Instructions to use neerajs7/AST-audio-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neerajs7/AST-audio-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="neerajs7/AST-audio-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("neerajs7/AST-audio-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - audio-classification | |
| - music-genre-classification | |
| - audio-spectrogram-transformer | |
| - pytorch | |
| datasets: | |
| - messy-mashup | |
| metrics: | |
| - f1 | |
| library_name: transformers | |
| pipeline_tag: audio-classification | |
| # π΅ AST Music Genre Classifier | |
| Audio Spectrogram Transformer model fine-tuned for music genre classification on noisy mashup data. | |
| ## Model Description | |
| This model is a fine-tuned version of MIT's Audio Spectrogram Transformer (AST) with custom **Patchout regularization** for robust music genre classification. | |
| ### Key Features | |
| - π― Classifies 10 music genres with high accuracy | |
| - π Robust to noise, tempo variations, and audio mashups | |
| - π Achieves 98.19% F1-Score on public leaderboard | |
| - π¨ Custom Patchout regularization for better generalization | |
| ## Performance | |
| | Metric | Score | | |
| |--------|-------| | |
| | **Public Leaderboard** | **0.98191** Macro F1 | | |
| | **Private Leaderboard** | **0.97634** Macro F1 | | |
| | Competition | Kaggle Messy Mashup Genre Classification | | |
| ## Model Architecture | |
| - **Base Model:** `MIT/ast-finetuned-audioset-10-10-0.4593` | |
| - **Custom Components:** | |
| - Patchout regularization (time=0.4, freq=0.2) | |
| - Custom classification head with LayerNorm and GELU activation | |
| - Dropout layers for regularization | |
| - **Parameters:** ~86.5M trainable parameters | |
| ### Architecture Details | |
| Input (Audio) β AST Encoder β Patchout β Pooling β Classification Head β Genre (10 classes) | |
| **Classification Head:** | |
| LayerNorm(768) β Dropout(0.15) β Linear(768β384) β GELU β Dropout(0.1) β Linear(384β10) | |
| ## Citation | |
| @misc{ast-genre-classifier-2026, | |
| author = {Neeraj Surin}, | |
| title = {AST Music Genre Classifier with Patchout Regularization}, | |
| year = {2026}, | |
| publisher = {HuggingFace}, | |
| journal = {HuggingFace Model Hub}, | |
| howpublished = {\url{https://huggingface.co/neerajs7/AST-audio-classifier}} | |
| } | |
| ## Acknowledgments | |
| - Base AST model: MIT/ast-finetuned-audioset-10-10-0.4593 | |
| - Competition: Kaggle Messy Mashup Genre Classification Challenge | |
| - Framework: Hugging Face Transformers | |
| ## License | |
| MIT License - See LICENSE file for details | |
| **Model Card Authors:** Neeraj Surin | |
| **Model Card Contact:** https://huggingface.co/neerajs7 | |