Spleeter vs StemRoller

Struggling to choose between Spleeter and StemRoller? Both products offer unique advantages, making it a tough decision.

Spleeter is a Audio & Music solution with tags like audio-separation, remixing, music-manipulation, deep-learning.

It boasts features such as Uses deep learning models for audio source separation, Separates audio into stems of vocals, drums, bass, piano and other instruments, Provides pre-trained models for 2, 4 and 5 stem separation, Command line interface and Python library for integration into apps, Open source under MIT license and pros including High quality separation powered by deep learning, Pre-trained models require no setup or training, Modular design allows customizing for new separation tasks, Actively maintained by research team at Deezer.

On the other hand, StemRoller is a Audio & Music product tagged with stem-extraction, vocal-isolation, instrumental-isolation, ai, deep-learning.

Its standout features include Isolates and extracts vocal and instrumental stems from stereo audio files, Uses advanced AI and deep learning for audio source separation, Allows editing and processing of isolated stems, Supports common audio formats like MP3, WAV, FLAC, etc., Available as VST/AU plugin or standalone application, and it shines with pros like Powerful stem separation and extraction, Intuitive and easy to use interface, Saves time compared to manual stem creation, Great for remixing, sampling, karaoke tracks, Impressive quality and accuracy.

Feature	Spleeter	StemRoller
Overall Score	N/A	N/A
Primary Category	Audio & Music	Audio & Music

Product Overview

Spleeter

Description: Spleeter is an open-source audio source separation tool intended for music manipulation. It separates audio recordings into stems of vocals, drums, bass, and other instruments for remixing or analysis. It utilizes deep learning for high quality source separation.

StemRoller

Description: StemRoller is an audio editing tool that allows for the isolation and extraction of vocal and instrumental stems from stereo audio files. It utilizes advanced artificial intelligence and deep learning techniques to break down tracks into separate vocal and instrumental components.

Key Features Comparison

Spleeter Features

• Uses deep learning models for audio source separation
• Separates audio into stems of vocals, drums, bass, piano and other instruments
• Provides pre-trained models for 2, 4 and 5 stem separation
• Command line interface and Python library for integration into apps
• Open source under MIT license

StemRoller Features

• Isolates and extracts vocal and instrumental stems from stereo audio files
• Uses advanced AI and deep learning for audio source separation
• Allows editing and processing of isolated stems
• Supports common audio formats like MP3, WAV, FLAC, etc.
• Available as VST/AU plugin or standalone application

Pros & Cons Analysis

Spleeter

Pros

• High quality separation powered by deep learning
• Pre-trained models require no setup or training
• Modular design allows customizing for new separation tasks
• Actively maintained by research team at Deezer

Cons

• Pre-trained models work best on pop/rock music
• Requires powerful GPU for real-time separation
• Limited documentation and examples

StemRoller

Pros

• Powerful stem separation and extraction
• Intuitive and easy to use interface
• Saves time compared to manual stem creation
• Great for remixing, sampling, karaoke tracks
• Impressive quality and accuracy

Cons

• Requires powerful computer for real-time processing
• Limited to stereo audio input
• Not free - subscription or one-time purchase required
• Some artifacts may be present in separated stems

Pricing Comparison

Spleeter

• Open Source

StemRoller

• Subscription
• One-time Purchase