songsee
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install nousresearch-hermes-agent-songsee
Repository
Skill path: skills/music-creation/songsee
Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
Open repositoryBest for
Primary workflow: Design Product.
Technical facets: Full Stack, Designer.
Target audience: everyone.
License: MIT.
Original source
Catalog source: SkillHub Club.
Repository owner: NousResearch.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install songsee into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/NousResearch/hermes-agent before adding songsee to shared team environments
- Use songsee for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: songsee
description: Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation.
version: 1.0.0
author: community
license: MIT
metadata:
hermes:
tags: [Audio, Visualization, Spectrogram, Music, Analysis]
homepage: https://github.com/steipete/songsee
---
# songsee
Generate spectrograms and multi-panel audio feature visualizations from audio files.
## Prerequisites
Requires [Go](https://go.dev/doc/install):
```bash
go install github.com/steipete/songsee/cmd/songsee@latest
```
Optional: `ffmpeg` for formats beyond WAV/MP3.
## Quick Start
```bash
# Basic spectrogram
songsee track.mp3
# Save to specific file
songsee track.mp3 -o spectrogram.png
# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux
# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg
# From stdin
cat track.mp3 | songsee - --format png -o out.png
```
## Visualization Types
Use `--viz` with comma-separated values:
| Type | Description |
|------|-------------|
| `spectrogram` | Standard frequency spectrogram |
| `mel` | Mel-scaled spectrogram |
| `chroma` | Pitch class distribution |
| `hpss` | Harmonic/percussive separation |
| `selfsim` | Self-similarity matrix |
| `loudness` | Loudness over time |
| `tempogram` | Tempo estimation |
| `mfcc` | Mel-frequency cepstral coefficients |
| `flux` | Spectral flux (onset detection) |
Multiple `--viz` types render as a grid in a single image.
## Common Flags
| Flag | Description |
|------|-------------|
| `--viz` | Visualization types (comma-separated) |
| `--style` | Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray` |
| `--width` / `--height` | Output image dimensions |
| `--window` / `--hop` | FFT window and hop size |
| `--min-freq` / `--max-freq` | Frequency range filter |
| `--start` / `--duration` | Time slice of the audio |
| `--format` | Output format: `jpg` or `png` |
| `-o` | Output file path |
## Notes
- WAV and MP3 are decoded natively; other formats require `ffmpeg`
- Output images can be inspected with `vision_analyze` for automated audio analysis
- Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines