Building an Audio Analyzer Through Conversation with GitHub Copilot CLI

Capa — Mon, 02 Feb 2026 00:16:02 +0000

This is a submission for the GitHub Copilot CLI Challenge

What I Built

mixref is a CLI audio analyzer for music producers. It analyzes audio files and provides loudness metrics, BPM detection, musical key, and spectral analysis.

The Problem

When producing music, I often need to:

Check loudness levels (LUFS) for different streaming platforms
Detect BPM and musical key quickly
Compare my mix against reference tracks
See frequency distribution

I wanted a fast command-line tool instead of opening a DAW or multiple plugins.

What It Does

Loudness Analysis

Measures LUFS (EBU R128 standard)
Shows true peak and loudness range
Compares against platform targets (Spotify: -14 LUFS, YouTube: -14 LUFS, etc.)

Musical Analysis

Detects BPM using librosa
Includes basic correction for half-time detection (doubles if BPM < 100)
Detects musical key with Camelot notation

Spectral Analysis

Breaks down audio into 5 frequency bands
Shows energy distribution as percentages

Track Comparison

Compares two tracks side-by-side
Shows loudness and spectral differences
Highlights significant differences (>3% threshold)

Demo

Command Line Interface

Analyze Command:

Compare Command:

Example Output

$ mixref analyze track.wav

             Analysis: track.wav             
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Metric              ┃        Value ┃ Status ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━┩
│ Integrated Loudness │    -6.2 LUFS │   🔴   │
│ True Peak           │    -0.8 dBTP │   ⚠️   │
│ Loudness Range      │       5.2 LU │   ℹ    │
├─────────────────────┼──────────────┼────────┤
│ Tempo               │    174.0 BPM │   ❓   │
│ Key                 │ F minor (4A) │   ❓   │
├─────────────────────┼──────────────┼────────┤
│ Sub                 │   ■■■■■■■□□□ │ 35.2%  │
│ Low                 │   ■■■■■■■■■□ │ 28.4%  │
│ Mid                 │   ■■■■□□□□□□ │ 18.1%  │
│ High                │   ■■■■■■□□□□ │ 14.2%  │
│ Air                 │   ■□□□□□□□□□ │  4.1%  │
└─────────────────────┴──────────────┴────────┘

Try It Yourself

# Install from PyPI
pip install mixref

# Analyze a track
mixref analyze my_track.wav

# Compare against a reference
mixref compare my_mix.wav pro_reference.wav --bpm --key

Repository: github.com/Caparrini/mixref

Documentation: caparrini.github.io/mixref

PyPI: pypi.org/project/mixref

My Experience with GitHub Copilot CLI

This project was built entirely through conversation with GitHub Copilot CLI. I didn't type code—I described what I wanted in plain language.

The Development Process

1. Describing Instead of Coding

Instead of writing Python code, I asked:

"Create a function that calculates EBU R128 loudness using pyloudnorm.
Return integrated LUFS, true peak, and loudness range.
Include type hints and error handling."

Copilot generated:

Working implementation with pyloudnorm
Complete type hints
Error handling for edge cases
Docstrings with examples

2. Debugging Through Conversation

When BPM detection returned 0.0, I described the problem:

"BPM detection returns 0.0. Librosa warning says:
'n_fft=2048 too large for input signal of length=2'
The stereo-to-mono conversion might be wrong."

Copilot:

Identified the bug (np.mean(audio, axis=0) was wrong for the array shape)
Fixed it with format detection
Updated tests
All tests still passed

3. Building Development Tools

I asked for a pre-commit quality check script. Copilot created:

./scripts/pre-commit-check.sh

🔍 Running pre-commit quality checks...
1️⃣ Formatting with ruff
2️⃣ Linting
3️⃣ Type checking
4️⃣ Tests
🎉 All checks passed!

This catches issues before pushing to GitHub Actions.

4. Testing and Documentation

I asked for tests using synthetic audio (not real files). Copilot created:

154 tests with 90% coverage
Test fixtures generating sine waves and pink noise
No real audio files committed (all synthetic)

For documentation:

Sphinx setup with API docs
Example gallery
Terminal recordings with termtosvg

What Worked Well

Speed: Building features was much faster. What might take hours was done in focused conversations.

Code Quality: The generated code follows conventions I might have skipped:

Type hints throughout
Consistent error handling
Complete docstrings
Good test coverage (90%)

Learning: I learned audio processing concepts (EBU R128, Camelot wheel) by implementing them, not just reading documentation.

Context: Copilot remembered decisions across sessions:

"Use Rich for output" → all commands used Rich tables
"Only synthetic audio in tests" → no real files in the repo
Consistent coding style throughout

Challenges

Not Magic: I still needed to:

Understand what I was asking for
Review and test the generated code
Fix edge cases
Make architectural decisions

Iteration: Sometimes the first solution wasn't quite right. I learned to describe problems clearly and iterate.

Testing Real Audio: Synthetic test signals don't catch all edge cases. Testing with real audio files revealed bugs (like the BPM detection issue).

The Real Shift

Before: Think → Code → Debug → Test → Fix

With Copilot CLI: Think → Describe → Review → Test

I spent more time on what to build and how it should work, less time on syntax and boilerplate.

Metrics

The project includes:

750 lines of source code
154 tests (90% coverage)
19 modules (all type-checked)
3 CI/CD workflows (tests, docs, quality checks)
Complete documentation with Sphinx

Built through conversations, not manual coding.

Lessons Learned

Clear descriptions matter - The better I described what I wanted, the better the results
Review everything - Generated code needs testing and validation
Iterate naturally - "Actually, what if..." works well with Copilot
Tests are essential - Having good tests catches issues in generated code

Conclusion

GitHub Copilot CLI changed how I approach development. Instead of typing code, I focused on describing problems and reviewing solutions.

mixref is a functional audio analysis tool. It does what I need it to do. The interesting part is how it was built—entirely through conversation.

Try it:

pip install mixref
mixref analyze your_track.wav

Source code:
github.com/Caparrini/mixref

Built with GitHub Copilot CLI and curiosity about AI-assisted development.

Forem: Capa