msaligs/ast_fine_tuned_music_genre_10

🎵 Music Genre Classification using AST

📌 Model Overview

This model is a fine-tuned Audio Spectrogram Transformer (AST) for music genre classification.

It predicts one of the following 10 genres:

• —blues, classical, country, disco, hiphop
• —jazz, metal, pop, reggae, rock

🧠 Architecture

• —Base Model: MIT/ast-finetuned-audioset
• —Type: Transformer (Audio Spectrogram Transformer)
• —Framework: PyTorch + Hugging Face Transformers

🎯 Task

Audio Classification (Music Genre Classification)

📊 Dataset

• —Training Data: Clean instrument stems (drums, vocals, bass, others)
• —Test Data: Noisy mashups with:
• —cross-song mixing
• —tempo variation
• —ESC-50 noise injection

⚙️ Preprocessing

• —Sampling rate: 16kHz
• —Fixed duration: 15 seconds
• —Padding / truncation applied
• —Feature extraction using ASTFeatureExtractor

📈 Performance

• —Metric: Macro F1 Score
• —Achieved: 0.87

🔗 Live Demo

Try the model here: 👉 https://huggingface.co/spaces/msaligs/music-genre-classifier

🚀 Usage

from transformers import ASTForAudioClassification, ASTFeatureExtractor

model = ASTForAudioClassification.from_pretrained("msaligs/ast_fine_tuned_music_genre_10")
feature_extractor = ASTFeatureExtractor.from_pretrained("msaligs/ast_fine_tuned_music_genre_10")