---
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- diarizers-community/ami
model-index:
- name: speaker-segmentation-fine-tuned-ami
  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-ami

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/ami ihm dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3660
- Der: 0.1396
- False Alarm: 0.0503
- Missed Detection: 0.0578
- Confusion: 0.0314

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4133        | 1.0   | 1427 | 0.3629          | 0.1388 | 0.0424      | 0.0646           | 0.0318    |
| 0.3907        | 2.0   | 2854 | 0.3638          | 0.1400 | 0.0492      | 0.0583           | 0.0324    |
| 0.3651        | 3.0   | 4281 | 0.3631          | 0.1403 | 0.0506      | 0.0581           | 0.0316    |
| 0.3692        | 4.0   | 5708 | 0.3643          | 0.1394 | 0.0489      | 0.0591           | 0.0314    |
| 0.3484        | 5.0   | 7135 | 0.3660          | 0.1396 | 0.0503      | 0.0578           | 0.0314    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1