---
library_name: transformers
language:
- id
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- modality:audio
- modality:text
- format:parquet
- generated_from_trainer
datasets:
- speaker-segmentation
model-index:
- name: speaker-segmentation-fine-tuned-id
  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-id

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the speaker-segmentation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5964
- Model Preparation Time: 0.0059
- Der: 0.2071
- False Alarm: 0.0393
- Missed Detection: 0.0410
- Confusion: 0.1268

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.7607        | 1.0   | 72   | 0.6580          | 0.0059                 | 0.2281 | 0.0444      | 0.0462           | 0.1375    |
| 0.6374        | 2.0   | 144  | 0.6117          | 0.0059                 | 0.2152 | 0.0385      | 0.0452           | 0.1315    |
| 0.5943        | 3.0   | 216  | 0.6168          | 0.0059                 | 0.2163 | 0.0431      | 0.0412           | 0.1320    |
| 0.5547        | 4.0   | 288  | 0.6026          | 0.0059                 | 0.2077 | 0.0401      | 0.0410           | 0.1265    |
| 0.5579        | 5.0   | 360  | 0.5964          | 0.0059                 | 0.2071 | 0.0393      | 0.0410           | 0.1268    |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1