BytesAgain is a curated directory of 60,000+ AI agent skills from ClawHub, GitHub, LobeHub, and Dify. Search skills by keyword in 7 languages, browse by role (developer, creator, trader, marketer) or by use case.

How do I find AI skills on BytesAgain?

Use the search bar on BytesAgain.com to search by keyword in 7 languages. You can also browse by role (developer, creator, trader, marketer) or by use case. Each skill shows install instructions for Claude, Cursor, OpenClaw, Continue, and more.

Yes, BytesAgain is completely free. No registration required for searching skills. The MCP API is also free with rate limits.

Does BytesAgain have an API for AI agents?

Yes! BytesAgain provides a free MCP SSE endpoint at /api/mcp/sse for AI agents, plus a REST API at /api/mcp?action=search&q= . No authentication needed.

Can I request a new AI skill on BytesAgain?

Yes! Visit the Requests page on BytesAgain.com to submit a skill request. Your request will be visible to the community and notified to the site admin.

🦀 ClawHub

Voice Note To Midi

by @danbennettuk

Convert voice notes, humming, and melodic audio recordings to quantized MIDI files using ML-based pitch detection and intelligent post-processing

Versionv0.1.0

Downloads2,265

Installs1

Comments1

#audio #social-media #legal #communication

View on ClawHub →

TERMINAL

clawhub install voice-note-to-midi

📖 About This Skill

name: voice-note-to-midi description: Convert voice notes, humming, and melodic audio recordings to quantized MIDI files using ML-based pitch detection and intelligent post-processing author: Clawd tags: [audio, midi, music, transcription, machine-learning]

🎵 Voice Note to MIDI

Transform your voice memos, humming, and melodic recordings into clean, quantized MIDI files ready for your DAW.

What It Does

This skill provides a complete audio-to-MIDI conversion pipeline that:

1. Stem Separation - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds 2. ML-Powered Pitch Detection - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction 3. Key Detection - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles 4. Intelligent Quantization - Snaps notes to a configurable timing grid with optional key-aware pitch correction 5. Post-Processing - Applies octave pruning, overlap-based harmonic removal, and legato note merging for clean output

Pipeline Architecture

Audio Input (WAV/M4A/MP3)
    ↓
┌─────────────────────────────────────┐
│ Step 1: Stem Separation (HPSS)     │
│ - Isolate harmonic content          │
│ - Remove drums/percussion           │
│ - Noise gating                      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 2: Pitch Detection             │
│ - Basic Pitch ML model (Spotify)    │
│ - Polyphonic note detection         │
│ - Onset/offset estimation           │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 3: Analysis                    │
│ - Pitch class distribution          │
│ - Key detection                     │
│ - Dominant note identification      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 4: Quantization & Cleanup      │
│ - Timing grid snap                  │
│ - Key-aware pitch correction        │
│ - Octave pruning (harmonic removal) │
│ - Overlap-based pruning             │
│ - Note merging (legato)             │
│ - Velocity normalization            │
└─────────────────────────────────────┘
    ↓
MIDI Output (Standard MIDI File)

Setup

Prerequisites

Python 3.11+ (Python 3.14+ recommended)

FFmpeg (for audio format support)

pip

Installation

Quick Install (Recommended):

cd /path/to/voice-note-to-midi
./setup.sh

This automated script will:

Check Python 3.11+ is installed

Create the ~/melody-pipeline directory

Set up the virtual environment

Install all dependencies (basic-pitch, librosa, music21, etc.)

Download and configure the hum2midi script

Add melody-pipeline to your PATH

Manual Install:

If you prefer manual setup:

mkdir -p ~/melody-pipeline
cd ~/melody-pipeline
python3 -m venv venv-bp
source venv-bp/bin/activate
pip install basic-pitch librosa soundfile mido music21
chmod +x ~/melody-pipeline/hum2midi

5. Add to your PATH (optional):

echo 'export PATH="$HOME/melody-pipeline:$PATH"' >> ~/.bashrc
source ~/.bashrc

Verify Installation

cd ~/melody-pipeline
./hum2midi --help

Usage

Basic Usage

Convert a voice memo to MIDI:

./hum2midi my_humming.wav

This creates my_humming.mid with 16th-note quantization.

