TorchFX: A Modern Approach to Audio DSP with PyTorch and GPU Acceleration
The increasing complexity and real-time processing demands of
audio signals require optimized algorithms that utilize the computational power of Graphics Processing Units (GPUs).
Existing Digital Signal Processing (DSP) libraries often do not provide
the necessary efficiency and flexibility, particularly for integrating
with Artificial Intelligence (AI) models. In response, we introduce TorchFX: a GPU-accelerated Python library for DSP, engineered to facilitate sophisticated audio signal processing. Built on
the PyTorch framework, TorchFX offers an Object-Oriented interface similar to torchaudio but enhances functionality with a novel
pipe operator for intuitive filter chaining. The library provides a
comprehensive suite of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, with a focus on multichannel
audio, thereby facilitating the integration of DSP and AI-based
approaches.
Our benchmarking results demonstrate significant
efficiency gains over traditional libraries like SciPy, particularly
in multichannel contexts. While there are current limitations in
GPU compatibility, ongoing developments promise broader support and real-time processing capabilities. TorchFX aims to become a useful tool for the community, contributing to innovation
in GPU-accelerated DSP. TorchFX is publicly available on GitHub
at https://github.com/matteospanio/torchfx.