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---
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: 0.3691
- Model Preparation Time: 0.0185
- Der: 0.1163
- False Alarm: 0.0627
- Missed Detection: 0.0220
- Confusion: 0.0315
## 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 | 0.4010 | 0.0185 | 0.1278 | 0.0638 | 0.0228 | 0.0412 |
| 0.5209 | 2.0 | 94 | 0.3736 | 0.0185 | 0.1200 | 0.0612 | 0.0236 | 0.0351 |
| 0.4799 | 3.0 | 141 | 0.3710 | 0.0185 | 0.1165 | 0.0636 | 0.0207 | 0.0322 |
| 0.4621 | 4.0 | 188 | 0.3699 | 0.0185 | 0.1163 | 0.0621 | 0.0226 | 0.0315 |
| 0.4649 | 5.0 | 235 | 0.3691 | 0.0185 | 0.1163 | 0.0627 | 0.0220 | 0.0315 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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