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AST Audio Classifier

Audio Spectrogram Transformer model fine-tuned for music genre classification on noisy mashup data.

TipoOtherVersãoRevision 6204922Cnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
330 MB
Versão
vRevision 6204922
Atualizado
17 de out. de 2026
Downloads
0
Fonte
Hugging Face

Descrição da fonte

🎵 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

  • 🎯 Classifies 10 music genres with high accuracy
  • 🔊 Robust to noise, tempo variations, and audio mashups
  • 🚀 Achieves 98.19% F1-Score on public leaderboard
  • 🎨 Custom Patchout regularization for better generalization

Performance

MetricScore
Public Leaderboard0.98191 Macro F1
Private Leaderboard0.97634 Macro F1
CompetitionKaggle 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) → AST Encoder → Patchout → Pooling → Classification Head → Genre (10 classes)

Classification Head:

LayerNorm(768) → Dropout(0.15) → Linear(768→384) → GELU → Dropout(0.1) → Linear(384→10)

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

Fontes

1 fonte
Hugging FacePrimárioVersão 620492243374a3248661385752dcb2a551e67cc1
huggingface.co/neerajs7/AST-audio-classifier/tree/620492243374a3248661385752dcb2a551e67cc1

Verificado 29 de ago.

Artefatos de modelo

1 artefato

model.pth

pth · 330 MB · SHA-256 e2b49489c3a1…6960 · Hugging Face

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Trechos de fonte

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--- license: mit tags: - audio-classification - music-genre-classification - audio-spectrogram-transformer - pytorch datasets: - messy-mashup metrics: - f1 library_name: transformers pipeline_tag: audio-classification --- # 🎵 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 - 🎯 Classifies 10 music genres with high accuracy - 🔊 Robust to noise, tempo variations, and audio mashups - 🚀 Achieves 98.19% F1-Score on public leaderboard - 🎨 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) → AST Encoder → Patchout → Pooling → Classification Head → Genre (10 classes) **Classification Head:** LayerNorm(768) → Dropout(0.15) → Linear(768→384) → GELU → Dropout(0.1) → Linear(384→10) ## 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

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