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Matchering

github.com

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Spec score 8.7 #1 of 33 in Audio Mastering Software

What it is

Matchering is open-source software for matching and mastering audio. Give it a TARGET track and a REFERENCE track, and it produces a mastered TARGET that follows the reference’s RMS, frequency response, peak amplitude, and stereo width. Matchering 2.0 comes as a containerized web application, Python library, and ComfyUI node, and is integrated into the UVR5 Desktop App. The Python library also provides a command-line application and can connect with the Python ecosystem. It requires Python 3.8.0 or later and a machine with 4 GB of RAM. WAV and MP3 are supported input formats, though MP3 loading needs a separate FFmpeg installation. The project says the algorithm uses digital signal processing rather than a neural network, and works well across nearly all genres, especially EDM, with an exception for experimental music with a very specific form. The free plan is listed at free under GPLv3, with no published paid plans. The maker advises against exposing its web application to the public Internet without security and scalability changes.

Spec sheet

What it does
Matchering processes a target recording against a reference recording and returns a master intended to match the reference’s loudness, frequency response, peak amplitude and stereo width.Read 21 Oct 2026 · github.com
License
Matchering is distributed under the GNU GPL version 3.0 license.Read 21 Oct 2026 · github.com
Album consistency
The documentation presents using one reference across multiple tracks as a way to make an album sound more cohesive.Read 21 Oct 2026 · saxtalk.com
Audio formats
An independent software directory describes WAV and FLAC input/output support and professional WAV output.Read 21 Oct 2026 · toolstock.net
Audio-format limit
MP3 loading requires installing FFmpeg separately.Read 1 Oct 2026 · github.com
ComfyUI integration
A ComfyUI-Matchering node is offered as an integration route.Read 21 Oct 2026 · github.com
Command line
The repository points developers to a separate Matchering CLI and an enhanced fork of that CLI.Read 21 Oct 2026 · github.com
Desktop integration
Matchering is integrated into the UVR5 Desktop App.Read 1 Oct 2026 · github.com
Developer integration
The Python library can be connected to everything in the Python ecosystem and has a command-line application.Read 1 Oct 2026 · github.com
Docker deployment
The repository provides separate Docker instructions for Windows, macOS and Linux, plus an update guide.Read 21 Oct 2026 · github.com
Documentation
The repository includes a README, FAQ wiki, Docker guides, examples, log-code documentation and limiter testing material.Read 21 Oct 2026 · github.com
Genre guidance
The algorithm is described as working well with almost all genres, especially EDM, except experimental music with a very specific musical form.Read 1 Oct 2026 · github.com
Hosted access
The project README identifies Songmastr, MVSEP and Moises as services that host Matchering for browser-based trials.Read 21 Oct 2026 · github.com
Hosted integrations
The project says users can try it without installation through Songmastr, MVSEP and Moises hosting.Read 1 Oct 2026 · github.com
Independent assessment
Hysen Labs describes the project as matching four measurements with a project-specific limiter and notes that the newest tagged release is from 2022.Read 21 Oct 2026 · hysenlabs.com
Limit: interface and controls
Beathoven characterizes Matchering as having a less polished interface and fewer adjustable parameters than commercial services such as LANDR or eMastered.Read 21 Oct 2026 · beathoven.tv
Limit: reference dependence
Beathoven warns that good results depend on choosing a suitable reference track.Read 21 Oct 2026 · beathoven.tv
Limit: service outage
A Songmastr user reported that the hosted site had gone down, while another later comment said it was back online.Read 21 Oct 2026 · reddit.com
Limit: setup knowledge
ToolStock says self-hosting requires basic technical knowledge for installation.Read 21 Oct 2026 · toolstock.net
Limit: upload errors
Reddit commenters reported browser bad-request errors and later said they could not upload songs to the hosted service.Read 21 Oct 2026 · reddit.com
Limiter
Version 2 introduced the project’s own open-source Hyrax brickwall limiter.Read 21 Oct 2026 · github.com
Linux dependency
Linux installations require the system libsndfile library to be installed separately.Read 21 Oct 2026 · github.com
Maker identity
The package credits Sergree as author and lists one maintainer on PyPI.Read 21 Oct 2026 · pypi.org
Maker location
The maintainer’s GitHub profile identifies Sergey Grishakov and lists Moscow, Russian Federation.Read 21 Oct 2026 · github.com
Matching method
It takes a TARGET track and a REFERENCE track and produces a mastered TARGET matching the reference’s RMS, frequency response, peak amplitude and stereo width.Read 1 Oct 2026 · github.com
MP3 support
FFmpeg is optional and is needed when MP3 loading is desired.Read 21 Oct 2026 · github.com
Neural-network status
The FAQ says Matchering does not use a neural network and instead uses digital signal processing.Read 1 Oct 2026 · github.com
Package version
PyPI lists Matchering 2.0.6 as the latest package release, uploaded on October 19, 2022.Read 21 Oct 2026 · pypi.org
Praise: clarity and separation
Users in a Spanish music forum report added clarity, more separation between instruments and normalization without an obviously compressed sound.Read 21 Oct 2026 · guitarristas.info
Praise: easy matching
A Spanish forum user says the tool can clarify and open up dull-sounding songs while raising their levels.Read 21 Oct 2026 · guitarristas.info
Praise: free access
SaxTalk praises the software for enabling local mastering without paid proprietary services or expensive equipment.Read 21 Oct 2026 · saxtalk.com
Praise: reference control
Beathoven describes the reference-based approach as useful for artists who have a precise sonic target and limited budgets.Read 21 Oct 2026 · beathoven.tv
Product forms
The project is provided as a containerized web application, Python library and ComfyUI node.Read 21 Oct 2026 · github.com
Public-hosting risks
The privacy page lists Django, SQLite, Redis and the Matchering worker in one container, non-scalable SQLite, DEBUG enabled and no production web server as reasons not to expose it publicly.Read 1 Oct 2026 · github.com
Python integration
The project says its Python library can be connected to other software in the Python ecosystem.Read 21 Oct 2026 · github.com
Python requirement
The current packaging metadata requires Python 3.8 or newer.Read 21 Oct 2026 · github.com
Reference workflow
Users supply two audio files: the target they want mastered and a reference whose sonic character they want to follow.Read 21 Oct 2026 · github.com
Release change
Release 2.0.6 says it fixed CVE-2021-34141 and updated dependencies for stability and speed.Read 21 Oct 2026 · github.com
Runtime requirement
The Python library requires a machine with 4 GB of RAM and Python 3.8.0 or higher.Read 1 Oct 2026 · github.com
Security warning
The maker says the web application is designed for home and in-house use and is not suitable for public Internet hosting without security and scalability changes.Read 1 Oct 2026 · github.com
Self-hosted web UI
An independent tutorial describes cloning the repository, starting Docker Compose and opening a local browser interface on port 8000.Read 21 Oct 2026 · saxtalk.com
Software forms
Matchering 2.0 is provided as a containerized web application, Python library and ComfyUI node.Read 1 Oct 2026 · github.com
Supported operating systems
The package is classified as operating-system independent, while the Docker documentation covers Windows, macOS and Linux.Read 21 Oct 2026 · pypi.org
System memory
The Python library documentation requires a machine with at least 4 GB of RAM.Read 21 Oct 2026 · github.com
Usage rights
The FAQ says users may use their Matchered tracks wherever they want.Read 1 Oct 2026 · github.com
UVR5 integration
Matchering is available inside the UVR5 desktop application under its audio-tools menus.Read 21 Oct 2026 · github.com

