---
base_model: Vignesh-M/Indic-whisper
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: whisper-indic-audio-abuse-feature
  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. -->

# whisper-indic-audio-abuse-feature

This model is a fine-tuned version of [Vignesh-M/Indic-whisper](https://huggingface.co/Vignesh-M/Indic-whisper) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5056
- Accuracy: 0.8868
- Macro Precision: 0.8642
- Macro Recall: 0.8509
- Macro F1-score: 0.8572

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1-score |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|
| 0.4635        | 0.4367 | 50   | 0.4010          | 0.8020   | 0.8176          | 0.8096       | 0.8014         |
| 0.3403        | 0.8734 | 100  | 0.3162          | 0.8684   | 0.8685          | 0.8668       | 0.8675         |
| 0.2689        | 1.3100 | 150  | 0.3025          | 0.8807   | 0.8838          | 0.8774       | 0.8793         |
| 0.2339        | 1.7467 | 200  | 0.3019          | 0.8782   | 0.8776          | 0.8777       | 0.8776         |
| 0.1723        | 2.1834 | 250  | 0.3715          | 0.8868   | 0.8870          | 0.8854       | 0.8861         |
| 0.1027        | 2.6201 | 300  | 0.3472          | 0.8930   | 0.8937          | 0.8912       | 0.8921         |
| 0.123         | 3.0568 | 350  | 0.3690          | 0.8795   | 0.8855          | 0.8751       | 0.8776         |
| 0.0497        | 3.4934 | 400  | 0.4423          | 0.8918   | 0.8916          | 0.8907       | 0.8911         |
| 0.0534        | 3.9301 | 450  | 0.3937          | 0.9041   | 0.9048          | 0.9024       | 0.9033         |
| 0.0235        | 4.3668 | 500  | 0.4753          | 0.8979   | 0.8993          | 0.8958       | 0.8970         |
| 0.0196        | 4.8035 | 550  | 0.5204          | 0.8967   | 0.8982          | 0.8944       | 0.8957         |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1