Speech De-Noise, Vocal Enhancement
v1.3.1Speech enhancement / vocal denoising on remote (FREE) L4 GPU. Trigger when user says: denoise, remove noise, clean up audio, 去噪, 降噪, enhance audio. Takes loc...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (speech denoise on a remote L4 GPU) matches the code and SKILL.md: it uses Modal, mounts volumes, runs ffmpeg conversions and a ClearerVoice model on GPU. No unrelated credentials or services are requested.
Instruction Scope
Instructions are focused on finding audio/video files, uploading them to Modal volumes, running the denoise job, and downloading results. Caution: the workflow scans the current directory for matching extensions and uploads matched files — running it from a wide or sensitive directory could accidentally upload unintended files.
Install Mechanism
This is instruction-plus-code (no external arbitrary URL downloads). The image build (src/images.py) installs ffmpeg (apt) and Python packages (clearvoice, torch, torchaudio) in the container via standard package managers (apt/pip). This is expected for the task; no unusual remote installers or URL-shortened downloads are used.
Credentials
The skill declares no required environment variables or external credentials. It does rely on the user's Modal authentication (modal CLI) to upload to Modal volumes, which is expected. The image sets only harmless container env vars (TQDM_DISABLE, HF_HUB_DISABLE_PROGRESS).
Persistence & Privilege
always is false; the skill does not request permanent injection or modify other skills. It creates/uses Modal volumes and an image scoped to its app, which is appropriate for a remote processing pipeline.
Assessment
This skill appears to do exactly what it claims: convert and denoise local audio using a ClearerVoice model on a remote Modal GPU. Before using it: (1) ensure you run it from a directory that only contains the audio/video files you intend to upload (it will scan and upload matched extensions), (2) be aware you must authenticate with Modal (modal setup) so the tool can create/use Modal volumes — keep your Modal token private, (3) try with non-sensitive sample audio first, and (4) if you have policy or data-residency concerns, confirm that uploading files to Modal volumes and remote GPU inference is acceptable. If you'd like higher assurance, inspect or run the denoise.py logic locally on a small file to confirm behavior (it does symlink a checkpoints path inside the container and installs packages in the container only).Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.