What makes Audiolla notable?

1.3K downloads. Trust evidence available

How should Audiolla be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Audiolla

Connect to a user-deployed audiolla server to perform stem separation, mastering, MIR analysis, DSP transforms, and loudness normalization on audio files. Skill: Audiolla Owner: psyb0t Summary: Connect to a user-deployed audiolla server to perform stem separation, mastering, MIR analysis, DSP transforms, and loudness normalization on audio files. Tags: latest:1.4.2 Version history: v1.4.2 | 2026-07-25T22:29:58.691Z | auto audiolla v1.4.2 - Updated documentation in SKILL.md and references/setup.md for clarity and accuracy. - Removed obsolete file: skill-card.md. - No ch

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.3k

Updated

Oct 10, 2026

Version

1.4.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.3K downloadsadoption · observed Oct 10, 2026
Latest release: 1.4.2release · observed Jul 25, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17fq93tmpky791n7516jcn08n83sfn2:audiolla

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-psyb0t-audiolla/snapshot"

Documentation

CLAWHUB

146,274 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: audiolla
description: HTTP/MCP client for a user-deployed audiolla audio-production server. Use ONLY when the user has explicitly named audiolla AND provided AUDIOLLA_URL (or has it set in the environment). Capabilities: stem separation (Demucs / MDX / BS-Roformer), mastering (matchering reference / pedalboard preset chain), MIR analysis (BPM, key, LUFS, spectral features, beat grid, onset detection, melody contour, structural segmentation via librosa), DSP transforms (gain, EQ, compand, reverb, pitch, tempo via SoX), loudness measurement and normalization, generic effects chains (full pedalboard catalog as ordered chain), multiband compression (LR4 crossovers), transient shaping, sidechain ducking, de-essing, mid/side encode-decode, parametric EQ, panning, stereo width, silence detection and trimming, audio repair (declip + dehum), clip detection, harmonic/percussive separation, time-stretch and pitch-shift, BPM/key matching, pitch correction (auto-tune), beat slicing, audio thumbnail extraction, convolution reverb, static PNG spectrogram/waveform and 8-mode animated MP4/WebM video (ffmpeg), Chromaprint acoustic fingerprinting, AudioSet tagging, CLAP audio embeddings + similarity + zero-shot classification, ID3/Vorbis/FLAC metadata read/write, MIDI composition from JSON spec, MIDI inspection, MIDI transformation (transpose/quantize/tempo/channel-filter), MIDI quantize and humanize, drum pattern generation, MIDI rendering via fluidsynth, polyphonic audio-to-MIDI transcription (Spotify basic-pitch ONNX), chords-to-MIDI conversion, AI audio restoration (de-reverb, de-echo, AI de-noise via UVR/audio-separator), DSP noise reduction, neural speech/vocal enhancement (DeepFilterNet DF3), voice activity detection (silero-vad), speaker diarization (pyannote 3.1), DJ prep (BPM + key + Camelot + LUFS in one call), loop-point detection, curated server-side workflow presets (master-for-spotify, podcast-cleanup, vocal-cleanup) and ad-hoc op pipelines that chain multiple operations server-side. v1.0.0 API is JSON-everywhere: every audio endpoint takes a JSON body; the ONLY multipart route is `PUT /v1/files/{path}` for raw byte uploads. Audio I/O supports two input modes (`file_path` referencing a pre-staged file under FILES_DIR, xor `file_url` — only when the operator has enabled AUDIOLLA_FETCH_MODE) and two output modes (`output_path` writing back to staging, xor `output_url` PUTing to a presigned URL). There is no inline-bytes audio response anywhere — every audio-producing endpoint returns JSON describing where the result landed. Audiolla only fetches/uploads to URLs when the operator has explicitly enabled AUDIOLLA_FETCH_MODE — if a request returns "URL fetch/upload is disabled", do NOT try to bypass it. Do not use this skill for generic audio-processing questions or for users who haven't named audiolla.
compatibility: Requires curl and a running audiolla instance (Docker image psyb0t/audiolla:latest or :latest-cuda). AUDIOLLA_URL env var must be

_meta.json

{
  "ownerId": "kn79dhvmpjng4rp2jjk8k0v5xx80ccbk",
  "slug": "audiolla",
  "version": "1.4.2",
  "publishedAt": 1785018598691
}

references/setup.md

# audiolla setup

## Requirements

- Linux/macOS host with Docker
- ~3 GB disk for the CPU image, ~8 GB for the CUDA image (PyTorch + CUDA runtime is heavy)
- 4 GB RAM minimum, 8 GB recommended (Demucs separation peaks around 3-5 GB)
- NVIDIA GPU + drivers + `nvidia-container-toolkit` for the CUDA variant (CUDA 12.6+)

## Quick install

CPU image — no GPU needed:

```bash
docker run -d --rm --name audiolla \
  -v $HOME/.audiolla-data:/data \
  -p 8000:8000 \
  psyb0t/audiolla:latest
```

CUDA image — GPU-accelerated Demucs separation:

```bash
docker run -d --rm --name audiolla \
  --gpus all \
  -v $HOME/.audiolla-data:/data \
  -e AUDIOLLA_DEVICE=cuda \
  -p 8000:8000 \
  psyb0t/audiolla:latest-cuda
```

First run downloads the image (~3 GB CPU, ~8 GB CUDA), then on container start prefetches Demucs model weights (~600 MB) into `/data/torch_cache/`. The model fetch logs to the container's stdout — `docker logs -f audiolla` to watch.

