Música com IA
instruct-MusicGen
Research code for instruction-based text-to-music editing of music models.
★ 111 estrelasApache-2.0
Ver o guia →HS-TasNet é um projeto de música com IA de código aberto de lucidrains (lucidrains/HS-TasNet no GitHub). Tem 112 estrelas no GitHub e 15 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 25 de agosto de 2026.
Real-time, low-latency music source separation in PyTorch.
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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A resource-efficient music source separation model in PyTorch.
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Research code for instruction-based text-to-music editing of music models.
Automatic AI video editing.
Agent skill that produces beat-synced 1080p motion-graphic videos in HyperFrames.
ComfyUI nodes for multilingual AI songs with lyrics via HeartMuLa.
One-click AIGC pipeline: image, video, music, and prompts in a single flow.
Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.