Project Icon

matchering

智能音频匹配与母带处理工具

Matchering 2.0是开源音频处理工具,提供容器化Web应用和Python库。基于目标音轨和参考音轨比对,自动调整RMS、频率响应、峰值振幅和立体声宽度,实现专业级母带处理。支持多种音频格式,可用于风格模仿、专辑统一和音频实验,为音乐制作者提供灵活高效的解决方案。

Buy Me A Coffee

Matchering 2.0

License PyPI Version PyPI Python Versions Mentioned in Awesome Python Code style: black

Matching + Mastering = ❤️

Matchering 2.0 is a novel Containerized Web Application and Python Library for audio matching and mastering.

It follows a simple idea - you take TWO audio files and feed them into Matchering:

  • TARGET (the track you want to master, you want it to sound like the reference)
  • REFERENCE (another track, like some kind of "wet" popular song, you want your target to sound like it)

Our algorithm matches both of these tracks and provides you the mastered TARGET track with the same RMS, FR, peak amplitude and stereo width as the REFERENCE track has.

You can try out Matchering yourself without having to install it, thanks to the hosting provided by Songmastr and Moises.

Watch the video:

Matchering 2.0 Promo Video

So Matchering 2.0 will make your song sound the way you want! It opens up a wide range of opportunities:

  • You can make your music instantly sound like your favorite artist's music
  • You can make all the tracks on your new album sound the same very quickly
  • You can find new aspects of your sound in experiments
  • You can do everything as you want! Because of Your References, Your Rules.™ (just a little nostalgic note) 🤭

Matchering WEB GIF Animation

Differences from the previous major version:

  • Completely rewritten in Python 3, based on open source tech stack (no more MATLAB)
  • Our own open source brickwall limiter was implemented for it
  • Processing speed and accuracy have been increased
  • Now it is the library that can be connected to everything in the Python world

If you are looking for a Matchering paper, you can read this Habr article.

Installation and Usage

If you are a music producer or an audio engineer, choose the Docker Image.

If you are a developer, choose the Python Library.

Docker Image - The Easiest Way

Matchering 2.0 works on all major platforms using Docker.

Choose yours

NEW! Try WITHOUT Installation

Windows

macOS

Linux

NEW! ComfyUI

Updating

If you need to update the version of the installed Docker Image, follow these instructions.

Python Library - For Developers

Installation

4 GB RAM machine with Python 3.8.0 or higher is required

libsndfile

Matchering 2.0 depends on the SoundFile library, which depends on the system library libsndfile. On Windows and macOS, it installs automatically. On Linux, you need to install libsndfile using your distribution's package manager, for example:

sudo apt update && sudo apt -y install libsndfile1

python3-pip

On some Linux distributions, python3-pip is not installed by default. For example use this command on Ubuntu Linux to fix this:

sudo apt -y install python3-pip

Matchering Python Package

Finally, install our matchering package:

# Linux / macOS
python3 -m pip install -U matchering

# Windows
python -m pip install -U matchering

(Optional) FFmpeg

If you would like to enable MP3 loading support, you need to install the FFmpeg library. For example use this command on Ubuntu Linux:

sudo apt -y install ffmpeg

Or follow these instructions: Windows, macOS.

Quick Example

import matchering as mg

# Sending all log messages to the default print function
# Just delete the following line to work silently
mg.log(print)

mg.process(
    # The track you want to master
    target="my_song.wav",
    # Some "wet" reference track
    reference="some_popular_song.wav",
    # Where and how to save your results
    results=[
        mg.pcm16("my_song_master_16bit.wav"),
        mg.pcm24("my_song_master_24bit.wav"),
    ],
)

You can find more examples in the examples directory.

Or you can use premade Matchering 2.0 Command Line Application: matchering-cli.

💓 WhatBPM

Looking for the perfect BPM or key for a new EDM track?

Check out WhatBPM!

A completely free open-source web service from the author of Matchering.

A Coffee

If our package saved your time or money, you may:

Buy Me A Coffee

Thank you!

Links

项目侧边栏1项目侧边栏2
推荐项目
Project Cover

豆包MarsCode

豆包 MarsCode 是一款革命性的编程助手,通过AI技术提供代码补全、单测生成、代码解释和智能问答等功能,支持100+编程语言,与主流编辑器无缝集成,显著提升开发效率和代码质量。

Project Cover

AI写歌

Suno AI是一个革命性的AI音乐创作平台,能在短短30秒内帮助用户创作出一首完整的歌曲。无论是寻找创作灵感还是需要快速制作音乐,Suno AI都是音乐爱好者和专业人士的理想选择。

Project Cover

有言AI

有言平台提供一站式AIGC视频创作解决方案,通过智能技术简化视频制作流程。无论是企业宣传还是个人分享,有言都能帮助用户快速、轻松地制作出专业级别的视频内容。

Project Cover

Kimi

Kimi AI助手提供多语言对话支持,能够阅读和理解用户上传的文件内容,解析网页信息,并结合搜索结果为用户提供详尽的答案。无论是日常咨询还是专业问题,Kimi都能以友好、专业的方式提供帮助。

Project Cover

阿里绘蛙

绘蛙是阿里巴巴集团推出的革命性AI电商营销平台。利用尖端人工智能技术,为商家提供一键生成商品图和营销文案的服务,显著提升内容创作效率和营销效果。适用于淘宝、天猫等电商平台,让商品第一时间被种草。

Project Cover

吐司

探索Tensor.Art平台的独特AI模型,免费访问各种图像生成与AI训练工具,从Stable Diffusion等基础模型开始,轻松实现创新图像生成。体验前沿的AI技术,推动个人和企业的创新发展。

Project Cover

SubCat字幕猫

SubCat字幕猫APP是一款创新的视频播放器,它将改变您观看视频的方式!SubCat结合了先进的人工智能技术,为您提供即时视频字幕翻译,无论是本地视频还是网络流媒体,让您轻松享受各种语言的内容。

Project Cover

美间AI

美间AI创意设计平台,利用前沿AI技术,为设计师和营销人员提供一站式设计解决方案。从智能海报到3D效果图,再到文案生成,美间让创意设计更简单、更高效。

Project Cover

AIWritePaper论文写作

AIWritePaper论文写作是一站式AI论文写作辅助工具,简化了选题、文献检索至论文撰写的整个过程。通过简单设定,平台可快速生成高质量论文大纲和全文,配合图表、参考文献等一应俱全,同时提供开题报告和答辩PPT等增值服务,保障数据安全,有效提升写作效率和论文质量。

投诉举报邮箱: service@vectorlightyear.com
@2024 懂AI·鲁ICP备2024100362号-6·鲁公网安备37021002001498号