facebookresearch/sam-audio
The repository provides code for running inference with the Meta Segment Anything Audio Model (SAM-Audio), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 8 contributors on GitHub.
Use Cases & Benefits
- Provides a foundation model for isolating specific sounds in audio using text, visual, or temporal prompts.
- Enables flexible sound separation from complex audio mixtures using natural language, video cues, or time spans for precise audio segmentation.
- Use for isolating speech or music tracks from mixed audio recordings in multimedia editing workflows.
- Use for enhancing audio-visual applications by separating sounds linked to visual objects in videos.
- Use for analyzing environmental sound events by specifying time ranges to extract target sounds from long audio streams.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Other
GitHub could not identify this license; read the LICENSE file before depending on it.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
- License not identified by GitHub; read the LICENSE file before depending on it.
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
Segment Anything Model for Audio [Blog] [Paper] [Demo]
SAM-Audio is a foundation model for isolating any sound in audio using text, visual, or temporal prompts. It can separate specific sounds from complex audio mixtures based on natural language descriptions, visual cues from video, or time spans.
SAM-Audio and the Judge model crucially rely on Perception-Encoder Audio-Visual (PE-AV), which you can read more about here
⚠️ Before using SAM Audio, please request access to the checkpoints on the SAM Audio Hugging Face repo. Once accepted, you need to be authenticated to download the checkpoints. You can do this by running the following steps (e.g.
hf auth loginafter generating an access token.)
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
332+8
Open issues
53+3
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| facebookresearch/sam-audio | 3.7K | — | 20 | — |
| Category median | 56.0K | +2.3K | 29 | — |
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