Audio-AGI/AudioSep

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1929 stars · 156 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AudioSep is the official implementation of the 'Separate Anything You Describe' foundation model for open-domain, language-queried audio source separation. It separates a target sound from an audio mixture given a natural language query, supporting tasks like audio event separation, instrument separation, and speech enhancement.

Use cases

• separate a specific sound from an audio mixture using a text description
• separate musical instruments from a mixed track
• enhance speech by removing background noise via a text query
• zero-shot separation of unseen audio events
• run language-queried audio source separation in a Python pipeline

When to choose

• you need text-query-driven audio source separation with zero-shot generalization
• you want a research-grade foundation model for audio event, instrument, or speech separation
• you need a Python API with pretrained checkpoints and Hugging Face/Colab integration

When to avoid

• you need production-grade, low-latency real-time audio processing
• you lack GPU resources or don't want to manage model checkpoints
• you need simple classical DSP filtering rather than learned separation

Facets

library · maturity active

audio-processing machine-learning deep-learning nlp machine-learning deep-learning artificial-intelligence python cross-platform audio-source-separation text-queried-separation foundation-model speech-enhancement zero-shot lass audio gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Audio-AGI/AudioSep")