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adam-researchh

Music Analysis

作者 Adam-Researchh · GitHub ↗ · v3.0.2 · MIT-0
cross-platform ✓ 安全检测通过
543
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5
当前安装
7
版本数
在 OpenClaw 中安装
/install music-analysis
功能描述
Analyze music/audio files locally without external APIs. Extract tempo, pocket/groove feel, pulse stability, swing proxy, section/repetition structure, key c...
使用说明 (SKILL.md)

Music Analysis (Local, No External APIs)

Primary tool: a full listen that combines snapshot analysis, structure, groove, harmonic tension, temporal mood mapping, and optional Whisper lyric alignment into one report.

1. Full Listen — primary / recommended

python3 skills/music-analysis/scripts/listen.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/listen.py track.mp3 --json
python3 skills/music-analysis/scripts/listen.py track.mp3 --out report.txt
python3 skills/music-analysis/scripts/listen.py track.mp3 --json --out report.json

What it does in one pass:

  1. Snapshot analysis: tempo, pulse stability, swing proxy, key clarity, harmonic tension, timbre, structure
  2. Whisper lyric transcription and filtering first — keep only real lyric text, drop artifact tags like [MUSIC]
  3. Temporal listen: windowed energy / mood / tension journey
  4. Synthesis layer that aligns lyrics with peak / tension / quiet windows and lets the lyric layer override the final vibe when confidence is high

Human-readable output structure

  • SNAPSHOT
    • groove/pocket
    • structure summary + repeated sections
    • harmony (key clarity + tension)
    • timbre descriptor tags
  • INSTRUMENT READ
    • likely instrument palette (strong/likely/possible confidence)
    • per-section instrument entrances and exits
    • how instruments color the emotional feel
    • written as natural language, not clinical data
  • TEMPORAL JOURNEY
    • opening / middle / closing mood-energy-tension read
    • peak / quietest / tensest moments
    • mood journey and transition count
  • EMOTIONAL READ
    • explainable emotion summary based on measured features
  • LYRICS
    • Whisper segment count
    • excerpt or graceful skip note
  • SYNTHESIS
    • lyric-energy/tension alignment
    • peak / tension / quiet lyric moments
  • ALIGNED TIMELINE
    • per-window moments where transitions / lyrics / tension spikes occur

2. Snapshot Analysis — standalone

python3 skills/music-analysis/scripts/analyze_music.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/analyze_music.py track.mp3 --json

Reports:

  • tempo / pulse stability / pulse confidence / swing proxy / pocket
  • key estimate / key clarity / chroma entropy / harmonic change / tonal motion / tension
  • timbre descriptors (brightness, richness, low-end, contrast, dynamic range)
  • section labels (A/B/C...) and repeated material detection
  • explainable emotional read with reasons

3. Temporal Listen — standalone

python3 skills/music-analysis/scripts/temporal_listen.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/temporal_listen.py track.mp3 --json

Reports:

  • sliding-window timeline (4s windows, 2s hops)
  • energy contour
  • mood labels
  • harmonic tension + tonal motion
  • transition types (drop hits, pulls back, tightens harmonically, shifts color, evolves)
  • narrative arc (mountain / ascending / descending / plateau / wave)

Interpretation rules

  • Structure labels are similarity labels, not verse/chorus claims.
  • Swing proxy is a feel estimate, not drummer-grade microtiming truth.
  • Emotion is explainable, derived from pulse + timbre + harmonic tension rather than a black-box mood guess.
  • Lyrics can override the final vibe when filtered Whisper text is confident and emotionally clear.

Audio sourcing

The tool needs a real audio file on disk.

  • Direct file (mp3, wav, flac, ogg, m4a — anything ffmpeg/librosa can read)
  • YouTube / supported URLs: yt-dlp -x --audio-format mp3 -o "output.mp3" "URL_OR_SEARCH"

Whisper lyrics transcription

listen.py uses:

  • CLI: /opt/homebrew/bin/whisper-cli
  • Model: ~/.local/share/whisper-cpp/ggml-large-v3-turbo.bin
  • Preprocess: convert input to mono 16kHz WAV via ffmpeg
  • Fallback: skip gracefully if Whisper is missing or errors

Dependencies

Python:

  • librosa
  • numpy

System:

