deezer/spleeter

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.

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