mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
83 lines
2.2 KiB
Markdown
83 lines
2.2 KiB
Markdown
---
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name: songsee
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description: "Audio spectrograms/features (mel, chroma, MFCC) via CLI."
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version: 1.0.0
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author: community
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license: MIT
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metadata:
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hermes:
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tags: [Audio, Visualization, Spectrogram, Music, Analysis]
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homepage: https://github.com/steipete/songsee
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prerequisites:
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commands: [songsee]
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---
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# songsee
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Generate spectrograms and multi-panel audio feature visualizations from audio files.
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## Prerequisites
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Requires [Go](https://go.dev/doc/install):
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```bash
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go install github.com/steipete/songsee/cmd/songsee@latest
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```
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Optional: `ffmpeg` for formats beyond WAV/MP3.
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## Quick Start
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```bash
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# Basic spectrogram
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songsee track.mp3
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# Save to specific file
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songsee track.mp3 -o spectrogram.png
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# Multi-panel visualization grid
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songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux
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# Time slice (start at 12.5s, 8s duration)
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songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg
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# From stdin
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cat track.mp3 | songsee - --format png -o out.png
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```
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## Visualization Types
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Use `--viz` with comma-separated values:
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| Type | Description |
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|------|-------------|
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| `spectrogram` | Standard frequency spectrogram |
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| `mel` | Mel-scaled spectrogram |
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| `chroma` | Pitch class distribution |
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| `hpss` | Harmonic/percussive separation |
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| `selfsim` | Self-similarity matrix |
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| `loudness` | Loudness over time |
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| `tempogram` | Tempo estimation |
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| `mfcc` | Mel-frequency cepstral coefficients |
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| `flux` | Spectral flux (onset detection) |
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Multiple `--viz` types render as a grid in a single image.
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## Common Flags
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| Flag | Description |
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|------|-------------|
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| `--viz` | Visualization types (comma-separated) |
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| `--style` | Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray` |
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| `--width` / `--height` | Output image dimensions |
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| `--window` / `--hop` | FFT window and hop size |
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| `--min-freq` / `--max-freq` | Frequency range filter |
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| `--start` / `--duration` | Time slice of the audio |
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| `--format` | Output format: `jpg` or `png` |
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| `-o` | Output file path |
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## Notes
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- WAV and MP3 are decoded natively; other formats require `ffmpeg`
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- Output images can be inspected with `vision_analyze` for automated audio analysis
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- Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines
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