Plans and prices

Open-source software Free GPLv3 license · no published paid plans Read 1 Oct 2026 · github.com

Every Matchering plan and price →

Strong points and limits

Strong points

  • Matches RMS, frequency response, peak, and stereo width.
  • Available as a web app, Python library, and ComfyUI node.
  • Integrated into the UVR5 Desktop App.
  • Free under GPLv3, with no published paid plans.

Limits

  • MP3 loading requires a separate FFmpeg installation.
  • Python library requires 4 GB RAM and Python 3.8.0+.
  • Web app is not intended for public Internet hosting as-is.

Who it suits

Matchering suits musicians and audio makers who want a reference-based mastering workflow, especially those working in EDM. Developers can use its Python library or command-line application, while the web application is intended for home or in-house use.

Matchering in depth

Read the full write-up

Matchering is reference-led audio mastering software for musicians, producers and developers who want a target track shaped toward a chosen recording. Its focused DSP approach and multiple deployment options make it a practical free choice; users who need public web hosting or a less reference-dependent mastering workflow should look elsewhere.

Overview

Give Matchering a target and a reference, and it produces a mastered target aimed at the reference’s RMS, frequency response, peak amplitude and stereo width. That makes it useful when a particular recording provides a meaningful benchmark, but less suitable when you want to build a sound without a reference or need a broad mastering environment.

Matchering 2.0 is available as a containerized web application, a Python library and a 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 it. The project says the algorithm uses digital signal processing rather than a neural network, and users may use their mastered tracks wherever they want.