  • ffmpeg
  • ffprobe

Workspace hygiene

  • Keep temporary audio files in a dedicated temp/output folder for the skill.
  • Avoid modifying unrelated project files while working on audio analysis tasks.
安全使用建议
This skill appears to do what it claims: offline analysis of audio files using librosa and local tools. Before installing or running: 1) ensure you trust and want any local binaries it calls (ffmpeg/ffprobe and optionally whisper-cli) because the scripts invoke them via subprocess; 2) the hardcoded whisper-cli path (/opt/homebrew/bin/whisper-cli) and the model path (~/.local/share/whisper-cpp/...) are mac- and home-directory-specific — if you don't have Whisper or the model, the code will skip it, but if you do, be aware Whisper models can be large; 3) the README suggests using yt-dlp to fetch YouTube audio — that will download data from the network if you follow that workflow; 4) no credentials are requested and there are no network callbacks in the code, but you should still review the included scripts yourself (they are present) before running them on sensitive data. If you want higher confidence, run the scripts in an isolated environment (temporary folder or container) and verify the absence/presence of whisper-cli and the model if you do not want transcription.
功能分析
Type: OpenClaw Skill Name: music-analysis Version: 3.0.2 The music-analysis skill bundle is a legitimate tool for local audio processing, utilizing librosa and numpy for feature extraction (tempo, key, timbre) and whisper-cli for lyric transcription. The implementation in scripts like listen.py and analyze_music.py follows security best practices by using subprocess with argument lists to interact with ffmpeg and ffprobe, preventing shell injection. No evidence of data exfiltration, network communication, or malicious prompt injection was found; the hardcoded paths for Whisper components are environment-specific but not harmful.
能力评估
✓ Purpose & Capability
Name/description (local music/audio analysis) align with the included Python scripts and declared dependencies (librosa, numpy, ffmpeg/ffprobe). The code implements tempo, timbre, structure, instrument detection and optional lyric alignment via a local Whisper CLI/model which fits the stated purpose. Minor note: SKILL.md suggests using yt-dlp for fetching YouTube audio (network use) as an optional audio sourcing method; that is outside the 'no external APIs' claim but presented as an explicit optional workflow.
✓ Instruction Scope
Runtime instructions and scripts operate on local audio files, run ffmpeg/ffprobe and (optionally) whisper-cli, and write analysis reports to disk if requested. The SKILL.md and scripts do not instruct reading unrelated system files, environment secrets, or posting data to remote endpoints. Whisper usage is optional and the code includes a fallback path if Whisper is missing.
✓ Install Mechanism
There is no install spec and requirements.txt only lists librosa and numpy. The skill relies on system binaries (ffmpeg/ffprobe) and optionally a locally installed whisper-cli and model file; nothing in the repository pulls code from arbitrary URLs or writes external installers. This is a low-risk install posture.
✓ Credentials
The skill declares no required environment variables or credentials. The code does reference concrete filesystem paths (a Homebrew whisper-cli path and a home-dir model path) but these are optional and not secrets. No credentials or sensitive environment access is requested.
✓ Persistence & Privilege
The skill is user-invocable, not always-included, and does not modify other skills or agent-wide configuration. It runs as a normal local tool and does not request elevated or persistent privileges.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install music-analysis
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /music-analysis 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v3.0.2
Public docs cleanup: replace internal sandbox/trading references with generic workspace hygiene guidance.
v3.0.1
Cleanup release: remove stale internal v2 reference, rename public output section to 'Instrument Read', and replace first-person/internal language in skill docs and generated output with neutral public wording.
v3.0.0
v3: Instrument detection — spectral profile matching for 11 instrument families, per-section arrangement tracking, natural language narrative ('WHAT I HEAR' section). Upright/electric bass tiebreaker. Joel directive: emotion over science.
v1.2.0
Release 1.2.0: improved full-listen workflow, lyric-aware synthesis, and local analysis tooling.
v1.0.1
Improve full-listen workflow, lyric-aware synthesis, and local analysis tooling.
v1.1.0
Bug fixes: ffprobe path resolution, whisper-cpp integration, min-tokens threshold
v1.0.0
Initial release: analyze_music.py (snapshot) + temporal_listen.py (journey). Pure librosa signal processing — BPM, key, energy, spectral, mood timelines, narrative arcs. No external APIs.
元数据
Slug music-analysis
版本 3.0.2
许可证 MIT-0
累计安装 5
当前安装数 5
历史版本数 7
常见问题

Music Analysis 是什么？

Analyze music/audio files locally without external APIs. Extract tempo, pocket/groove feel, pulse stability, swing proxy, section/repetition structure, key c... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 543 次。

如何安装 Music Analysis？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install music-analysis」即可一键安装，无需额外配置。

Music Analysis 是免费的吗？

是的，Music Analysis 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。

Music Analysis 支持哪些平台？

Music Analysis 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Music Analysis？

由 Adam-Researchh（@adam-researchh）开发并维护，当前版本 v3.0.2。

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