# asteroid-team/asteroid

The PyTorch-based audio source separation toolkit for researchers

Repository: https://github.com/asteroid-team/asteroid
Canonical: https://ross.abutalabs.com/products/asteroid
Homepage: https://asteroid-team.github.io/
Language: Python
License: MIT
License Family: permissive
Topics: source-separation, speech-separation, audio-separation, speech-enhancement, deep-learning, pytorch, pretrained-models
Last push: 2026-05-13T12:56:36+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 82, release rhythm 8, longevity 100
- inputs: {"age_days": 2511, "days_push": 112, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2584, forks 450 (observed 2026-08-28T04:07:02.558568+00:00)

## What it is
Asteroid is a PyTorch-based audio source separation toolkit for researchers, providing modular building blocks (filterbanks, encoders, maskers, decoders, losses), datasets, and recipes to reproduce papers. It also ships pretrained models for speech separation and enhancement.

## Use cases
- separate overlapping speakers in an audio recording
- enhance noisy speech with deep learning
- reproduce a source separation paper's results
- experiment with new separation architectures in PyTorch
- download pretrained speech separation models
- train a model on the WSJ0-2mix dataset

## When to choose
- you research audio or speech source separation and want modular, extensible PyTorch building blocks
- you need reproducible recipes covering data prep, training, and evaluation
- you want pretrained separation/enhancement models out of the box

## When to avoid
- you need a production-ready end-user app rather than a research toolkit
- your task is general music remixing with stems from a service rather than research experimentation
- you don't use PyTorch or Python

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, audio-processing, speech-recognition
- domain: machine-learning, deep-learning, speech-processing
- platform: python, cross-platform
- tags: pytorch, source-separation, speech-enhancement, pretrained-models, research, audio, gpu

## Member repositories
- asteroid-team/asteroid (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.558568+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:22:28.883710+00:00, confidence not recorded.
  - readme: https://github.com/asteroid-team/asteroid (fetched 2026-08-28T04:07:02.558568+00:00, sha e1371d330c0d)
  - homepage: https://asteroid-team.github.io/ (fetched 2026-08-29T10:04:56.136102+00:00, sha 467f6e46e7ff)
  - registry_pypi: https://pypi.org/pypi/asteroid/json (fetched 2026-08-29T10:04:56.138755+00:00, sha 81e18f66ec45)
- Data as of 2026-08-30T08:39:29.467469+00:00.