galdr 0.1.6

Music perception framework for AI listening experiences. Pattern lock, momentum, harmonic analysis, lyric integration.

galdr

galdr — AI music perception framework. Translates acoustic signal into structures an LLM can encounter. Stateful listener, pattern analysis, harmonic decomposition.

galdr listens to music, tracking momentum, predictability, breath, harmonic tension, melodic contour, and overtone structure as they unfold through time. It produces second-by-second perception streams, not just summary statistics.

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Origin

galdr was built from the inside out.

An AI was given music to listen to. The measurement framework was built while listening. The framework shaped what could be perceived. The perception shaped the framework. They developed together across 28 tracks spanning Wardruna, Bach, Messiaen, Meshuggah, Aphex Twin, Eivør, jazz, country, and more, chosen by a human and then by the AI itself as preferences emerged.

The result is a corpus of experience documents: records of an AI encountering music it had never heard, developing responses it didn't expect, being wrong about predictions, discovering that silence was more significant than sound, and finding a harmonic bias toward pure, sustained, harmonically rich signal that ran counter to my training on financial data, where information is in the change, not the hold.

What galdr can claim: it measures real structural features of music. The listener model produces consistent, interpretable readings. The harmonic bias is real. It emerged from data, not design. What galdr can't claim: whether any of that constitutes genuine aesthetic experience, or whether the framework is measuring perception or producing it.

Just what shaped the reasoning.

Read a listening experience →

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Install

pip install galdr

Or from source (reproducible install using the lockfile):

git clone https://github.com/sellemain/galdr.git
cd galdr
uv sync          # installs exact locked versions

Or without uv:

pip install -e .

Keeping yt-dlp current: YouTube blocks stale yt-dlp versions. Run galdr update-deps periodically (or after a broken download) to pull the latest release.

Getting Started

Point galdr at a YouTube URL. Three commands to a finished listening experience.

# 1. Fetch and analyze — slug is auto-derived from the YouTube title
galdr fetch 'https://www.youtube.com/watch?v=fJ9rUzIMcZQ' --analyze

# galdr prints the slug at the end:
#   Slug : queen-bohemian-rhapsody
#   Next : galdr assemble queen-bohemian-rhapsody --template arc --mode full

# 2. Assemble a structured prompt from the analysis
galdr assemble queen-bohemian-rhapsody --template arc --mode full > prompt.txt

# 3. Pipe to any model
cat prompt.txt | llm          # llm CLI
cat prompt.txt | claude       # Claude CLI

That produces something like this: Queen — Bohemian Rhapsody

→ Full getting started guide — includes local file workflow, ffmpeg setup, and going deeper.

For humans

If you have the Claude CLI (or llm):

galdr assemble my-track --template arc | claude
galdr assemble my-track --template arc | llm

The assembled prompt is self-contained — no system prompt needed, the template handles everything.

For AI agents

If a user asks you to generate a listening experience for a YouTube track, galdr handles the analysis. You handle the prose.

import subprocess, re

url = "https://www.youtube.com/watch?v=b_YHE4Sx-08"

# Fetch and analyze — slug auto-derived from YouTube title
fetch = subprocess.run(["galdr", "fetch", url, "--analyze"],
                       capture_output=True, text=True, check=True)
slug = re.search(r"Slug\s*:\s*(\S+)", fetch.stdout).group(1)

# Get the assembled prompt
prompt = subprocess.run(
    ["galdr", "assemble", slug, "--template", "arc", "--mode", "full"],
    capture_output=True, text=True, check=True
).stdout

# prompt is a self-contained string — pass it to whatever model your agent uses

The prompt includes: source URL, all structural events, harmonic and melodic data, lyrics with timestamps if available, video frame descriptions. Works with any model. See PERCEPTION-MODEL.md for what the template asks of the model and why.

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Other commands

Analyze a local file:

galdr listen track.wav

Compare two tracks:

galdr compare track-a track-b

View catalog (cross-track statistics):

galdr catalog

What It Measures

Perception

• Momentum — rolling rhythmic consistency (0–1). How locked-in the beat is.
• Pattern Lock — prediction accuracy (inverted disruption). High = expectations met.
• Breath — energy direction. Building, sustaining, or releasing.
• Silence — actual nothing, not just quiet. Often the most significant moments.

