songsee

Generate spectrograms and analyze mel, chroma, and MFCC audio features via CLI.

1|Updated Jun 9, 2026

Search Tags:#CLI #spectrogram #audio analysis #MFCC #mel spectrogram

Ready to run

npx skills add https://github.com/aivos-xie/hermes-skills --skill songsee-aivos-xie

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: songsee
Source: https://github.com/aivos-xie/hermes-skills/tree/main/media/songsee
Command: npx skills add https://github.com/aivos-xie/hermes-skills --skill songsee-aivos-xie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires go, ffmpeg, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Analyze audio files and quickly generate visual representations of their features like mel, chroma, MFCCs via CLI, enabling rapid understanding and comparisons of audio signals.

Core Features & Use Cases

Generate Spectrograms: Visualize audio content with various spectrogram types.
Feature Analysis: Obtain insights on pitch, rhythm, and timbre using tools like mel spectrogram and MFCC analysis.
Quick Comparison: Easily compare different audio files and their characteristics side-by-side with a simple CLI interface.

Quick Start

Generate a spectrogram for the track.mp3 audio file with: songsee track.mp3

Frequently Asked Questions about songsee

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a mel spectrogram for an audio file using CLI?▼

You can generate a spectrogram by running the songsee CLI with your audio file. It processes the input and outputs visual representations of mel, chroma, and MFCC features for rapid signal analysis.

What do I need to analyze audio features like MFCC if my files are not standard WAV?▼

You need ffmpeg installed alongside the Go environment to analyze audio features from various formats. ffmpeg provides the necessary decoding support for processing additional audio formats.

Can I compare multiple audio files side-by-side using a command line tool?▼

Yes, you can compare multiple audio files side-by-side using the songsee CLI. It generates spectrograms and feature visualizations for each file, enabling quick comparisons of their audio characteristics.

What is the best way to visualize pitch and rhythm for debugging an audio processing pipeline?▼

The best way to visualize pitch and rhythm for debugging is using songsee to generate chroma and mel spectrograms. This provides immediate visual insights into the audio signal characteristics.

Does songsee require a Go environment to run?▼

Yes, songsee requires a Go environment to run. The CLI is built using Go and depends on this environment to execute its audio analysis and feature generation processes.

Why use MFCC analysis for audio files?▼

MFCC analysis is used to represent the timbre of audio files. By generating MFCC visualizations, you can obtain insights on pitch, rhythm, and timbre to debug audio processing pipelines.