---
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: 0.3788
- Model Preparation Time: 0.0136
- Der: 0.1188
- False Alarm: 0.0628
- Missed Detection: 0.0216
- Confusion: 0.0345

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.6461        | 1.0   | 47   | 0.4021          | 0.0136                 | 0.1318 | 0.0649      | 0.0218           | 0.0450    |
| 0.5224        | 2.0   | 94   | 0.3720          | 0.0136                 | 0.1179 | 0.0632      | 0.0217           | 0.0330    |
| 0.4869        | 3.0   | 141  | 0.3716          | 0.0136                 | 0.1180 | 0.0636      | 0.0208           | 0.0337    |
| 0.4707        | 4.0   | 188  | 0.3707          | 0.0136                 | 0.1175 | 0.0622      | 0.0224           | 0.0329    |
| 0.4697        | 5.0   | 235  | 0.3788          | 0.0136                 | 0.1188 | 0.0628      | 0.0216           | 0.0345    |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1