---
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
should probably proofread and complete it, then remove this comment. -->

# 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: 0.4367
- Model Preparation Time: 0.0045
- Der: 0.1440
- False Alarm: 0.0230
- Missed Detection: 0.0280
- Confusion: 0.0930

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4598        | 1.0   | 194  | 0.4815          | 0.0045                 | 0.1608 | 0.0231      | 0.0340           | 0.1036    |
| 0.3926        | 2.0   | 388  | 0.4519          | 0.0045                 | 0.1545 | 0.0225      | 0.0312           | 0.1008    |
| 0.3602        | 3.0   | 582  | 0.4442          | 0.0045                 | 0.1476 | 0.0232      | 0.0288           | 0.0956    |
| 0.3611        | 4.0   | 776  | 0.4388          | 0.0045                 | 0.1443 | 0.0228      | 0.0281           | 0.0934    |
| 0.3399        | 5.0   | 970  | 0.4367          | 0.0045                 | 0.1440 | 0.0230      | 0.0280           | 0.0930    |


### Framework versions

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