---
language:
- hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Samyak29/synthetic-speaker-diarization-dataset-hindi-large
model-index:
- name: speaker-segmentation-fine-tuned-hindi
  results: []
---

# speaker-segmentation-fine-tuned-hindi

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the synthetic hindi dataset (https://huggingface.co/datasets/Samyak29/synthetic-speaker-diarization-dataset-hindi-large).
It achieves the following results on the evaluation set:
- Loss: 0.4442
- Der: 0.1448
- False Alarm: 0.0243
- Missed Detection: 0.0280
- Confusion: 0.0925

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.477         | 1.0   | 194  | 0.4877          | 0.1651 | 0.0259      | 0.0320           | 0.1072    |
| 0.3908        | 2.0   | 388  | 0.4562          | 0.1526 | 0.0231      | 0.0315           | 0.0980    |
| 0.3708        | 3.0   | 582  | 0.4356          | 0.1451 | 0.0242      | 0.0284           | 0.0924    |
| 0.3567        | 4.0   | 776  | 0.4461          | 0.1441 | 0.0244      | 0.0280           | 0.0917    |
| 0.3447        | 5.0   | 970  | 0.4442          | 0.1448 | 0.0243      | 0.0280           | 0.0925    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1