | 0.4718          | 0.1890 | 0.0617      | 0.0724           | 0.0549    |
| 0.3832        | 2.0   | 724  | 0.4581          | 0.1836 | 0.0561      | 0.0749           | 0.0526    |
| 0.3812        | 3.0   | 1086 | 0.4676          | 0.1846 | 0.0598      | 0.0702           | 0.0546    |
| 0.3607        | 4.0   | 1448 | 0.4610          | 0.1819 | 0.0585      | 0.0701           | 0.0533    |
| 0.3581        | 5.0   | 1810 | 0.4632          | 0.1810 | 0.0575      | 0.0707           | 0.0528    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1