AI Music
instruct-MusicGen
Research code for instruction-based text-to-music editing of music models.
★ 111 starsApache-2.0
View guide →Real-time, low-latency music source separation in PyTorch.
HS-TasNet is an open-source ai music project by lucidrains (lucidrains/HS-TasNet on GitHub). It has 112 GitHub stars and 15 forks, is released under the MIT license, and was last updated on GitHub on 25 August 2026.
Get the implementation.
Set up the training environment.
Get separated-track training data.
Run separation on your mixes.
Splits mixes into stems.
Low-latency architecture.
Hybrid Spectrogram-TasNet in code.
Standard deep-learning stack.
Tuned for musical sources.
Free for research and products.
Download the source and enter the folder.
git clone https://github.com/lucidrains/HS-TasNet.git
cd HS-TasNetSet up the Python environment.
pip install -r requirements.txtFollow the README training/inference commands.
# see README for the exact training commandA PyTorch implementation of real-time music source separation, at lucidrains/HS-TasNet.
Splitting a mixed track into vocals, drums, bass, and so on.
The architecture targets low latency; actual speed depends on hardware.
Yes. MIT licensed.
Yes. Separated stems are needed to train; see the README.
Research code for instruction-based text-to-music editing of music models.
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