---
library_name: transformers
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: []
---

<!-- 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-hindi

This model is a fine-tuned version of [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) on the Samyak29/synthetic-speaker-diarization-dataset-hindi-large dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4284
- Model Preparation Time: 0.0095
- Der: 0.1417
- False Alarm: 0.0235
- Missed Detection: 0.0281
- Confusion: 0.0901

## 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 | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4708        | 1.0   | 194  | 0.4808          | 0.0095                 | 0.1613 | 0.0255      | 0.0323           | 0.1035    |
| 0.388         | 2.0   | 388  | 0.4553          | 0.0095                 | 0.1499 | 0.0225      | 0.0314           | 0.0960    |
| 0.3654        | 3.0   | 582  | 0.4368          | 0.0095                 | 0.1433 | 0.0242      | 0.0278           | 0.0913    |
| 0.363         | 4.0   | 776  | 0.4296          | 0.0095                 | 0.1410 | 0.0239      | 0.0279           | 0.0893    |
| 0.3388        | 5.0   | 970  | 0.4284          | 0.0095                 | 0.1417 | 0.0235      | 0.0281           | 0.0901    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1