Matchering

Overview

Supply a TARGET and a REFERENCE, and Matchering produces a mastered target aimed at matching the reference’s RMS, frequency response, peak amplitude and stereo width. That makes it most useful when a known recording is the desired benchmark; it offers less direction when you want to define a sound without a reference.

Matchering 2.0 comes as a containerized web application, Python library and ComfyUI node, and it is integrated into the UVR5 Desktop App. Songmastr, MVSEP and Moises host it for users who want to try it without installing software. The project describes its method as digital signal processing rather than neural-network processing, and says users may use their mastered tracks wherever they want.

Key features

Reference matching is the defining feature. Matching loudness, tonal response, peak level and stereo width to another track can help pursue a consistent sound, but the outcome depends on choosing an appropriate reference. The algorithm is described as working well with almost all genres, especially EDM, while experimental music with a particularly specific musical form is a weaker fit.

The Python library can connect to the broader Python ecosystem and includes a command-line application, making it the natural choice for developers automating or incorporating the process into other software. It requires Python 3.8.0 or higher and a machine with 4 GB of RAM. WAV and MP3 are supported, though MP3 loading requires FFmpeg installed separately.

ComfyUI and UVR5 integrations provide other ways to use Matchering, while the hosted options remove the need to install it locally. Those forms make the project accessible beyond Python, though the available facts do not establish that every hosted integration has the same workflow or terms.

Pricing

Matchering’s Open-source software plan costs 0.00 USD per free under the GNU General Public License v3 (GPLv3). It includes reference-track matching and WAV export, with WAV and MP3 input formats; there are no published paid plans. That makes it a clear fit for users comfortable installing or integrating open-source software, rather than buyers seeking a paid tier with defined support or service levels.

The Python package’s GPLv3 license matters if you plan to distribute software that incorporates it. The project says users may use tracks produced by Matchering wherever they want. No subscription or renewal cost is attached to the free plan.

Platforms

Matchering is available for Linux, macOS and Windows, as well as through web and self-hosted forms. The Python library needs Python 3.8.0 or higher and 4 GB of RAM; MP3 input requires a separate FFmpeg installation. These requirements are manageable for a developer or technically comfortable user, but add setup work for someone expecting a conventional desktop installer.

The web application is designed for home and in-house use, not public Internet hosting without security and scalability changes. Its current setup puts Django, SQLite, Redis and the Matchering worker in one container; the maker cites non-scalable SQLite, DEBUG enabled and the absence of a production web server among the reasons not to expose it publicly. This is a significant constraint for anyone considering a public service.

Who it's for

Matchering makes sense for musicians who have a suitable reference and want to bring a target track toward its level, tonal response and stereo presentation. It is particularly promising for EDM according to the project’s genre guidance. It is a less convincing choice for experimental music with a highly specific form, or for anyone who needs mastering decisions made without a reference.

Developers may value the Python library and command-line application, while ComfyUI users can choose the node and desktop users can access the UVR5 integration. If installation is a barrier, Songmastr, MVSEP and Moises provide hosted ways to try the project. Those seeking to deploy the web app publicly should look elsewhere unless they are prepared to make security and scaling changes.

Pros and cons

• Pros: Free, open-source access includes reference matching and WAV export, with no published paid plans.
• Pros: Multiple forms—Python library, command-line application, containerized web app and ComfyUI node—suit different technical workflows.
• Pros: The focused matching targets RMS, frequency response, peak amplitude and stereo width, and the project says output tracks may be used wherever users want.
• Cons: Results are guided by a reference, so it is not the right tool for open-ended mastering without a target sound in mind.
• Cons: Python use requires a compatible runtime and 4 GB of RAM, while MP3 loading needs separately installed FFmpeg.
• Cons: The web application should not be exposed publicly as-is because of its stated security and scalability shortcomings.

Alternatives

For a broader starting point, browse Audio Mastering Software or AI Music Mastering Software.

• IK Multimedia ReSing is worth considering if you want voice and instrument tools: its free plan includes 2 voices, 2 instruments and 1 RVC import, while its 129.99 USD one-time plan includes 10 voices.
• DSP-Quattro is a macOS alternative with a free trial; its new-license plan is 99.00 USD per month and its upgrade plan is 49.00 USD per month.
• Tunr is another free option for macOS and Windows, with a free key by email counted per machine and a three-master limit before the licence key.
• Voxengo SPAN may suit readers looking for a free real-time FFT spectrum analyzer plugin rather than reference-based mastering.
• oXygen Mastering Suite is a free alternative for Linux, macOS and Windows.
• StudioZIO Mastering Suite is a free macOS option for readers seeking AU, VST3, AAX or standalone formats; it requires macOS 11 or later and no licence key or account.
• Acustica Audio Erin Studio is a paid option for Windows and macOS.
• Sonoris DDP Creator is a paid alternative with a free trial and Standard and Pro licenses priced at 249.00 EUR and 349.00 EUR per one-time purchase, respectively, before VAT.

Verdict

Choose Matchering if you want free, reference-based mastering, especially for EDM, or need an open-source audio-matching component for a Python workflow. Its multiple software forms and clear output targets are its strongest reasons to choose it. Look elsewhere if you need an open-ended mastering tool, work mainly with highly experimental forms, or intend to host the web application publicly without undertaking security and scalability changes.