Specify Output File

./hum2midi input.wav output.mid

Command-Line Options

| Option | Description | Default | |--------|-------------|---------| | --grid | Quantization grid: 1/4, 1/8, 1/16, 1/32 | 1/16 | | --min-note | Minimum note duration in milliseconds | 50 | | --no-quantize | Skip quantization (output raw Basic Pitch MIDI) | disabled | | --key-aware | Enable key-aware pitch correction | disabled | | --no-analysis | Skip pitch analysis and key detection | disabled |

Usage Examples

#### Quantize to eighth notes

./hum2midi melody.wav --grid 1/8

#### Key-aware quantization (recommended for tonal music)

./hum2midi song.wav --key-aware

#### Require longer minimum notes

./hum2midi humming.wav --min-note 100

#### Skip analysis for faster processing

./hum2midi quick.wav --no-analysis

#### Combine options

./hum2midi recording.wav output.mid --grid 1/8 --key-aware --min-note 80

Processing MIDI Input

You can also process existing MIDI files through the quantization pipeline:

./hum2midi input.mid output.mid --grid 1/16 --key-aware

This skips the audio processing steps and goes directly to analysis and quantization.

Sample Output

═══════════════════════════════════════════════════════════════ hum2midi - Melody-to-MIDI Pipeline (Basic Pitch Edition) [Key-Aware Mode Enabled] ═══════════════════════════════════════════════════════════════ Input: my_humming.wav Output: my_humming.mid → Step 1: Stem Separation (HPSS) Isolating melodic content... Loaded: 5.23s @ 44100Hz ✓ Melody stem extracted → 5.23s → Step 2: Audio-to-MIDI Conversion (Basic Pitch) Running Spotify's Basic Pitch ML model on melody stem... ✓ Raw MIDI generated (Basic Pitch) → Step 3: Pitch Analysis & Key Detection Notes detected: 42 total, 7 unique Note range: C3 - G4 Pitch classes: C3, E3, G3, A3, C4, D4, G4 Dominant note: G3 (23.8% of notes) Detected key: G major → Step 4: Quantization & Cleanup Octave pruning: removed 3 harmonic notes above 67 (median+12) Overlap pruning: removed 2 harmonic notes at overlapping positions Note merging: merged 5 staccato chunks into legato notes (gap<=60 ticks) Grid: 240 ticks (1/16) Notes: 38 notes Key: G major Key-aware: 2 notes corrected to scale Tempo: 120 BPM ✓ Quantized MIDI saved ═══════════════════════════════════════════════════════════════ ✓ Done! Output: my_humming.mid ═══════════════════════════════════════════════════════════════ 📊 ANALYSIS SUMMARY ───────────────────────────────────────────────────────────── Detected Notes: C3, E3, G3, A3, C4, D4, G4 Detected Key: G major Quantization: Key-aware mode (notes snapped to scale)

MIDI Info: 38 notes, 7 unique pitches, 120 BPM Pitches: C3, E3, G3, A3, C4, D4, G4

Notes & Limitations

Audio Quality Matters

Clear, loud melody produces the best results

Background noise can cause false note detection

Reverb and effects may confuse pitch detection

Close-mic'd vocals work significantly better than room recordings

Musical Considerations

Monophonic sources work best (single melody line)

Polyphonic audio (chords, multiple instruments) will produce messy results

Vibrato and pitch bends may be quantized to stepped pitches

Rapid note passages may be missed or merged

Technical Limitations

Tempo is fixed at 120 BPM in output (time positions are preserved, but tempo may need adjustment in your DAW)

Note velocities are normalized but may need manual adjustment

Very short notes (<50ms) may be filtered out by default

Extreme pitch ranges may cause octave detection issues

Post-Processing Recommendations

After generating MIDI, you may want to:

1. Import into your DAW and adjust tempo to match your original recording 2. Quantize further if stricter timing is needed 3. Adjust note velocities for dynamics 4. Apply swing/groove templates if the rigid grid sounds too mechanical 5. Edit individual notes that were misdetected (common with fast runs)

Supported Audio Formats

Input formats supported via FFmpeg:

WAV, AIFF, FLAC (uncompressed, best quality)

MP3, M4A, AAC (compressed, acceptable)

OGG, OPUS (open source formats)

Most other formats FFmpeg supports

Troubleshooting

No notes detected

Check that input file isn't silent or corrupted

Try increasing --min-note threshold

Verify audio has clear melodic content (not just noise)

Too many notes / messy output

Enable octave pruning and overlap pruning (on by default)

Use --key-aware to constrain to musical scale

Check for background noise in source audio

Wrong key detected

Key detection works best with at least 8-10 measures of music

Chromatic passages may confuse the detector

Manually review and adjust in your DAW if needed

Notes in wrong octave

Basic Pitch sometimes detects harmonics instead of fundamentals

The pipeline includes pruning, but some may slip through

Use your DAW's transpose function for simple octave shifts

References

Basic Pitch - Spotify's polyphonic pitch detection model

librosa HPSS - Harmonic-Percussive Source Separation

Krumhansl-Kessler Key Profiles - Key detection algorithm

License

This skill integrates Basic Pitch by Spotify, which is licensed under Apache 2.0. The pipeline script and documentation are provided under MIT license.

💡 Examples