---
library_name: transformers
tags:
- music
license: mit
language:
- en
base_model:
- MIT/ast-finetuned-audioset-10-10-0.4593
pipeline_tag: audio-classification
---

# AST Audio Classification Model (Messy Mashup)

## Introduction
This model is a fine-tuned **Audio Spectrogram Transformer (AST)** designed for audio classification tasks on the Messy Mashup dataset. It leverages pretrained audio representations and adapts them to classify audio inputs into predefined categories.

## Model Description

- **Developed by:** Rudransh Mathur  
- **Institution:** Indian Institute of Technology, Madras  
- **Model type:** Transformer-based Audio Classification Model  
- **Base model:** AST (fine-tuned on AudioSet)  
- **Framework:** Transformers (Hugging Face) + PyTorch  
- **License:** MIT

This model builds upon the pretrained AST architecture and is fine-tuned for improved performance on domain-specific audio data.

## Model Sources

- **Repository:** https://github.com/rudransmathur/dl-genai-project-26-t1
- **Kaggle Competetion:** https://www.kaggle.com/competitions/jan-2026-dl-gen-ai-project


## Intended Use
- Audio classification tasks  
- Music/audio tagging  
- Experimental research in audio transformers  

## Training Details

- **Dataset:** Messy Mashup Audio Dataset
  - GENRES: [blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock]
  - STEMS = [drums, vocals, bass, other]
- **Epochs:** 10  
- **Optimizer:** AdamW
- **Loss Function:** Cross-Entropy Loss
- **Scheduler:** Cosine Sheduler with warmup steps

### Preprocessing
- Randomly sampled audio stem files within the same genre and mixed to create a mixed song audio song similar to test dataset
- Added 5 seconds of noise from the noise dataset 2-3 times on a random basis in the audio file.
- Audio inputs converted using AST feature extractor  
- Sampling rate aligned with model requirements

### Performance
- **Best Validation Accuracy:** 0.87  
- **Best Validation Loss:** 0.40373
- **Best Test Accuracy:** 0.92  
- **Best Validation Loss:** 0.3458

## 🚀 Usage

```python
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor

model = AutoModelForAudioClassification.from_pretrained("rudranshmathur/ASTMessyMashup")
feature_extractor = AutoFeatureExtractor.from_pretrained("rudranshmathur/ASTMessyMashup")
```