TorchFX is a modern Python library for high-performance digital signal processing in audio, leveraging PyTorch and GPU acceleration. Built for researchers, engineers, and developers who need fast, flexible, and differentiable audio processing.

• ⚡ GPU Acceleration - Built on PyTorch for high-performance audio processing on CUDA-enabled devices
• 🔗 Composable Pipelines - Chain filters with the pipe operator (|) for sequential processing
• ➕ Parallel Processing - Combine filters with the add operator (+) for parallel filter combination
• 🧠 PyTorch Native - All filters are torch.nn.Module subclasses, enabling gradient-based optimization
• 🎯 Simple & Intuitive - Clean, object-oriented API designed for ease of use
• ⚙️ Highly Extensible - Create custom filters and effects by extending base classes
• 📊 Performance Optimized - Substantial performance gains over SciPy for long and multichannel signals

pip install torchfx

Or install from source:

git clone https://github.com/matteospanio/torchfx
cd torchfx
pip install -e .

import torch
from torchfx import Wave
from torchfx.filter import LoButterworth, ParametricEQ

# Load audio
wave = Wave.from_file("audio.wav")

# Create filters
lowpass = LoButterworth(cutoff=5000, order=4, fs=wave.fs)
eq = ParametricEQ(frequency=1000, q=2.0, gain=3.0, fs=wave.fs)

# Sequential processing with pipe operator (|)
processed = wave | lowpass | eq

# Parallel processing with add operator (+)
stereo_enhancer = lowpass + eq
enhanced = wave | stereo_enhancer

# Save result
processed.save("output.wav")

• Full Documentation - Complete guides and API reference
• Getting Started - Installation and first steps
• Tutorials - Practical examples and use cases
• API Reference - Detailed API documentation
• Blog - Updates, releases, and insights

• Contributing Guide - How to contribute to TorchFX
• Style Guide - Coding standards and conventions
• Roadmap - Future plans and priorities

We welcome contributions from everyone! Please read our Contributing Guide to get started.

If you use TorchFX in your research, please cite our paper:

@conference{Spanio2025torchfx,
	author = {Spanio, Matteo and Rodà, Antonio},
	title = {TORCHFX: A MODERN APPROACH TO AUDIO DSP WITH PYTORCH AND GPU ACCELERATION},
	year = {2025},
	journal = {Proceedings of the International Conference on Digital Audio Effects, DAFx},
	pages = {390 – 395},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105028935688&partnerID=40&md5=552e54afc1a074cbd1b7e8ed4ad1c010},
	type = {Conference paper},
}

This project is licensed under the terms of the GNU General Public License v3.0. See the LICENSE file for details.

This project uses the following third-party libraries:

• PyTorch – BSD-style license
• NumPy – BSD 3-Clause License
• SoundFile – BSD 3-Clause License

Their respective license texts are included in the licenses/ directory.