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A producer's four-year search for a local, cloud-free music generator ended with him building DAWalka himself—an open-source Audio Unit plugin that runs Stable Audio 3 directly inside Logic Pro on Apple Silicon.
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EngMisha Ivanov, from Sergey's team, spent years waiting for a tool that could generate music and sound effects directly inside his working DAW—no cloud queue, no subscription, no sending unfinished drafts off to someone else's server. When nothing like that showed up, he built it himself.
The result is DAWalka, an open-source Audio Unit plugin for Logic Pro. It runs natively on Apple Silicon, is powered by Stable Audio 3, generates audio locally, and keeps the creative flow inside the project itself rather than in a browser tab or someone else's infrastructure.
Audio Unit format: The plugin installs into Logic Pro as an AU instrument and runs directly inside your music project.
T2A / A2A modes: Text-to-audio for generating new fragments, and audio-to-audio for reworking your own files in the style of a prompt.
Apple Silicon, fully local: After the initial model download, generation runs entirely on Macs with M1–M4 chips via MLX and Metal—no persistent cloud loop required.
For a producer, a cloud-based generator often breaks the very flow it's supposed to support: open a browser, describe the idea outside the project, wait for a response, then drag the result back in. For rough drafts, unreleased material, and live sessions, that's not just inconvenient—it's a real risk and a loss of momentum.
Creative drafts should stay with their creator. DAWalka doesn't depend on a constant external service: the idea, the prompt, the source audio, and the output all stay on the Mac once the models are installed.
The tool needs to live inside Logic Pro. A musician doesn't need a separate website sitting on top of their DAW—they need a plugin that understands the project's tempo and timeline length, and hands the result straight back to the track.
Open source beats closed-box magic. The code, installation process, and project structure are all public on GitHub, so anyone can verify the approach, build their own version, and avoid depending on an opaque black-box service.
The first version is honest about its limits. This isn't a polished SaaS promising flawless results—it's a working tool built by a practitioner: try it, figure out where it's actually useful, then keep improving it.
DAWalka focuses on two real use cases: generating sound from text, or reimagining your own audio file. There's no elaborate onboarding funnel—just two straightforward modes that solve real musical problems.
T2A: text-to-audio. Describe a fragment, a mood, an instrumental phrase, or an effect, and get audio back directly inside your working session.
A2A: audio-to-audio. Drop in a WAV, AIFF, FLAC, or OGG file and reshape it according to a text prompt while preserving its original duration.
Stable Audio 3 under the hood. The plugin uses Apple Silicon–optimized variants of Stable Audio 3, including dedicated models for music, sound effects, and a higher-quality balanced mode.
Results you can drag straight onto a track. Generated audio doesn't just land in some random folder like an orphaned file—it comes back into your project ready for a familiar drag-and-drop.
What actually matters for musical work is that AI generation doesn't turn your session into a waiting game. That's why the heavy computation runs in a separate process, keeping Logic Pro responsive, while the plugin automatically picks up project parameters wherever it can, without manual tweaking.
The plugin reads the project's BPM and timeline length so generated fragments fit the musical context.
Generation runs in a separate process, so Logic Pro shouldn't freeze while the model is computing.
Acceleration runs through MLX and Metal on Apple Silicon, avoiding a heavy PyTorch stack inside the user's live session.
The installer sets up a local environment, downloads the models, and validates the Audio Unit using `auval`.
After the initial setup, DAWalka shouldn't need to reach the internet for every single sound. Here, 'local' isn't a marketing buzzword—for a musician or a team, it's about privacy, cost, reliability, and control over material, especially when it comes to unreleased ideas and personal audio files.
No monthly subscription just to run the process. Users don't pay per generation attempt in the cloud, and they're not stuck behind an external queue while working.
About 6.7 GB of models on install. Stable Audio 3 weights are downloaded once into a local cache; after that, generation runs entirely on the user's machine.
About 10 GB total for the environment and models. The project's README states disk requirements upfront so installation doesn't feel unpredictable or mysterious.
16 GB RAM as a reasonable baseline. The project is honest about the minimum comfortable Mac configuration instead of promising it'll run the same on any hardware.
The plugin itself is only about 11 MB. The heavy lifting lives in the models and environment; the Audio Unit itself stays a lightweight shell around a local backend.
Generated audio stays in your Documents folder. The `~/Documents/DAWalka/T2A` and `~/Documents/DAWalka/A2A` folders keep results separate from system files and aren't removed by the uninstaller by default.
A key part of this story is that DAWalka didn't stay a private binary for personal use. The repository is public, complete with a README, MIT License, installer, build scripts, source code, backend, and screenshots—making the project accessible to both early users and other developers.
Users know exactly what they're installing. The README covers system requirements, installation steps, file paths, models, uninstallation, and backend dependencies.
Developers can see exactly how it's built. The repo includes a C++/JUCE component, a Python backend, an installer, a launcher, and build scripts.
The MIT License lowers the barrier to entry. Anyone can study, fork, and adapt the project instead of it becoming a closed-off showcase after its first public release.
Project support is handled transparently. Instead of a hidden subscription model, there's an open request to star the GitHub repo, share the link, or support the developer via a donation link.
The Outcome: A Real Example of a Local-First Creative Tool, Not Just Another AI Service Review
This case matters beyond musicians alone—it points to a broader shift: AI features can live inside professional tools, run on the user's own hardware, respect the privacy of unfinished work, and remain open to inspection.
A years-long frustration turned into a working plugin. Four years of waiting for the right local tool ended not in a compromise with the cloud, but in building it yourself, directly inside Logic Pro.
AI audio got closer to the actual creative session. Generation stopped being a separate web ritual and became part of the music project itself: prompt, BPM, waveform, preview, drag-and-drop.
A solid public example of the local-first approach emerged. DAWalka shows how Stable Audio 3, MLX, Metal, and JUCE can come together into a tool that speaks to musicians, not just ML enthusiasts.
A new kind of case study. This isn't client automation or a marketing website—it's a product-focused open-source experiment at the intersection of music, local models, and applied engineering.
This approach matters to anyone building AI tools meant to sit alongside real work rather than replace it entirely. DAWalka is especially relevant where AI should shorten the path from idea to usable fragment inside a familiar environment—not replace the craft itself.
Musicians working in Logic Pro. When you want to quickly sketch a mood, an effect, an instrumental phrase, or a variation without leaving your project.
Producers handling private material. When you don't want to send drafts, stems, or unreleased ideas to a cloud service just to try an idea.
Teams building local-first AI products. When you need to understand how to package a model, installer, UX, and open-source structure into one working product.
The original article covers who Misha Ivanov is, why DAWalka grew out of a personal producer frustration, how T2A and A2A actually work, why locality matters for the creative process, and how to support the open-source project.
View the full article. View DAWalka on GitHub.
Alpha version: audio generated via local TTS, errors possible.
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