After that, subsequent runs reuse the volume and skip the download.

**Verify:** `curl http://localhost:8000/healthz` → `{"ok": true, "device": "cpu", "engines": [...]}`.

## Configuration

All config is via environment variables passed at `docker run`:

| Variable | Default | Description |
|----------|---------|-------------|
| `AUDIOLLA_DEVICE` | `auto` | `auto`, `cpu`, `cuda`, or `cuda:N` for a specific GPU |
| `AUDIOLLA_ENGINES_FILE` | `/app/engines.json` | path to engines registry |
| `AUDIOLLA_DATA_DIR` | `/data` | where models and staged files live |
| `AUDIOLLA_AUTH_TOKEN` | _(none)_ | bearer token; empty means no auth |
| `AUDIOLLA_ENABLED_ENGINES` | _(all)_ | comma-separated slugs to allow; empty = all |
| `AUDIOLLA_PRELOAD` | _(none)_ | comma-separated slugs to load into memory at startup |
| `AUDIOLLA_ENGINE_TTL` | `600` | seconds idle before an engine is unloaded (`10m` also works) |
| `AUDIOLLA_SWEEPER_INTERVAL` | `60` | how often the idle-engine sweeper runs, in seconds |
| `AUDIOLLA_MAX_UPLOAD_BYTES` | `209715200` | upload cap (default 200 MB); also caps remote URL fetch body size |
| `AUDIOLLA_FETCH_MODE` | `disabled` | `disabled` / `allowlist` / `denylist` — server-side fetch policy for `file_url` and `output_url` |
| `AUDIOLLA_FETCH_HOSTS` | _(none)_ | comma-separated host patterns (`bucket.s3.amazonaws.com`, `*.s3.amazonaws.com`) — required when mode=allowlist |
| `AUDIOLLA_FETCH_SCHEMES` | `https` | comma-separated schemes; add `http` only for trusted local networks |
| `AUDIOLLA_FETCH_ALLOW_PRIVATE` | `false` | allow URLs resolving to private / loopback / link-local IPs (e.g. internal MinIO) |
| `AUDIOLLA_FETCH_TIMEOUT` | `30` | per-fetch/upload timeout (seconds; also accepts `30s`, `1m`) |
| `AUDIOLLA_FETCH_MAX_REDIRECTS` | `5` | max redirects per fetch; each `Location` re-validated through the policy |
| `AUDIOLLA_SOUNDFONT` | `/usr/share/sounds/sf2/FluidR3_GM.sf2` (prod images) | Default SoundFont (`.sf2`) path used by `/v1/midi/render`. Empty = midi-render refuses unless `soundfont_path` i

skill-card.md

## Description:

Connects to a user-deployed Audiolla server to perform stem separation, mastering, MIR analysis, DSP transforms, loudness normalization, and related audio file workflows.

This skill is ready for commercial/non-commercial use.

## Publisher:

[psyb0t](https://clawhub.ai/user/psyb0t)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and audio-production operators use this skill to drive an existing Audiolla HTTP or MCP server for file-based audio processing, analysis, generation, metadata, MIDI, and workflow-preset tasks.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: An Audiolla server exposed beyond localhost without authentication can let reachable users run expensive audio-processing jobs and upload large files.

Mitigation: Bind Audiolla to 127.0.0.1 unless remote access is intentional, and set a strong AUDIOLLA_AUTH_TOKEN before exposing the service.

Risk: Remote URL fetch and upload support can create SSRF or unintended network access risk when broadly enabled.

Mitigation: Keep AUDIOLLA_FETCH_MODE disabled unless needed, and use an allowlist with restricted schemes and hosts when URL I/O is required.

Risk: Mutable Docker image tags can change between deployments.

Mitigation: Prefer pinned image digests over latest tags for repeatable deployments.

## Reference(s):

- [Audiolla setup guide](references/setup.md)
- [ClawHub Audiolla skill page](https://clawhub.ai/psyb0t/skills/audiolla)

## Skill Output:

**Output Type(s):** [text, markdown, shell commands, configuration, guidance]

**Output Format:** [Markdown with inline shell commands and JSON request examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces guidance for API calls against a user-operated Audiolla server; audio-producing operations return server-side file paths or URLs rather than inline audio bytes.]

## Skill Version(s):

1.4.2 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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