Create README.md

README.md

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+---
+license: mit
+tags:
+- audio
+- audio-classification
+- speech
+- music
+- pytorch
+- ast
+- VAD
+library_name: pytorch
+pipeline_tag: audio-classification
+---
+
+# Audio Classification with AST (Music / Non-Speech / Speech)
+
+This model is a **fine-tuned Audio Spectrogram Transformer (AST)** for **audio classification**.  
+It classifies audio clips into three categories:
+
+- **Speech**
+- **Music**
+- **Non-Speech**
+
+The model operates on **log-Mel filterbank features extracted from 16 kHz audio** and uses a **Transformer-based architecture** adapted for audio spectrograms.
+
+Training and source code are available here:
+
+**GitHub repository:**  
+https://github.com/areffarhadi/audio-classification/tree/main/AST-model
+
+---
+
+# Model Details
+
+## Architecture
+
+The model is based on **Audio Spectrogram Transformer (AST)**, which applies the Vision Transformer architecture to audio spectrograms.
+
+Key characteristics:
+
+- Transformer encoder architecture
+- Patch-based spectrogram representation
+- Learned positional embeddings
+- Classification token and distillation token
+- Final classification head fine-tuned for the target classes
+
+### Input
+
+- **Audio format:** WAV
+- **Sampling rate:** 16 kHz
+- **Features:** Log-Mel filterbank
+- **Mel bins:** 128
+- **Target length:** 1024 frames
+
+### Output Classes
+
+| Index | Label |
+|------|------|
+| 0 | Music |
+| 1 | Non-Speech |
+| 2 | Speech |
+
+---
+
+# Usage
+
+[**Inference code**](https://github.com/areffarhadi/audio-classification/blob/main/AST-model/ast_inference_with_manifest.py)