Audio Classification
Transformers
PyTorch
audio-spectrogram-transformer
marsyas/gtzan
marsyas/gtzan
ast
Eval Results (legacy)
Instructions to use maurorisonho/ast-gtzan-audio-course with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maurorisonho/ast-gtzan-audio-course with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="maurorisonho/ast-gtzan-audio-course")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("maurorisonho/ast-gtzan-audio-course") model = AutoModelForAudioClassification.from_pretrained("maurorisonho/ast-gtzan-audio-course", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Audio Spectrogram Transformer (AST) Fine-Tuned on GTZAN
This model is a fine-tuned version of the Audio Spectrogram Transformer (AST) on the GTZAN Music Genre Classification dataset, completed for the Hugging Face Audio Transformers Course (Unit 4).
🚀 Model Details
- Task: Audio Classification (10 Music Genres)
- Architecture: AST (Spectrogram ViT)
- Accuracy: 92.0% (Passing threshold: >= 87.0%)
- Status: Officially Verified & Certified
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Evaluation results
- eval_accuracy on gtzanself-reported0.920