---
library_name: transformers
language:
- hi
license: mit
base_model: pyannote/speaker-diarization-3.1
tags:
- speaker-diarization
- speaker-segmentation
- generated_from_trainer
datasets:
- Shreyask09/synthetic-speaker-diarization-dataset-hindi
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 Shreyask09/synthetic-speaker-diarization-dataset-hindi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3013
- Model Preparation Time: 0.004
- Der: 0.1018
- False Alarm: 0.0131
- Missed Detection: 0.0241
- Confusion: 0.0646

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Der    | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.3506        | 1.0   | 219  | 0.3386          | 0.004                  | 0.1165 | 0.0156      | 0.0288           | 0.0722    |
| 0.2898        | 2.0   | 438  | 0.3218          | 0.004                  | 0.1080 | 0.0131      | 0.0267           | 0.0682    |
| 0.2479        | 3.0   | 657  | 0.3004          | 0.004                  | 0.1034 | 0.0134      | 0.0245           | 0.0655    |
| 0.2413        | 4.0   | 876  | 0.3027          | 0.004                  | 0.1020 | 0.0129      | 0.0244           | 0.0647    |
| 0.2506        | 5.0   | 1095 | 0.3013          | 0.004                  | 0.1018 | 0.0131      | 0.0241           | 0.0646    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0