Spectrogram-VisionTransformer

A project that applies Vision Transformer (ViT) architectures to spectrograms for audio / speech tasks (e.g. classification, recognition).
Transforms audio signals into spectrogram “images” and uses transformer models to learn from them.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Examples
• Model / Architecture
• Experiments & Results
• Dataset
• Training
• Evaluation
• Contributing
• License
• References

Introduction

Deep learning for audio often uses convolutional networks on spectrogram inputs.
This project explores using Vision Transformers (ViT) on spectrograms to capture global patterns and long-range dependencies in time-frequency space.

The key idea:

1. Convert a raw audio waveform → spectrogram (e.g. Mel spectrogram)
2. Treat the spectrogram as a 2D image
3. Feed patches / tokens into a Transformer / ViT model
4. Train / fine-tune for audio tasks (classification, detection, etc.)

Features

• Pipeline for audio → spectrogram → transformer input
• Preprocessing scripts (e.g. STFT, Mel, normalization)
• Model architectures based on ViT / Transformer
• Training and evaluation scripts
• Support for GPU / multi-GPU
• Logging, checkpoints, and result saving

Installation

# Clone this repository
git clone https://github.com/Sree14hari/Spectrogram-VisionTransformer.git
cd Spectrogram-VisionTransformer

# (Optional) create virtual environment
python -m venv venv
source venv/bin/activate   # or venv\Scripts\activate on Windows

# Install dependencies
pip install -r requirements.txt

You’ll need packages like torch, numpy, librosa (or similar for spectrograms), etc.

Usage

Prepare data

• Place your audio files / dataset in a folder
• (Optional) a config or script to convert them into spectrograms

Train a model

python train.py --config configs/your_config.yaml

Evaluate or infer

python evaluate.py --checkpoint path/to/model.pth --data your_test_data

Example script

You could include a sample script run_demo.py that loads a sample audio, converts to spectrogram, runs through the model, and prints results.

Model / Architecture

Describe the architecture you used:

• Patch size (e.g. 16×16)
• Number of transformer layers / heads / embedding dimension
• Any modifications you did for spectrogram data (positional embeddings, time-frequency embedding, masking, etc.)
• Loss functions, regularization, etc.

If you adopted or adapted from other works (e.g. ASiT: Audio Spectrogram vIsion Transformer ([arXiv][1]) or other similar works), mention it here.

Experiments & Results

Provide a table of your experiments, for example:

Task / Dataset	Model Variant	Accuracy / Metric	Notes
Speech Commands	ViT-base	95.6%	baseline
Your dataset	Your model	XX.X%	your experiment

Include charts, loss curves, confusion matrices, etc.

Dataset

Describe the dataset(s) you used:

• Name (e.g. Speech Commands, ESC-50, custom)
• Number of classes
• Preprocessing (sampling rate, window size, overlap, normalization)
• Train / val / test splits

Training

Detail your training settings, e.g.:

• Learning rate, scheduler
• Batch size
• Number of epochs
• Optimizer (Adam, SGD, etc.)
• Data augmentation (if any)
• Hardware setup

Evaluation

• How metrics are computed (accuracy, F1, AUC, etc.)
• Any special evaluation scripts
• How to reproduce results

Contributing

If you welcome contributions, you can say:

• Please open issues or pull requests
• Follow the code style
• Add tests / documentation
• Cite your work

License

State your license (e.g. MIT, Apache, etc.)

MIT License
© Sreehari R