---
base_model: HariprasathSB/indic-whisper-vulnerable
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 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. -->

# audio-abuse-feature

This model is a fine-tuned version of [HariprasathSB/indic-whisper-vulnerable](https://huggingface.co/HariprasathSB/indic-whisper-vulnerable) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4489
- Accuracy: 0.8814
- Macro Precision: 0.8557
- Macro Recall: 0.8472
- Macro F1-score: 0.8513

## 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: 1e-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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|
| 0.4633        | 0.4367 | 50   | 0.3753          | 0.8327   | 0.8321          | 0.8314       | 0.8317         |
| 0.345         | 0.8734 | 100  | 0.4170          | 0.8241   | 0.8612          | 0.8126       | 0.8150         |
| 0.2592        | 1.3100 | 150  | 0.3357          | 0.8512   | 0.8506          | 0.8502       | 0.8504         |
| 0.2097        | 1.7467 | 200  | 0.3142          | 0.8758   | 0.8757          | 0.8744       | 0.8749         |
| 0.1545        | 2.1834 | 250  | 0.3551          | 0.8721   | 0.8713          | 0.8718       | 0.8715         |
| 0.0829        | 2.6201 | 300  | 0.3916          | 0.8795   | 0.8797          | 0.8778       | 0.8786         |
| 0.0944        | 3.0568 | 350  | 0.4137          | 0.8721   | 0.8714          | 0.8730       | 0.8718         |
| 0.0416        | 3.4934 | 400  | 0.5350          | 0.8659   | 0.8677          | 0.8631       | 0.8646         |
| 0.0469        | 3.9301 | 450  | 0.5129          | 0.8733   | 0.8727          | 0.8726       | 0.8727         |
| 0.0247        | 4.3668 | 500  | 0.5543          | 0.8708   | 0.8713          | 0.8689       | 0.8698         |
| 0.0208        | 4.8035 | 550  | 0.5611          | 0.8696   | 0.8691          | 0.8688       | 0.8689         |


### Framework versions

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