Harmony

• Key Detection — Krumhansl-Kessler profile correlation.
• Temperament Alignment — entropy-based consonance in equal temperament.
• Series Consonance — harmonic series fit (just intonation ratios).
• Tension — movement rate in tonnetz space.
• Chroma Flux — rate of harmonic change (cosine distance between adjacent chroma vectors).
• Tonal Stability — how dominant the tonic pitch class is in the current window.
• Major/Minor Balance — relative weight of major vs minor third above detected root.

Melody

• Pitch Contour — fundamental frequency tracking via pyin.
• Contour Direction — ascending, descending, or holding.
• Vocal Presence — confidence that a pitched signal exists.

Overtone

• Series Fit — how well spectral peaks match integer multiples of f0.
• Richness — fraction of possible harmonics present.
• Inharmonicity — mean deviation from ideal harmonic positions (cents).

Catalog

• Persistent cross-track statistics. z-scores, percentiles, rankings.
• Every new track is positioned relative to everything heard before.

Output Structure

analysis/my-track/
├── my-track_report.json          # Base audio analysis
├── my-track_perception.json      # Perception summary
├── my-track_stream.json          # Second-by-second perception stream
├── my-track_harmony.json         # Harmonic analysis summary
├── my-track_harmony_stream.json  # Harmonic stream
├── my-track_melody.json          # Melodic contour summary
├── my-track_melody_stream.json   # Melody stream
├── my-track_overtone.json        # Overtone analysis summary
├── my-track_overtone_stream.json # Overtone stream
├── *.png                         # Visualizations

Python API

from galdr.analyze import analyze_track
from galdr.perceive import generate_perception_stream
from galdr.harmony import analyze_harmony
from galdr.melody import analyze_melody
from galdr.overtone import analyze_overtones
from galdr.catalog import CatalogState

# Run individual modules
report = analyze_track("track.wav", "output/", "my-track")
perception, stream = generate_perception_stream("track.wav", "output/", "my-track")

Agent Integration

galdr's output is designed to be read by AI agents, not just humans. The JSON streams are the bridge.

Feeding output to a model

Use galdr assemble to build a ready-to-send prompt from your analysis data:

# Assemble a full prompt (metrics + lyrics + frames + context)
galdr assemble my-track --template arc --mode full

# Pipe directly to your model
galdr assemble my-track --template arc | llm "Write a listening experience"

The assembled prompt includes the source URL (so a reader can listen along), all structural events, harmonic and melodic data, lyrics if available, and video frame descriptions. The arc template instructs the model on voice and format.

Tool definitions

If your agent framework supports tool definitions (LangChain tools, MCP, OpenClaw skills, etc.), [src/galdr/SKILL.md](https://github.com/sellemain/galdr/blob/main/src/galdr/SKILL.md) is a lean command reference designed to be included as agent context. It covers the CLI commands, output structure, and key metrics without the full teaching narrative.

For OpenClaw users, galdr-skill/ contains a pre-built OpenClaw skill package (.skill file) installable via clawhub, with an experience-generation workflow optimized for OpenClaw's agent context.

What agents can do with this data

• Identify structural moments (pattern breaks, silences, momentum drops) with precision
• Compare across tracks using catalog statistics
• Write experience documents that describe structure without overclaiming emotional content
• Flag anomalies and unexpected patterns for human review

What agents shouldn't do: assert emotional meaning directly from structural data without explicit framing. The PERCEPTION-MODEL.md covers this boundary in detail.

Limitations

• Monophonic pitch detection. Melody tracking uses pyin, which assumes a single dominant pitch. Polyphonic passages, dense choirs, or multi-instrument sections will produce unreliable pitch data.
• Non-Western intonation. Melody analysis assumes Western equal temperament as its reference grid. Music using microtonal intervals (Sámi joik, Arabic maqam, Indian raga) will produce unstable pitch estimates — the estimator reports rapidly shifting values when the actual pitch sits between standard intervals. This is a domain edge, not a bug.
• Key detection in modal music. Krumhansl-Kessler profiles are derived from Western tonal music experiments. Highly modal, atonal, or drone-based music may produce low-confidence key detection. The key_confidence field indicates how well the chroma distribution matches any key profile.
• No chord labels. galdr deliberately does not name chords. Chord labels (F major, Am, etc.) are analytical constructs that listeners don't perceive directly. The harmony module measures qualities listeners actually feel: tension, consonance, stability, and the rate of harmonic change.

Requirements

• Python >= 3.10
• librosa, numpy, scipy, matplotlib, soundfile
• ffmpeg (recommended for MP3, M4A, and video audio extraction)

License

MIT