Audio Spectrogram Transformer (AST)
| Property | Value |
|---|---|
| Parameter Count | 86.6M |
| License | BSD-3-Clause |
| Paper | AST: Audio Spectrogram Transformer |
| Framework | PyTorch |
| Tags | Audio Classification, Transformers |
What is ast-finetuned-audioset-10-10-0.4593?
The Audio Spectrogram Transformer (AST) is an innovative model that bridges the gap between computer vision and audio processing. Developed by MIT researchers, this model adapts the Vision Transformer (ViT) architecture for audio classification tasks by converting audio inputs into spectrograms and processing them as images.
Implementation Details
AST operates by first transforming audio signals into spectrograms, which are visual representations of sound frequencies over time. The model then processes these spectrograms using a transformer-based architecture similar to ViT. With 86.6M parameters and utilizing F32 tensor types, this model has been specifically fine-tuned on the AudioSet dataset to achieve state-of-the-art performance in audio classification tasks.
- Leverages Vision Transformer architecture for audio processing
- Implements spectrogram-based audio analysis
- Utilizes PyTorch framework with Safetensors support
- Supports inference endpoints for practical deployment
Core Capabilities
- High-accuracy audio classification across AudioSet categories
- Efficient processing of audio spectrograms
- Robust feature extraction from audio signals
- State-of-the-art performance on audio classification benchmarks
Frequently Asked Questions
Q: What makes this model unique?
AST's uniqueness lies in its innovative approach of treating audio classification as an image recognition task by processing spectrograms through a Vision Transformer architecture, enabling superior performance compared to traditional audio processing methods.
Q: What are the recommended use cases?
The model is ideal for audio classification tasks, including sound event detection, music classification, and acoustic scene analysis. It's particularly well-suited for applications requiring precise audio categorization within the AudioSet classes.
