Tencent SkillHub · AI

Voice Note To Midi

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

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

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: QUICKSTART.md, README.md, SKILL.md, setup.sh

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 0.1.0

Provenance

Publisher: DanBennettUK
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 23 sections Open source page

🎵 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: Stem Separation - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds ML-Powered Pitch Detection - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction Key Detection - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles Intelligent Quantization - Snaps notes to a configurable timing grid with optional key-aware pitch correction 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)

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 Add to your PATH (optional): echo 'export PATH="$HOME/melody-pipeline:$PATH"' >> ~/.bashrc source ~/.bashrc

Verify Installation

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

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

OptionDescriptionDefault--grid <value>Quantization grid: 1/4, 1/8, 1/16, 1/321/16--min-note <ms>Minimum note duration in milliseconds50--no-quantizeSkip quantization (output raw Basic Pitch MIDI)disabled--key-awareEnable key-aware pitch correctiondisabled--no-analysisSkip pitch analysis and key detectiondisabled

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

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: Import into your DAW and adjust tempo to match your original recording Quantize further if stricter timing is needed Adjust note velocities for dynamics Apply swing/groove templates if the rigid grid sounds too mechanical 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

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.

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs1 Scripts

SKILL.md Primary doc
QUICKSTART.md Docs
README.md Docs
setup.sh Scripts