---
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-2
  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-ami-2

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: 0.3764
- Der: 0.1401
- False Alarm: 0.0503
- Missed Detection: 0.0575
- Confusion: 0.0323

## 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: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4149        | 1.0   | 1427  | 0.3607          | 0.1407 | 0.0492      | 0.0593           | 0.0323    |
| 0.3915        | 2.0   | 2854  | 0.3684          | 0.1422 | 0.0460      | 0.0621           | 0.0340    |
| 0.3748        | 3.0   | 4281  | 0.3730          | 0.1419 | 0.0530      | 0.0570           | 0.0318    |
| 0.3778        | 4.0   | 5708  | 0.3649          | 0.1409 | 0.0472      | 0.0611           | 0.0326    |
| 0.3565        | 5.0   | 7135  | 0.3723          | 0.1415 | 0.0501      | 0.0591           | 0.0324    |
| 0.3566        | 6.0   | 8562  | 0.3740          | 0.1406 | 0.0499      | 0.0584           | 0.0323    |
| 0.3534        | 7.0   | 9989  | 0.3736          | 0.1399 | 0.0493      | 0.0581           | 0.0325    |
| 0.3418        | 8.0   | 11416 | 0.3744          | 0.1397 | 0.0500      | 0.0577           | 0.0321    |
| 0.3388        | 9.0   | 12843 | 0.3777          | 0.1403 | 0.0505      | 0.0574           | 0.0324    |
| 0.346         | 10.0  | 14270 | 0.3764          | 0.1401 | 0.0503      | 0.0575           | 0.0323    |


### Framework versions

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