| MISST on Windows 11 with Dark mode and 'Blue' theme with 'Kanye West's All of The Lights' playing

| MISST on Windows 11 with Light mode and 'Blue' theme with 'Beabadoobee's Cologne' playing

| MISST on Windows 11 Showcasing how versatile and personal you can be with MISST!

| MISST on Windows 11 Showcasing how importing audios is as easy as two clicks!

Original Repository of MISST : Music/Instrumental Stem Separation Tool.

This application uses state-of-the-art source separation models to extract the 4 core stems from audio files (Bass, Drums, Other Instrumentals and Vocals). But it is not limited to this. MISST acts as a developped music player aswell, fit to enjoy and medal with your audio files as you see fit. MISST even comes prepared to import songs and playlists directly from your music library.

This project is OpenSource, feel free to use, study and/or send pull request.

Objectives:

• Import songs and playlists from your music library
• Play your songs and playlists
• Extract and manipulate the 4 core stems from your audio files as they play
• Save your stems as audio files
• If imported from your music library, view lyrics and metadata just as you would in your old music player
• Minimal memory usage
• Customizable themes
• Additional Efects like nightcore
• Easy to use equalizer
• Preprocessing service available on both CPU and GPU
• Docker image (WIP)
• Make it as fast as possible (Preprocessing, Model loading, etc.) (Not a priority)
• Stable on Windows, Linux and MacOS (WIP)
• Reasonable download size
• Proper installer/updater (Not a priority)

Installation

As of version 3.0.3, MISST is only available on windows with guaranteed compatibility. Until a later release, if you are not on a windows device please refer to Manual Installation. Otherwise, refer to the latest Release

Manual Installation

These instructions are for those installing MISST v3.0.2 manually only.

1. Download & install Python 3.9 or higher (but no lower than 3.9) here
   • Note: Ensure the "Add Python to PATH" box is checked
2. Download the Source code here
3. Open the command prompt from the MISST directory and run the following commands, separately -

$ pip install -r requirements.txt
$ pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

• Note: The last command is only necessary if you intend to accelerate preprocessing with your GPU.

From here you should be able to open and run the MISSTapp.py file

• CUDA

  • CUDA must be installed and configured for the application to process any track with GPU acceleration. You will need to look up instruction on how to configure it on your operating system. Click here for nvidia's installation guide.

Small Output Size

By employing advanced compression techniques, MISST optimizes the output files to minimize their size without compromising audio quality. This is achieved through a two-step process:

• Audio Compression: MISST employs the FLAC (Free Lossless Audio Codec) format to compress the separated audio stems. FLAC offers a high level of compression while maintaining the original audio fidelity. As a result, the size of each stem is significantly reduced compared to other common audio formats.

• Metadata Compression: In addition to compressing the audio, MISST also optimizes the metadata associated with the stems. It encodes the metadata using Base64 encoding, which allows for efficient representation of the information while keeping the file size to a minimum.

As a result of these compression techniques, the output file size of MISST is remarkably small. In fact, even a four-stem output from MISST can be almost the same size as your original one-stem input. This makes MISST an ideal choice for scenarios where storage space or bandwidth limitations are a concern.

License

The MISST code is GPL-licensed.

• Please Note: For all third-party application developers who wish to use MISST or its code, please honor the GPL license by providing credit to MISST and its developer.

Issue Reporting

Please be as detailed as possible when posting a new issue.

If possible, check the "MISST.log" file in your install directory for detailed error information that can be provided to me.

Contributing

• For anyone interested in the ongoing development of MISST, please send us a pull request, and I will review it.
• This project is 100% open-source and free for anyone to use and modify as they wish.
• I only maintain the development for MISST and the models provided.

More documentation to come...