julius
Nice DSP sweets: resampling, FFT Convolutions. All with PyTorch, differentiable and with CUDA support.
Decision gist · record as of 2026-08-14
Yes. Julius is actively maintained, has no known vulnerabilities, installs with minimal friction (torch only), and fills a genuine gap for GPU-accelerated, differentiable DSP in PyTorch workflows. Install it if you need signal processing as part of a neural network or GPU pipeline; skip it if you only do offline audio analysis on CPU.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.9.0 and torch installed.
- Low friction: pure Python wheel with only torch as a runtime dependency.
- Actively maintained with a recent release (72 days old), last commit 2026-06-03, and 461 repository stars.
License · maintenance · safety
MIT License (permissive) — MIT license (permissive) — you can use, modify, and distribute julius freely in commercial and private projects with minimal restrictions.
last release 2026-06-03 (72 days) · last repo commit 2026-06-03 · 461 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,735,249 downloads/mo, #2,913 on PyPI
Alternatives
Verify before relying
pip install julius
import julius
import torch
signal = torch.randn(6, 4, 1024)
resampled = julius.resample_frac(signal, 100, 70)
low_freqs = julius.lowpass_filter(signal, 0.1)- Whether FFTConv1d performance advantage over torch.nn.Conv1d holds for your specific tensor sizes and hardware.
- TorchScript compatibility status and any known limitations beyond what the description states.
What it is and what it does
Julius is a PyTorch-based DSP library that makes audio and signal processing operations differentiable and GPU-accelerated. It implements sinc resampling, FFT-based convolutions for large kernels, FIR filter banks (lowpass, highpass, bandpass), and frequency-band decomposition in mel-scale space. All operations run on CUDA and are compatible with TorchScript, making them suitable for training neural networks that incorporate signal processing or for real-time inference pipelines.
The library is designed for cases where you need DSP operations to be part of a differentiable computation graph or where GPU acceleration matters. Its resampling is faster than resampy even on CPU and negligible on GPU for typical sample-rate ratios. FFT convolutions outperform standard convolution for kernels >= 128 samples, especially with many channels or large batch sizes.
Use it for
- Train end-to-end neural networks that include learnable audio resampling or filtering layers.
- Accelerate batch audio preprocessing on GPU by replacing CPU-based librosa or scipy operations.
- Implement parametric EQ or frequency-band-dependent processing as differentiable modules.
- Build real-time audio inference pipelines using TorchScript-compiled DSP operations.
- Perform large-kernel convolutions on long audio signals faster than torch.nn.Conv1d.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Julius is actively maintained, has no known vulnerabilities, installs with minimal friction (torch only), and fills a genuine gap for GPU-accelerated, differentiable DSP in PyTorch workflows. Install it if you need signal processing as part of a neural network or GPU pipeline; skip it if you only do offline audio analysis on CPU.
Install
julius on PyPI
Before you install
Low friction: pure Python wheel with only torch as a runtime dependency. Actively maintained with a recent release (72 days old), last commit 2026-06-03, and 461 repository stars.
Requires Python >= 3.9.0 and torch installed.
License in practice
MIT license (permissive) — you can use, modify, and distribute julius freely in commercial and private projects with minimal restrictions.
Quickstart
pip install julius
import julius
import torch
signal = torch.randn(6, 4, 1024)
resampled = julius.resample_frac(signal, 100, 70)
low_freqs = julius.lowpass_filter(signal, 0.1)Verify before relying
- Whether FFTConv1d performance advantage over torch.nn.Conv1d holds for your specific tensor sizes and hardware.
- TorchScript compatibility status and any known limitations beyond what the description states.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetorch |
| Maintenance | Actively maintained 72 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,735,249 / month, #2,913 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseTopic :: Multimedia :: Sound/AudioTopic :: Scientific/Engineering |
Evidence: julius-0.2.8-py3-none-any.whl
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