Key features

Reference matching is the central capability: the algorithm targets four measurable aspects of the reference rather than offering an open-ended set of controls. This can help producers pursue a consistent level and tonal profile across tracks, but results depend on choosing a reference that suits the material. The algorithm is described as working well with almost all genres, especially EDM; experimental music with a very specific musical form is the stated exception.

WAV and MP3 are supported input formats, and WAV export is available. MP3 loading has an extra setup requirement: FFmpeg must be installed separately. That is a modest hurdle for a local workflow, but worth accounting for in automated or shared deployments.

Developers can connect the Python library to the broader Python ecosystem or use its command-line application. The library requires Python 3.8.0 or higher and a machine with 4 GB of RAM, so it is a reasonable fit for compatible systems but not a zero-dependency option.

Pricing

Matchering’s Open-source software plan costs nothing under the GNU General Public License v3 (GPLv3). It includes the open-source software, and no paid plans are offered. That makes it a strong fit for users comfortable running or integrating the software themselves; there is no paid tier to trade setup work for.

The project also says users can try Matchering through Songmastr, MVSEP and Moises without installing it. The web application itself is designed for home and in-house use, not public Internet hosting as-is. Anyone considering a public service should account for the security and scalability work the maker says is necessary.

Platforms

Matchering supports Linux, macOS and Windows, as well as web and self-hosted use. The Python library, containerized web application and ComfyUI node provide distinct routes to use it; the choice depends on whether the priority is integration, a web interface or a node-based workflow. MP3 input requires separately installed FFmpeg.

Who it's for

Matchering suits musicians and producers with a reference track in mind, particularly those working in genres the algorithm is said to handle well, such as EDM. It also suits developers who want reference-matching processing in a Python workflow, provided their machine meets the library’s runtime requirements.

It is a poor fit for operators who plan to expose the supplied web application publicly without making changes. The maker warns that its current design is for home and in-house use: Django, SQLite, Redis and the Matchering worker run in one container, SQLite is non-scalable, DEBUG is enabled, and there is no production web server. Those conditions call for security and scalability changes before public exposure.

Pros and cons

Pro: Matching RMS, frequency response, peak amplitude and stereo width gives users a clear reference-based mastering target.

Pro: The Python library, command-line application, containerized web application, ComfyUI node and UVR5 integration accommodate several technical workflows.

Pro: The free GPLv3 offering and permission to use mastered tracks wherever users want make it an accessible option for personal and commercial output.

Con: The result depends on the choice of reference, and the project cautions against experimental music with a very specific musical form.

Con: MP3 loading requires a separate FFmpeg installation, adding a dependency to workflows that need MP3 input.

Con: The web application is not suitable for public Internet hosting without security and scalability changes.

Alternatives

For a broader directory of options, see Audio Mastering Software and AI mastering tools.

IK Multimedia ReSing is worth considering for macOS or Windows users who want its free tier or a one-time paid plan; its free tier is limited to 2 voices, 2 instruments and 1 RVC import, with no model generation.

Sonoris DDP Creator is a paid alternative for users seeking DDP Creator Standard or Pro licenses, with a free trial and one-time prices of 249.00 EUR and 349.00 EUR respectively.

DSP-Quattro may fit macOS users comparing a free trial and paid 6.x licenses, including a $99 a month new-license plan.

FUSER is an option for users who want a paid mastering tool for macOS or Windows; it has a free trial and costs £59, paid once.

Tunr is another free macOS and Windows option, with a free key sent by email, counted per machine, and output choices including 16-bit and 24-bit.

Brainworx bx_masterdesk may suit users comparing a paid subscription for macOS or Windows, with CORE at $14.99 a month and PRO at $29.99 a month.

Voxengo SPAN is a free real-time FFT spectrum analyzer plugin for macOS and Windows rather than a reference-matching mastering workflow.

Waves Curves AQ is an alternative for users considering a broader paid plugin subscription: Waves Ultimate costs $24.99 a month or 249 per year and includes Curves AQ.

Verdict

Choose Matchering if you want free, reference-led mastering and can use its local, Python or hosted routes within their limits. Its focused matching targets and flexible integrations are compelling; the public-hosting warning, MP3 dependency and reliance on a suitable reference are the main reasons to choose another tool.

Verdict

Matchering specifies its matching targets, formats, runtime requirements, and several ways to use it. Its free GPLv3 offering is broad, but the web application’s public-hosting caveat and MP3 dependency matter when planning a setup.

Where Specifiction places it

Alternatives to MatcheringAll alternatives →