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deezer/spleeter

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Spleeter

Spleeter is an AI audio source separation library and deep learning toolkit designed to split mixed music files into individual audio stems, such as vocals and drums. It provides a suite of pretrained models for isolating different instruments and voices from a recording.

The toolkit includes capabilities for training and evaluating custom audio separation models using labeled datasets and configuration files. It also features utilities for measuring model performance by comparing separation outputs against reference datasets.

The system manages audio processing through spectral representations and uses a custom interface for loading and saving audio data across different storage formats. Exporting separated stems is handled via asynchronous processing.

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Features

  • Audio Stem Extractors - Splits mixed music files into individual audio tracks like vocals and drums using deep learning models.
  • Audio Source Separation Models - Splits mixed audio files into individual stems like vocals and drums using pretrained machine learning models.
  • Separation Model Training - Implements capabilities for creating custom audio separation models using labeled datasets.
  • Spectral Masking - Uses spectral masking to filter out unwanted frequencies and isolate individual audio stems from a mixture.
  • Pretrained Model Integrations - Enables the application of pretrained deep learning model weights to new audio inputs for source separation.
  • Deep Learning Audio Libraries - Functions as a deep learning library for splitting music files into individual audio stems.
  • Deep Learning Toolkits - Provides a comprehensive toolkit for training, evaluating, and exporting custom audio separation models.
  • Source Separation Training - Provides the ability to train custom audio separation models using labeled datasets and configuration files.
  • U-Net Architectures - Employs a U-Net encoder-decoder architecture to learn spectral patterns for isolating audio sources.
  • FFT Spectral Processing - Converts audio waveforms into time-frequency representations using Short-Time Fourier Transforms for processing and reconstruction.
  • Model Performance Evaluators - Quantifies the accuracy and quality of source separation models by comparing outputs against reference datasets.
  • Training Configurations - Provides external configuration files to manage hyperparameters and dataset paths for automated model training.
  • AI and Machine Learning - Audio source separation library.
  • Natural Language Processing - Source separation library for audio.
  • Audio Processing - Source separation library for isolating audio tracks.
28,252 stars·3,064 forks·Python·MIT·31 views

Star history

Star history chart for deezer/spleeterStar history chart for deezer/spleeter

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does deezer/spleeter do?

Spleeter is an AI audio source separation library and deep learning toolkit designed to split mixed music files into individual audio stems, such as vocals and drums. It provides a suite of pretrained models for isolating different instruments and voices from a recording.

What are the main features of deezer/spleeter?

The main features of deezer/spleeter are: Audio Stem Extractors, Audio Source Separation Models, Separation Model Training, Spectral Masking, Pretrained Model Integrations, Deep Learning Audio Libraries, Deep Learning Toolkits, Source Separation Training.

Which projects share features with deezer/spleeter?

Projects with overlapping indexed features include: speechbrain/speechbrain — SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular… espnet/espnet — ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech… bytedance/music_source_separation — This project is a deep learning toolkit designed for audio source separation and music information retrieval. It… facebookresearch/demucs — Demucs is a deep learning stem splitter and AI music de-mixing software used to isolate vocals and instruments from a… allenai/allennlp — AllenNLP is a PyTorch-based research library and deep learning language toolkit designed for developing and training… fastai/course22 — This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It…

Projects sharing features with Spleeter

These projects share indexed features with Spleeter. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • speechbrain/speechbrainspeechbrain avatar

    speechbrain/speechbrain

    11,624View on GitHub↗

    SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular library, it provides a structured environment for developing, training, and deploying neural network models across a wide range of tasks, including automatic speech recognition, speaker identification, and audio enhancement. The framework distinguishes itself through a configuration-driven approach that separates model architecture and training hyperparameters from application logic. By utilizing externalized configuration files and standardized recipes, it enables reproducible rese

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  • espnet/espnetespnet avatar

    espnet/espnet

    9,861View on GitHub↗

    ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It

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    View on GitHub↗9,861
  • bytedance/music_source_separationbytedance avatar

    bytedance/music_source_separation

    1,385View on GitHub↗

    This project is a deep learning toolkit designed for audio source separation and music information retrieval. It provides a framework for decomposing polyphonic audio signals into distinct components, such as vocals, drums, and bass, by processing raw waveforms through neural network architectures. The library enables users to train custom separation models or fine-tune existing ones to improve accuracy on specific audio datasets. It supports the entire model lifecycle, including the conversion of raw audio into structured, indexed formats to optimize data loading and training efficiency. Th

    Pythonresearch
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  • facebookresearch/demucsfacebookresearch avatar

    facebookresearch/demucs

    10,236View on GitHub↗

    Demucs is a deep learning stem splitter and AI music de-mixing software used to isolate vocals and instruments from a single audio file. It functions as a PyTorch audio source separation tool that splits mixed tracks into individual stems such as drums, bass, and vocals. The system is a hybrid spectrogram waveform separator that combines spectral and waveform analysis. This approach allows the software to process audio in both frequency and time domains to achieve high-fidelity source separation. The tool provides capabilities for audio source separation, including acapella track extraction

    Python
    View on GitHub↗10,236
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