---
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: 0.6384
- Model Preparation Time: 0.0039
- Der: 0.2151
- False Alarm: 0.0579
- Missed Detection: 0.0412
- Confusion: 0.1160

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.6696        | 1.0   | 151  | 0.6690          | 0.0039                 | 0.2248 | 0.0590      | 0.0445           | 0.1214    |
| 0.6283        | 2.0   | 302  | 0.6558          | 0.0039                 | 0.2187 | 0.0575      | 0.0417           | 0.1196    |
| 0.6013        | 3.0   | 453  | 0.6436          | 0.0039                 | 0.2159 | 0.0584      | 0.0405           | 0.1170    |
| 0.5765        | 4.0   | 604  | 0.6379          | 0.0039                 | 0.2135 | 0.0579      | 0.0413           | 0.1142    |
| 0.5594        | 5.0   | 755  | 0.6384          | 0.0039                 | 0.2151 | 0.0579      | 0.0412           | 0.1160    |


### Framework versions

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