---
library_name: transformers
license: mit
base_model: pyannote/segmentation-3.0
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Venkatesh4342/pyannote-hindi-diarization
model-index:
- name: speaker-segmentation-fine-tuned-hi
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# speaker-segmentation-fine-tuned-hi

This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the Venkatesh4342/pyannote-hindi-diarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0767
- Model Preparation Time: 0.0076
- Der: 0.0269
- False Alarm: 0.0116
- Missed Detection: 0.0113
- Confusion: 0.0040

## 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 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: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.1348        | 1.0   | 2674  | 0.1155          | 0.0076                 | 0.0404 | 0.0167      | 0.0145           | 0.0093    |
| 0.0936        | 2.0   | 5348  | 0.0908          | 0.0076                 | 0.0319 | 0.0111      | 0.0155           | 0.0053    |
| 0.0838        | 3.0   | 8022  | 0.0803          | 0.0076                 | 0.0283 | 0.0111      | 0.0126           | 0.0045    |
| 0.0751        | 4.0   | 10696 | 0.0779          | 0.0076                 | 0.0275 | 0.0122      | 0.0111           | 0.0042    |
| 0.0871        | 5.0   | 13370 | 0.0767          | 0.0076                 | 0.0269 | 0.0116      | 0.0113           | 0.0040    |


### Framework versions

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